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Based on the theory of social comparison and intergenerational learning, parents' social comparison, occurring frequently in families during the process of education, may play a critical role in determining their children's academic adjustment. Few researchers have explored this issue, let alone the multiple types of parent social comparison and their interactive effects. The aim of this research was to identify profiles of parent social comparisons and their links with child social comparisons, academic self-efficacy, and academic self-handicapping, thus addressing the gap and presenting a simplified comprehensive picture of the collaborative effects of parents' social comparison from a person-centered perspective. A total of 368 adolescents were surveyed. Latent profile analysis identified four distinct profiles in terms of perceived parent social comparisons: indifferent (low all), overwhelmed (high all), positive (high upward identification and downward contrast), and negative (high upward contrast and downward identification). A Bolck-Croon-Hagenaars analysis revealed that adolescents whose parents' social comparison patterns were perceived as positive reported higher self-efficacy and less self-handicapping, whereas those whose perceptions of parent social comparison patterns were classified as negative reported the reverse. Moreover, adolescents tended to exhibit social comparison types similar to those of their parents. Findings serve as a reminder for parents and educators of the downsides and benefits of various types of social comparison and provide insight into how social comparison transmits via social interaction from an intergenerational perspective., Author(s): Huilin Xing [sup.1] , Meilin Yao [sup.1] , Wenlong Zhu [sup.1] , Jing Li [sup.1] , Hongrui Liu [sup.1] Author Affiliations: (1) grid.20513.35, 0000 0004 1789 9964, Faculty of [...]

To address the low accuracy of conventional bearing fault diagnosis methods that rely on empirical parameters in the scenarios with different noise degrees, this paper presents an adaptive bearing fault diagnosis method combining empirical mode decomposition (EMD) and tile coding. Firstly, the mature adaptive decomposition algorithm, namely the EMD, was employed to decompose the bearing vibration signal. Based on specific criteria, some components were selected as effective signals and further denoised based on spectral averaging to realize adaptive filtering of the bearing vibration signal. Secondly, feature extraction of the filtered signal was conducted using spectral coding based on tile coding. The coding results were input into the K-nearest neighbors (KNN) classifier as features. Finally, for validation, a test dataset with varying noise degrees was constructed to conduct a comparison between the proposed adaptive method and the classic adaptive filtering-based fault diagnosis methods. The results show that when the signal-to-noise ratio is greater than －10 dB, the diagnostic accuracy of the proposed approach exceeds 90% and outperforms that of other methods.

Heavy-duty mine electric-wheel dump truck is one of the commonly used vehicles for large-scale open-pit mines. In harsh and complex application environment of the mining area, failure rates of key components of electric-wheel dump truck are high, and once a failure occurs, it is difficult to repair and replace, which will affect mine production. Regular inspection and fault repair used in routine maintenance will also bring greater costs to mine owners.This paper introduces an intelligent operation and maintenance scheme for electric drive system of mine electric-wheel dump truck based on state repair. Different from the conventional fault diagnosis and maintenance scheme, it does not need to collect and analyze signals through acceleration sensors on critical locations to obtain the results. Instead, it can be based on the real-time electrical signals collected by the traction converter of the drive system and using the vehicle-ground collaborative big data platform, the early identification and precise fault-positioning of key components of the drive system are realized. The method not only effectively improves the maintenance efficiency and reduces the cost of drive system of mine electric-wheel dump truck, but also can greatly reduce the cost of fault diagnosis.

ObjectiveTo assess the epidemic trend of COVID-19 Omicron and the effectiveness of containment measures in Shanghai by estimating the time-varying reproduction number （Rt）.MethodsBased on the daily reported confirmed cases and asymptomatic infections in Shanghai from February 20 to April 26， 2022， the R package "Epiestim"， which was built by Bayesian framework method， was used to estimate the variation curve of Rt during the epidemic period and to analyze the trend of the epidemic.ResultsIn the early stage of the epidemic， after the implementation of school closure and nuclear acid screening in some communities， Rt continued to fluctuate between 2.000 and 3.000， reaching a peak of 2.740 （95%CI： 2.640‒2.830） on March 21， but began to decline around one week after the city lock-down on April 1. As of April 18， the Rt value in Shanghai was below the threshold of 1.000 for the first time， reaching 0.955 （95%CI： 0.951‒0.961）.ConclusionAfter the implementation of public health measures with increasing strength of containment in Shanghai， the transmission rate gradually decreased， reflecting the effectiveness of the interventions. In the actual prevention and control process， the containment work should not be relaxed in order to keep the Rt below 1.000.

Contactors are widely used in power, distribution and power applications. Health of contactor is a key factor to the healthy operation of power system. In this paper, a dynamic model of armature movement in the process of contactor closing is established, and the relationship between the contactor coil voltage, coil current, action time and the structure parameters of the contactor is studied. Based on this relationship, a fault diagnosis method of contactor based on support vector machine is proposed. Through this method, diagnosis and classification of four types of contactor common faults are realized, and the diagnostic accuracy is over 93%. At the same time, combined with the charging process data of train traction converter, the action process of contactor is verified for ten times, and the results show that the diagnosis is accurate.

Face recognition technology (FRT) has a significant impact on citizens’ lives. Based on involvement theory, interaction ritual chain theory, social interaction theory, and the task-technology fit (TTF) model, this study evaluates the effect of FRT on citizens’ happiness. We conducted data collection in a Chinese region widely using FRT, with 653 effective samples, using structural equation models to evaluate the data. Finally, we draw five novel and valuable conclusions. First, task characteristics have a significant positive influence on TTF, but a similar effect is not evident between TTF and technical characteristics. Second, cognitive involvement positively affects positive emotion, which subsequently positively affects actual use. Third, TTF has a significant positive impact on actual use, which can also be transmitted through positive emotion. Fourth, actual use and positive emotion have a significant positive effect on feedback interaction. Fifth, feedback interaction has a significant positive effect on happiness, and this impact can also be transmitted through social connection. A potential contribution of this study is its theoretical framework and empirical model, which integrate the similarities and differences between FRT and other technologies used to meet citizens’ needs, revealing respondents’ reactions to and appreciation for FRT. [ABSTRACT FROM AUTHOR]

Bogie is a pivotal system and plays a critical part in the safety and reliability management of high-speed rail. However, the available bogie system reliability analysis methods lack the consideration of multi-state characteristics, and the common multi-state reliability analysis methods, being an NP-hard problem, lead to low efficiency. In order to overcome these drawbacks, this paper proposes a novel multi-state rail train bogie system reliability analysis approach based on the extended d -MC model. Three different function interactions within the bogie system are considered to build the multi-state bogie system flow network model. Meanwhile, an extended d -MC model is established to remove unnecessary d -MC candidates and duplicates, which greatly enhances the computing efficiency. The bogie system reliability is calculated, and examples are provided. Numerical experiments are carried out for the different operational conditions of the bogie system and are used to test the practicability of the method proposed in this article; it is found that this method outperforms a newly developed method in solving multi-state reliability problems. [ABSTRACT FROM AUTHOR]

The study of influence maximizing in temporal social networks (IMT) is an important aspect of influence maximization (IM) research. Currently, two main types of algorithms can solve the IMT problem: greedy-based algorithms and heuristic-based algorithms. However, the greedy-based algorithm is too time-consuming to be used in practice, and most existing heuristic methods do not consider the attributes of nodes, resulting in these methods being unable to solve the IMT problem. Therefore, this paper proposes a mixed k-shell (MKS) algorithm, which considers nodes' local and global attributes to characterize their influence and select seed nodes. At the local level, we consider the degree centrality of nodes, and at the global level, we propose the temporal k-shell decomposition (TKS) algorithm. Ultimately, the influence of a node is determined by combining the influence of itself and its neighbors. Experiments on four real temporal social networks show that MKS performs better in effectiveness than other heuristic baselines and can maintain a balance between effectiveness and efficiency, providing a useful solution for solving the IMT problem. [ABSTRACT FROM AUTHOR]

When magnetic saturation occurs during the operation of a permanent magnet (PM) slotless brushless DC motor, the material permeability will no longer be a constant value, and the neglected magnetic saturation model used for motor optimization will no longer be applicable. And considering that the increase in motor torque will lead to a high temperature rise of the winding, therefore, an electromagnetic heat coupling model applicable to the occurrence of magnetic saturation in the motor is established, and the model is utilized in combination with the particle swarm algorithm to enhance the maximum output torque of the motor. Firstly, a 100 W, 16,400 r/min high-speed PM slotless DC brushless motor is taken as the object of study, and its electromagnetic–thermal coupling model is established to derive the analytical equations for the electromagnetic torque with respect to the split ratio, the thickness of the stator yoke, the PM thickness, and the copper loss. Secondly, based on the modeling, the motor was optimized using a particle swarm algorithm to maximize the output torque and minimize the copper loss. Finally, a prototype was fabricated and verified with the prototype through no-load and load experiments. The difference between the theoretical maximum output torque and the experimental maximum output torque is less than 8%. The results show that this method can effectively predict the maximum output torque of the motor in the case of magnetic saturation, and the model is suitable for increasing the maximum output torque of slotless brushless DC motors under space constraints. [ABSTRACT FROM AUTHOR]