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Guided by the cognitive-affective system theory of personality (CAPS), this study aims to investigate the parallel mediating effects of cognitive and affective cynicism on the relationship between illegitimate tasks and employees’ adaptive performance. It also proposes growth need strength as a moderating variable for relationships between illegitimate tasks and employees’ adaptive performance.

Using a time-lagged design, data were gathered from 330 frontline hotel employees in China.

The authors found that the presence of illegitimate tasks is negatively associated with employees’ adaptive performance, this relationship being mediated by cognitive and affective cynicism. Growth need strength weakens the negative impacts of cognitive and affective cynicism on employees’ adaptive performance. In addition, the indirect effect of illegitimate tasks on employees’ adaptive performance via cognitive and affective cynicism is stronger for employees with lower levels of growth need strength.

Hotel managers must heed the negative impact of illegitimate tasks. Furthermore, they should underscore the importance of promoting a harmonious and positive organizational culture and atmosphere. Naturally, hotel managers must also establish effective communication with employees, assisting them in fostering a desire for excellence in their work.

This study provides valuable insights for the hospitality industry by investigating how illegitimate tasks hold sway over hotel employees’ adaptive performance. The study uses a moderated dual-path model to uncover the mechanisms behind this impact and the influence of boundary conditions, thereby expanding the understanding of the topic.

This paper aims at building a discharge model for the power cable bellows based on plasma energy deposition and analyzing the discharge ablation problem.

Aiming at the multiphysical mechanism of the discharge ablation process, a multiphysical field model based on plasma energy deposition is established to analyze the discharge characteristics of the power cable bellows. The electrostatic field, plasma characteristics, energy deposition and temperature field are analyzed. The discharge experiment is also carried out for result validation.

The physical mechanism of the bellows ablative effect caused by partial discharge is studied. The results show that the electric field intensity between the aluminum sheath and the buffer layer easily exceeds the pressure resistance value of air breakdown. On the plasma surface of the buffer layer, the electron density is about 4 × 1,019/m³, and the average temperature of electrons is about 3.5 eV. The energy deposition analysis using the Monte Carlo method shows that the electron range in the plasma is very short. The release will complete within 10 nm, and it only takes 0.1 s to increase the maximum temperature of the buffer layer to more than 1,000 K, thus causing various thermal effects.

Its physical process involves the distortion of electric field, formation of plasma, energy deposition of electrons, and abrupt change of temperature field.

Wheeled mobile robots (WMR) are the most widely used robots. Avoiding obstacles in unstructured environments, especially dynamic obstacles such as pedestrians, is a serious challenge for WMR. This paper aims to present a hybrid obstacle avoidance method that combines an informed-rapidly exploring random tree* algorithm with a three-dimensional (3D)-object detection approach and model prediction controller (MPC) to conduct obstacle perception, collision-free path planning and obstacle avoidance for WMR in unstructured environments.

Given a reference orientation and speed, the hybrid method uses parametric ellipses to represent obstacle expansion boundaries based on the 3D target detection results, and a collision-free reference path is planned. Then, the authors build on a model predictive control for tracking the collision-free reference path by incorporating the distance between the robot and obstacles. The proposed framework is a mapless method for WMR.

The authors present experimental results with a mobile robot for obstacle avoidance in indoor environments crowded with obstacles, such as chairs and pedestrians. The results show that the proposed hybrid obstacle avoidance method can satisfy the application requirements of mobile robots in unstructured environments.

In this study, the parameter ellipse is used to represent the area occupied by the obstacle, which takes the velocity as the parameter. Therefore, the motion direction and position of dynamic obstacles can be considered in the planning stage, which enhances the success rate of obstacle avoidance. In addition, the distance between the obstacle and robot is increased in the MPC optimization function to ensure a safe distance between the robot and the obstacle.

Uncertainty is ubiquitous in practical engineering and scientific research. The uncertainties in parameters can be treated as interval numbers. The prediction of upper and lower bounds of the response of a system including uncertain parameters is of immense significance in uncertainty analysis. This paper aims to evaluate the upper and lower bounds of electric potentials in an electrostatic system efficiently with interval parameters.

The Taylor series expansion is proposed for evaluating the upper and lower bounds of electric potentials in an electrostatic system with interval parameters. The uncertain parameters of the electrostatic system are represented by interval notations. By performing Taylor series expansion on the electric potentials obtained using the equilibrium governing equation and by using the properties of interval mathematics, the upper and lower bounds of the electric potentials of an electrostatic system can be calculated.

To evaluate the accuracy and efficiency of the proposed method, the upper and lower bounds of the electric potentials and the computation time of the proposed method are compared with those obtained using the Monte Carlo simulation, which is referred to as a reference solution. Numerical examples illustrate that the bounds of electric potentials of this method are consistent with those obtained using the Monte Carlo simulation. Moreover, the proposed method is significantly more time-saving.

This paper provides a rapid computational method to estimate the upper and lower bounds of electric potentials in electrostatics analysis with interval parameters. The precision of the proposed method is acceptable for engineering applications, and the computation time of the proposed method is significantly less than that of the Monte Carlo simulation, which is the most widely used method related to uncertainties. The Monte Carlo simulation requires a large number of samplings, and this leads to significant runtime consumption.

This paper aims to investigate an identification strategy for the nonlinear state-space model (SSM) in the presence of an unknown output time-delay. The equations to estimate the unknown model parameters and output time-delay are derived simultaneously in the proposed strategy.

The unknown integer-valued time-delay is processed as a latent variable which is uniformly distributed in a priori known range. The estimations of the unknown time-delay and model parameters are both realized using the Expectation-Maximization (EM) algorithm, which has a good performance in dealing with latent variable issues. Moreover, the particle filter (PF) with an unknown time-delay is introduced to calculated the Q-function of the EM algorithm.

Although amounts of effective approaches for nonlinear SSM identification have been developed in the literature, the problem of time-delay is not considered in most of them. The time-delay is commonly existed in industrial scenario and it could cause extra difficulties for industrial process modeling. The problem of unknown output time-delay is considered in this paper, and the validity of the proposed approach is demonstrated through the numerical example and a two-link manipulator system.

The novel approach to identify the nonlinear SSM in the presence of an unknown output time-delay with EM algorithm is put forward in this work.

The tech giants Alibaba and Tencent compete on many fronts. This case focuses on three areas where they have competed very hard: new retailing, mobile payment, and ride-hailing. At the beginning of 2018, Alibaba and Tencent were gathering retail investments in bids to battle each other for shoppers' digital wallets. Key to the battle is China's mobile payment market, worth more than 200 trillion RMB, where Alibaba and Tencent are going head to head. The giants are not only directly competing in the payment platform area but also extensively fighting in other areas, such as ride-hailing, where they invested in and supported Didi and Kuaidi, respectively. To enhance understanding, this case also briefly goes through the history of the two giants. The purposes, methods, and consequences of their platform competition deserve an in-depth discussion

Realizing project portfolio benefits (PPBs) is considered a key challenge faced by enterprises. This challenge can largely be attributed to an unclear understanding of the factors influencing PPBs. However, synergistic relationships create complexity for the management of influencing factors. In response to this dilemma, the objective of this study is to quantitatively investigate the factors influencing PPBs while considering the synergistic effect among factors to provide guidelines for benefits management.

Through an integration of the synergy degree of the composite system model and social network analysis (SNA), a refined model is proposed to explore the factors influencing PPBs. First, a list that includes financial and nonfinancial influencing factors is clarified. Then, the corresponding network links, which represent the synergistic relationships among the factors, are innovatively assessed based on the synergy degree of the composite system. Finally, the influencing factor network is analyzed using both individual and overall indicators of SNA.

The resulting evidence demonstrates that four critical influencing factors exist, namely, “project managers,” “purchasers,” “development capacity” and “tangible resources.” These factors are relatively important and should be prioritized. Furthermore, the factors are divided into three subgroups: participant, resource and governmental factors. A general observation from the results is that factors that share the same subgroup are more likely to have a synergistic effect advantage, which leads to an increase in PPBs.

The value of this paper lies in its proposition of a quantitative model that can be used to measure and analyze the factors influencing PPBs with synergy considerations. This research contributes to the body of knowledge on benefits management by linking synergy with PPBs. It presents new insights for managers on how PPBs may be effectively managed and promoted from the perspective of influencing factors.