BACKGROUND: The rapid development of science and technology and the swift improvement of people’s material living standards enabled smartphones to be indispensable of people’s daily lives. OBJECTIVE: The purpose of this paper was to examine the influence of self-control in adolescents’ participation in physical activity on cell phone dependence. METHODS: The Physical Activity Rating Scale (PARS-3), the Self-Control Scale, and the Cell Phone Dependence Scale were used to measure the influence of self-control in adolescents’ participation in physical activity on cell phone dependence among 649 adolescents. RESULTS: The results show: (1) There were significant differences (p <  0.01) in the physical activity levels of adolescents under different gender, birthplace, and education background. (2) Each dimensional variable of physical activity was negatively correlated with the cell phone dependence variable, and positively correlated with each variable of self-control, and the self-control variables were negatively correlated with cell phone dependence. (3) Self-control was partially mediating the effects of physical activity on cell phone dependence, with the mediating effect accounting for 39.68% . CONCLUSIONS: Adolescents’ participation in physical exercise activities will improve self-control and ultimately reduce cell phone dependence. Curbing the negative and malignant events of cell phone addiction among adolescents, timely investigation of mobile phone and Internet addiction, pathological formation mechanisms and intervention measures are important measures to reshape the healthy lifestyle of adolescents and have great practical significance for the prosperity and development of families, society, nation and country.

Supplier network collaborative efficiency evaluation is important content in the transformation and upgrading of intelligent manufacturing enterprises. Aiming at the shortcomings of existing methods, this paper proposes a new method to evaluate the collaborative efficiency of internal members of a complex supplier network based on complex network theory. Based on the analysis of the characteristics of the complex supplier network, from the perspective of the system, the macro supplier network is divided into multiple multi-level supplier micro subsystems with manufacturing enterprises as the core. In order to reasonably quantify the collaboration relationship of members in the subsystem structure model, the collaboration entropy is introduced as a measurement tool, and combined with the hesitation fuzzy scoring function, and the collaborative evaluation model of the complex supplier network is constructed. By quantifying the collaboration relationship among the members in the subsystem and summarizing it step by step and iteratively, the collaborative efficiency evaluation of the complex supplier network from local to overall is realized. Finally, taking a large battery manufacturing enterprise in China as an example, the proposed method is used to calculate the collaboration entropy, collaborative efficiency, and collaboration ratio of members at different supplier network levels. The results verify the effectiveness of the model.

PurposeThe purpose of this study is to examine the role of positive workplace gossip (PWG) in employee innovative behavior, whereby a mediating effect of employee loyalty is proposed in this relationship. The moderating effect of organizational trust (OT) is also examined on the indirect of PWG on employee innovative behavior through employee loyalty.Design/methodology/approachThis research used a survey data of 327 employees from the enterprises selected from the Pearl River and Yangtze River Delta region of China. Based on the literature review, five main hypotheses were formulated and explored. The SPSS-Process Macro Plugin was used to analyze the hypothesized model.FindingsResults show there is a positive and significant relationship between PWG and employee innovative behavior. This study also confirm that employee loyalty is an intervening variable and OT as a moderator.Practical implicationsOrganizations should pay more attention to workplace gossip phenomena, encourage employees to take appropriate part in positive workplace gossip and to communicate positive information about other colleagues, and build an inclusive, open, sincere, and interdependent platform in the organization.Originality/valueEmployee innovative behavior plays an essential role in organization’s survival and development. Few studies have investigated PWG may promote employee innovative behavior through employee loyalty. The data, model, and findings of this research address the gap and complement the current state of knowledge.

Bitemporal supervised learning paradigm always dominates remote sensing change detection using numerous labeled bitemporal image pairs, especially for high spatial resolution (HSR) remote sensing imagery. However, it is very expensive and labor-intensive to label change regions in large-scale bitemporal HSR remote sensing image pairs. In this paper, we propose single-temporal supervised learning (STAR) for universal remote sensing change detection from a new perspective of exploiting changes between unpaired images as supervisory signals. STAR enables us to train a highaccuracy change detector only using unpaired labeled images and can generalize to real-world bitemporal image pairs. To demonstrate the flexibility and scalability of STAR, we design a simple yet unified change detector, termed ChangeStar2, capable of addressing binary change detection, object change detection, and semantic change detection in one architecture. ChangeStar2 achieves state-of-the-art performances on eight public remote sensing change detection datasets, covering above two supervised settings, multiple change types, multiple scenarios. The code is available at https: //github.com/Z-Zheng/pytorch-change-models.

In view of the current situation that the maturity of enterprise intelligent manufacturing capability is generally low and the information asymmetry in the upstream and downstream of the supply chain is high, taking any supply and demand link in the supply chain as an example, a group of initial demand signals that change nonlinearly over time are divided into intrinsic mode functions and noise residuals with different data characteristics by means of the variational modal decomposition (VMD) algorithm. On the basis of signal denoising and reconstruction, the support vector machine (SVM) algorithm is used to make regression prediction of the reconstructed signal with each intrinsic mode function as sample attribute. Compared with the regression prediction results of the original demand signal, it is verified that the VMD-SVM bullwhip effect weakening model can effectively filter the demand noise generated by each link in the supply chain and improve the accuracy of demand information transmission. It has a certain reference value to the weakening of the bullwhip effect and the improvement of supply chain synergy efficiency.