Studies have shown that psychological empowerment (PE) has a negative relationship with turnover intention (TI). However, there is little research regarding the potential mechanisms that may mediate ...or moderates this relationship. The present study is to examined the effect of PE on TI, and whether this effect is mediated by emotional exhaustion (EE) and moderated by emotional regulation (ER). A total of 503 university counselors in China completed the measures of PE, TI, EE and ER. As predicted, the relationship between PE and TI was partially mediated by EE. Furthermore, the effect of PE on TI via EE was moderated by ER. Specifically, the effect of EE on TI was stronger in individuals with lower cognitive reappraisal (CR), but weaker in ones with lower expression suppression (ES). These findings highlight the significance of clarifying the mechanisms that moderates the mediated paths between PE and TI.
Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted ...metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization (PSO) algorithm and a surrogate-assisted social learning-based PSO (SL-PSO) algorithm cooperatively search for the global optimum. The cooperation between the PSO and the SL-PSO consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the SL-PSO focuses on exploration while the PSO concentrates on local search. Empirical studies on six 50-D and six 100-D benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.
Turnover intention occurs frequently in nurses and psychological empowerment has been shown to be major factors that influence turnover intention. However, little is known about the driving force ...behind turnover intention among nurses in China during the COVID-19 pandemic.
To investigate the mediating role of job satisfaction and emotional exhaustion on the association between psychological empowerment and turnover intention among Chinese nurses during the COVID-19 pandemic.
A cross-sectional design was conducted in China. A total of 507 nurses completed scales of psychological empowerment, job satisfaction, emotional exhaustion and turnover intention anonymously. Descriptive analysis, Pearson's correlation analysis in SPSS 23.0 and structural equation modeling (SEM) by Mplus 7.4 RESULTS: Psychological empowerment had a significantly effect on turnover intention through three significantly indirect pathways: (1) through job satisfaction (B = -0.14, SE = .03, 95% CI = -.19, -.09). (2) through emotional exhaustion (B = -0.07, SE = .02, 95% CI = -.11, -.03). (3) through the chain mediating effect of "job satisfaction → emotional exhaustion" (B = -0.12, SE = .02, 95% CI = -.16, -.09).
Intervention measures to reduce the incidence of turnover intention of nurses should include the evaluations of work demands and emotional exhaustion of nurses and organization's management strategies to promote their psychological empowerment and job satisfaction.
This paper provides a method for automatically selecting optimal operational indices for unit processes in an industrial plant using measured data and without knowing dynamical models of the unit ...process. A dynamic multiobjective optimization problem is defined to find operational indices that lead to plant-wide production indices close to their target values. A case-based reasoning (CBR) technique is also employed, which uses the stored experience of a human expert to determine appropriate operational indices for given target production indices. The solutions of the optimization problem and CBR technique are combined to form baseline operational indices. The dynamic models of the production indices, however, are time varying and affected by disturbances and online corrections of these baseline operational indices are required. To this end, reinforcement learning (RL) is used to provide a data-driven optimization technique to compensate for disturbances and model approximation errors and variations. The data-driven RL approach is used in two different time scales. The samples of the predicted production indices are used at a fast sampling rate, i.e., at each sample time, and the samples of actual production indices are used at a slower sampling rate, i.e., after each operational run, to correct the baseline operational indices. The effectiveness of this automated decision procedure has been demonstrated by successful implementation of the proposed approach on a large mineral processing plant in Gansu Province, China.
Gaussian processes (GPs) are widely used in surrogate-assisted evolutionary optimization of expensive problems mainly due to the ability to provide a confidence level of their outputs, making it ...possible to adopt principled surrogate management methods, such as the acquisition function used in the Bayesian optimization. Unfortunately, GPs become less practical for high-dimensional multiobjective and many-objective optimization as their computational complexity is cubic in the number of training samples. In this article, we propose a computationally efficient dropout neural network (EDN) to replace the Gaussian process and a new model management strategy to achieve a good balance between convergence and diversity for assisting evolutionary algorithms to solve high-dimensional multiobjective and many-objective expensive optimization problems. While the conventional dropout neural network needs to save a large number of network models during the training for calculating the confidence level, only one single network model is needed in the EDN to estimate the fitness and its confidence level by randomly ignoring neurons in both training and testing the neural network. Extensive experimental studies on benchmark problems with up to 100 decision variables and 20 objectives demonstrate that, compared to state of the art, the proposed algorithm is not only highly competitive in performance but also computationally more scalable to high-dimensional many-objective optimization problems. Finally, the proposed algorithm is validated on an operational optimization problem of crude oil distillation units, further confirming its capability of handling expensive problems given a limited computational budget.
The aim of evolutionary multi/many-objective optimization is to obtain a set of Pareto-optimal solutions with good trade-off among the multiple conflicting objectives. However, the convergence and ...diversity of multiobjective evolutionary algorithms often seriously decrease with the number of objectives and decision variables increasing. In this paper, we present a decomposition-based evolutionary algorithm for solving scalable multi/many-objective problems. The key features of the algorithm include the following three aspects: (1) a resource allocation strategy to coordinate the utility value of subproblems for good coverage; (2) a multioperator and multiparameter strategy to improve adaptability and diversity of the population; and (3) a bidirectional local search strategy to prevent the decrease in exploration capability during the early stage and increase the exploitation capability during the later stage of the search process. The performance of the proposed algorithm is benchmarked extensively on a set of scalable multi/many-objective optimization problems. The statistical comparisons with seven state-of-the-art algorithms verify the efficacy and potential of the proposed algorithm for scalable multi/many-objective problems.
The recent financial crisis and other major crises have suggested that there are some strong interactions and interdependence between several supply chains and their external environments in various ...ways. A set of supply chains that are interdependent is called a holistic supply chain network (H-SCN) in this paper. There is a need to focus on building the resilience (in short, the ability of a system to recover from damage or disruption) of an entire H-SCN as it is believed that such a network system is strongly relevant to the recent economic recession that is triggered by financial crises. The objectives of this paper are to provide a classification of different SCNs in literature, leading to the identification of a new type of SCN system, i.e., an H-SCN, and to discuss the state of knowledge on the resilience of SCNs, particularly of an H-SCN. A systematic review approach is applied in this paper. Another contribution of this paper is the provision of a more comprehensive definition and description of resilient systems, including SCN systems. A final contribution of this paper is the proposal of the future directions of research on resilient SCN systems, particularly resilient H-SCN systems.
A hybrid intelligent control method is proposed to control the technical indices into their desired range. This is realized by on-line adjusting the set-points of control loops for optimal operation ...of the shaft furnace in response to changes in operating points. The controller consists of six modules, namely a pre-setting model for control loop set-points, a predictive model for technical index, a feedforward compensator using predictive model, a feedback compensator, a fault working situation diagnosis and a fault tolerant control model. The proposed approach has been successfully applied to the roasting process of shaft furnace in a mineral processing plant in China and its efficiency has been verified by the practical application results.
This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the ...whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid modeling approach of the mixed concentrate grade prediction is proposed, which consists of a linear model and a nonlinear model. The least-squares support vector machine is adopted to establish the nonlinear model. The inputs of the predictive model are the performance indices of each unit process, while the output is the mixed concentrate grade. In this paper, the model parameter selection is transformed into the shape control of the probability density function (PDF) of the modeling error. In this context, both the PDF-control-based and minimum-entropy-based model parameter selection approaches are proposed. Indeed, this is the first time that the PDF shape control idea is used to deal with system modeling, where the key idea is to turn model parameters so that either the modeling error PDF is controlled to follow a target PDF or the modeling error entropy is minimized. The experimental results using the real plant data and the comparison of the two approaches are discussed. The results show the effectiveness of the proposed approaches.
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