Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm and gradient (...) descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

Traditional mental health models focus on psychopathological symptoms. In contrast, a dual-factor model of mental health integrates psychopathology and subjective well-being into a mental health continuum, and it is adjustment and supplement for traditional mental health research paradigm. The present study explores the applicability of a dual-factor model of mental health in mental health screening of Chinese college students. To assess mental health statuses of 2,065 college students, we used Flourishing Scale Chinese Version, Satisfaction With Life Scale, the seven-item Patient (...) Health Questionnaire, the Mental Health Continuum–Short Form, and Purpose in Life Test–Short Form. Results showed that the dual-factor model of mental health has a good fit index. Also, a feasible screening scale was addressed. The results indicate the importance of addressing both subjective well-being and psychopathology in evaluating mental health screening of college students. (shrink)

In coal mining industry, the running state of mine ventilators plays an extremely significant role for the safe and reliable operation of various industrial productions. To guarantee the better reliability, safety, and economy of mine ventilators, in view of early detection and effective fault diagnosis of mechanical faults which could prevent unscheduled downtime and minimize maintenance fees, it is imperative to construct some viable mathematical models for mine ventilator fault diagnosis. In this article, we plan to establish a data-based mine (...) ventilator fault diagnosis method to handle situations where engineers are absent or they are incapable of coming to a conclusion from multisource data. In the process of building the mine ventilator fault diagnosis model, considering that probabilistic rough sets could reduce the errors triggered by incompleteness, inconsistency, and inaccuracy without needing any additional assumptions and Pythagorean fuzzy multigranulation rough sets over the two universes’ model could effectively handle data representation, fusion, and analysis issues, we generalize the existing PF MGRSs over the two universes’ model to the PRS setting, as well as to further establish a novel model named Pythagorean fuzzy multigranulation probabilistic rough sets over two universes. In the granular computing paradigm, three types of PF MG-PRSs over two universes based on the risk attitude of engineers are proposed at first. Afterwards, several basic propositions of the newly proposed model are explored. Moreover, a PF multigranulation probabilistic model for mine ventilator fault diagnosis based on PF MG-PRSs over two universes is investigated. At last, a real-world case study of dealing with a mine ventilator fault diagnosis problem is given to illustrate the practicality of the presented model, and a validity test, a sensitivity analysis, and a comparison analysis are further explored to demonstrate the effectiveness of the presented model. (shrink)

Meta-analytical research has demonstrated the benefits brought by telecommuting to wellbeing. However, we argue that such a setup in the course of the coronavirus disease pandemic exerts negative effects. On the basis of conservation of resources theory, this study determined how telecommuting depletes wellbeing through obstructing psychological detachment from work. Moreover, we incorporated family interfering with work and family–work enrichment as moderators that can buffer the negative effect of telecommuting on psychological detachment from work. Time-lagged field research was conducted with (...) 350 Chinese employees, and findings largely supported our theoretical hypotheses. The elevated level of telecommuting results in minimal psychological detachment from work, which then leads to low wellbeing. Meanwhile, the negative effect of the extent of telecommuting on psychological detachment from work is reduced by family interfering with work. These findings extend the literature on telecommuting and psychological detachment from work through revealing why teleworkers present negative feelings during the pandemic. (shrink)

Confronting the uncertain environment, this article adopts a case research approach to resonate with the studies of hybridity. It aims to explain how the perception of uncertainty in the institutional environment affects the adaptation of organizational structure in pursuing legitimacy for hybrid organizations. Based on the empirical data collected from a two-staged fieldwork and in-depth interviews, the case analysis concentrates on the correlation between the evolution of institutional logics and organizational structure change from a diachronic perspective. The findings indicate that (...) in the face of competing and changing institutional logics, Chinese mass media organizations have gradually shifted from a dominated blending strategy in the exploration stage to a deeply compartmentalizing strategy in the stable stage. The hybrids can deal with the uncertainty of the institutional environment by enhancing the uncertainty of the organizational structure. Consequently, the case evolves an organizational integration through internal legitimacy. It manifests a possibility for hybrids of combining the two major response mechanisms in one process. (shrink)

The present study investigated the configuration effect of human capital, social capital, and psychological capital on job performance. The human capital questionnaire, social capital scale, psychological capital scale, and job performance scale were used to survey 458 employees. Results revealed that four antecedent configurations could achieve high task performance, and three antecedent configurations can achieve high contextual performance. The high job performance driving path was characterized by “all roads lead to Rome.” Human capital, social capital, and psychological capital affected job (...) performance in the form of configuration, which reflected the asymmetric causal relationship. (shrink)

Reductionism and complexity theory are two paradigms frequently found in language research. There exist a number of conflicts in terms of concepts and methodologies between reductionism and complexity theory, which are not conducive to creating a unified language research framework. This paper starts by discussing the adaptability of complex dynamic systems and combines cognitive processing model and artificial neural networks to construct and verify an adaptive weight model, showing that the study of reductionism is induction of high-weight elements and the (...) study of complexity theory is a discussion of system complexity from adaptability, meaning that there is a good fit between the two frameworks. The adaptive weight model is conducive to developing a unified interpretation of language research results. (shrink)

The purpose of this study was to apply deep learning to music perception education. Music perception therapy for autistic children using gesture interactive robots based on the concept of educational psychology and deep learning technology is proposed. First, the experimental problems are defined and explained based on the relevant theories of pedagogy. Next, gesture interactive robots and music perception education classrooms are studied based on recurrent neural networks. Then, autistic children are treated by music perception, and an electroencephalogram is used (...) to collect the music perception effect and disease diagnosis results of children. Due to significant advantages of signal feature extraction and classification, RNN is used to analyze the EEG of autistic children receiving different music perception treatments to improve classification accuracy. The experimental results are as follows. The analysis of EEG signals proves that different people have different perceptions of music, but this difference fluctuates in a certain range. The classification accuracy of the designed model is about 72–94%, and the average classification accuracy is about 85%. The average accuracy of the model for EEG classification of autistic children is 85%, and that of healthy children is 84%. The test results with similar models also prove the excellent performance of the design model. This exploration provides a reference for applying the artificial intelligence technology in music perception education to diagnose and treat autistic children. (shrink)

This study analyzes the compositions of Hong Kong English as a second language learners and English as a foreign language learners in Mainland China in terms of lexical and syntactic features. A program based on the CoreNLP was developed and used to analyze written language texts, and differences in tags of parts of speech and syntactic dependencies between the two groups of texts were compared statistically to examine differences in the lexical and syntactic features of the learners’ written language. The (...) results show significant differences in the lexical and syntactic features of learners’ writing. Specifically, in EFL learners’ writing, there is a salient group pattern of higher lexical diversity, whereas ESL compositions are more flexible in vocabulary use with higher information density, in that they use more syntactic phrases and content words. In terms of syntax, Hong Kong ESL students use more adverbials and adverbial clauses, which is advantageous in syntactic simplicity and readability over their counterparts, whereas Mainland China EFL students prefer using more specific expressions to demonstrate syntactic relations. Compared to EFL compositions, ESL compositions are more informative, coherent, and grammatical in lexical features and more readable in syntactic features, which require more attention and further improvements in terms of EFL teaching. (shrink)