Hao Yu

Coal mine safety management involves many interested parties and there are complex relationships between them. According to game theory, a multiparty evolutionary game model is established to analyze the selection of strategies. Then, a simplified three-party model is taken as an example to carry out detailed analysis and solution. Based on stability theory of dynamics system and phase diagram analysis, this article studies replicator dynamics of the evolutionary model to make an optimization analysis of the behaviors of those interested parties and the adjustment mechanism of safety management policies and decisions. The results show how the charge of supervision of government department and inspection of coal mine enterprise impact the efficiency of safety management and the effect of constraint measures and incentive and other measures in safety management.

High-resolution meteorological satellite image is the basic data for weather forecasting, climate prediction, and early warning of various meteorological disasters. However, the poor image resolution is limited for both subjective and automated analyses. Through our investigation and study, we found that the meteorological satellite image is a kind of complex data with multimodal and multitemporal characteristics. Fortunately, based on zero-shot learning theory, the complexity of the meteorological satellite image can be used to enhance its own image resolution. In this work, we propose a novel framework called MSLp. Specifically, we choose a zero-shot network as a backbone and propose a phase loss function. A mapping from low- to high-resolution meteorological satellite images was trained for improving the resolution by up to a factor of 4×. Our quantitative study demonstrates the superiority of the proposed approach over ZSSR and bicubic interpolation. For qualitative analysis, visual tests were performed by 7 meteorologists to confirm the utility of the proposed algorithm. The mean opinion score is 9.32. These meteorologists think that weather forecasters need higher-resolution meteorological satellite images and the high-resolution images obtained by our method have the potential to be a great help for weather analysis and forecasting.

Static statistical regularities in the placement of targets and salient distractors within the search display can be learned and used to optimize attentional guidance. Whether statistical learning also extends to dynamic regularities governing the placement of targets and distractors on successive trials remains controversial. Here, we applied the same dynamic cross-trial regularity – one-step shift of the critical item in clock-/counterclockwise direction – to either the target or a distractor. In two experiments, we found and replicated robust learning of the predicted target location: processing of the target at this location was facilitated, compared to random target placement. But we found little evidence of proactive suppression of the predictable distractor location – even in a close replication of Wang et al. (2021), who had reported a dynamic distractor suppression effect. Facilitation of the predictable target location was associated with explicit awareness of the dynamic regularity, whereas participants showed no awareness of the distractor regularity. We propose that this asymmetry arises because, owing to the target’s central role in the task set, its location is explicitly encoded in working memory, enabling the learning of dynamic regularities. In contrast, the distractor is not explicitly encoded; so, statistical learning of dynamic distractor locations is more precarious. People can learn and use both static and dynamic (cross-trial) regularities in the positioning of target items during parallel, ‘pop-out’ visual search. Static target-location learning also works in serial search, however, acquiring dynamic regularities is hindered by the demands of item-by-item scanning. Also, questions have been raised regarding whether explicit awareness is necessary for using dynamic regularities to optimize performance. The present study re-examined if dynamic regularities can be learned in serial search when regular shifts of the target location occur frequently, and if such learning correlates with awareness of the dynamic rule. We adopted the same regularity used by Yu et al. (2023) to demonstrate dynamic learning in parallel search: a cross-trial shift of the target location in a (counter-)clockwise direction within a circular array in 80% of the trials, compared to irregular shifts in the opposite direction (10%) or some other random direction (10%). The results showed that about 70% of participants learned the dynamic regularity, with performance gains correlating with awareness: the more accurately they estimated the likelihood of the target shifting in the frequent direction, the greater their gains. Importantly, part of the gains accrued already early during the search: a large proportion of the very first and short-latency eye movements were directed to the predicted location, regardless of the target appeared there. We discuss whether this rule-driven behavior is causally mediated by conscious control. Target facilitation can be achieved via spatial statistical learning of the target. Although many behavioral studies have found participants' performance was improved when targets occur at frequent locations relative to infrequent, the electrophysiological correlates of statistically learned target enhancement, however, have not been well studied. Moreover, a recent behavioral study (Turatto and Valsecchi 2022) has proved that the learned distractor suppression could cause long-lasting changes in the priority map, which affects the future computation of target salience at the same location to facilitate attention selection. It remains to be seen whether the learned target enhancement can also cause long-term changes in the priority map. Thus in the current study, the EEG data were collected while participants searched for a target in a serial search task with an unbalanced distribution of targets. N2pc (lateralized event-related potentials) and lateralized alpha (8–12 Hz) power were used to track the temporal dynamics of effects. The experiment consisted of two phases: the probability manipulation of target location was implemented in the training phase; the probability manipulation of target location was removed in the testing phase, and the distractor was also introduced which shared a similar feature as the target. Behaviorally, we found robust learning of predicted target locations in the training phase: processing of the target at frequent locations was facilitated, compared to infrequent target placement. In the testing phase, we found that the long-term learning even persisted when the target in the training phase became the distractor. Electrophysiologically, the learned target enhancement is not associated with lateralized alpha power during the pre-stimulus period. Yet the targets at frequent locations induced larger N2pc amplitude and earlier N2pc latency in the training phase. Thus, we confirm that facilitation resulting from target spatial statistical learning can cause plastic changes on the priority map.

Abnormal behaviour detection algorithm needs to conduct behaviour analysis on the basis of continuous video inclination tracking, and the robustness of the algorithm is reduced for the occlusion of moving targets, the occlusion of the environment, and the movement of targets with the same colour. For this reason, the optical flow information between RGB images and video frames is used as the input of the network in view of group behaviour. Then, the direction, velocity, acceleration, and energy of the crowd were weighted and fused into a global optical flow descriptor. At the same time, the crowd trajectory map is extracted from the original image of a single frame. Following, in order to realize the detection of large displacement moving target and solve the problem that the traditional optical flow algorithm is only suitable for the detection of displacement moving target, a video abnormal behaviour detection algorithm based on the double-flow convolutional neural network is proposed. The network uses two network branches to learn spatial dimension information and temporal dimension information, respectively, and uses short- and long-time neural network to model the dependency relationship between long-time video frames, so as to obtain the final behaviour classification results. Simulation test results show that the proposed method can achieve good recognition effect on multiple datasets, and the performance of abnormal behaviour detection can be significantly improved by using interframe motion information.

Urban inclusive green growth (UIGG) refers to the synergetic enhancement of the economy, the environment, and the society in a city. Achieving such enhancement requires addressing a series of problems in the development of urbanization, such as unemployment, lack of access to education, insufficient medical resources, inequity, and environmental pollution. As firms are critical to city development and urbanization, whether they practice corporate social responsibility (CSR) plays a crucial part in UIGG. In this study, we focus on Chinese cities as examples of UIGG and Chinese listed companies as CSR engagers, employing matched data on Chinese listed companies and cities to investigate how CSR affects UIGG. Specifically, the empirical results of a high-dimensional fixed effect model indicate that CSR significantly promotes UIGG. This finding remains valid after a set of robustness checks, including instrumental variable (IV) regression. CSR can promote UIGG by positively influencing enterprises’ economic performance, innovation, and employment. To promote firms’ substantive CSR actions, the government of Jiangsu Province, China, launched a pilot policy for CSR promotion by issuing an official guidance document, which can be considered a quasi-natural experiment to test the causality between CSR and UIGG. We find that the CSR pilot significantly promoted UIGG. The heterogeneity test results indicate that the influence of CSR on UIGG varies based on the audit company, the nature of the enterprise, and the structure of corporate leadership.

Functional diversity is related to functional areas in which CFOs are experienced. It reflects their number of general managerial skills or social ties to some extent. In this paper, we try to examine whether there is an association between CFO functional diversity and the timeliness of annual reports. Using data on Chinese listed firms from 2009 to 2017, we found that in state-owned enterprises, there is a negative relationship between this diversity and timeliness. However, the promotion incentive of CFOs with functionally diverse experience can weaken this negative relationship. We also found that there is a positive relationship between the two factors in private enterprises whose offices are headquartered in regions with a low degree of marketization. We use difference-in-differences method to test the hypotheses again. The conclusions remain robust.