Regulating effect of virtual reality restorative environment on prefrontal cortex dysfunction after night shifts in medical staff: an fNIRS study protocol for a randomized controlled trial in Dalian, China.

BACKGROUND: Night shift work-related disturbed biological rhythm and insufficient sleep affect the functioning of brain activity and thus impair cognitive performance and mood state, which potentially leads to negative and even devastating results for both individuals and patients. A virtual reality (VR)-based restorative environment has shown to be an effective new technique to reduce stress and improve cognitive performance, but little is known about its mechanism of improving neuronal activity and connectivity. METHODS: This is a randomized, controlled, single-center clinical trial. A total of 140 medical staff will be enrolled and randomized in a 1:1 allocation to either the VR immersion group (intervention group) or the control group. In the morning after the night shift, the participants in the intervention group will watch 360° panoramic videos of immersive VR natural restorative environments for 10 min, while the participants in the control group will just rest for 10 min. Assessments of abbreviated Profile of Mood States Questionnaire (POMS) and verbal fluency task (VFT) performances, as well as oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) and total hemoglobin concentration acquired by functional near-infrared spectroscopy (fNIRS) will be performed at baseline (day work), the morning after night shift but before the intervention (previous) and after intervention (post). Data collected after a night shift will be compared to baseline performance as well as between the two groups. DISCUSSION: This trial will investigate the effects of the night shift and VR-based restorative environment intervention on mood, cognitive performance, and neuronal activity and connectivity. A positive result in this trial could encourage hospitals to apply VR technology to reduce physical and mental dysfunction during of night shifts among medical staff in every department. Furthermore, the findings from this study will contribute to understanding the underlying neuromodulation mechanisms of how restorative environments influence mood and cognition. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200064769 . Registered on 17 October 2022.

Carbon disclosure and stock price synchronization: from the perspective of analyst tracking.

Under the background of peaking carbon neutralization, it is a significant and fresh proposition to investigate the economic benefits of carbon disclosure (CD) in the Chinese market. By taking all listed enterprises as a sample (2009-2020), this paper firstly empirically analyzes the impact of enterprise CD on stock price synchronization and the indispensable role played by analysts in between. The results indicate that (1) enterprise CD is conducive to reduce stock price synchronization, confirming the accuracy of government mandatory CD system and the effectiveness of voluntary enterprise CD project. (2) Analysts play the role of "information scouts" and have a mediating effect between enterprise CD and stock price synchronization. (3) Analysts play the role of "analysis commentators," and analyst rating has a moderating effect between enterprise CD and stock price synchronization. (4) In further analysis, analysts will mobilize investors' positive investment sentiment, but only when the analyst rating upgrades or remains unchanged.

Combined laser-induced breakdown spectroscopy and hyperspectral imaging with machine learning for the classification and identification of rice geographical origin.

With the events of fake and inferior rice and food products occurring frequently, how to establish a rapid and high accuracy monitoring method for rice food identification becomes an urgent problem. In this work, we investigate using combined laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) with machine learning algorithms to identify the place of origin of rice production. Six geographical origin rice samples grown in different parts of China are selected and pretreated, and measured by the atomic emission spectra of LIBS and the reflection spectra of HSI, respectively. The principal component analysis (PCA) is utilized to realize data dimensionality and extract the data feat of LIBS, HSI and fusion data, and based on this, three models employing the partial least squares discriminant analysis (PLS-DA), the support vector machine (SVM) and the extreme learning machine (ELM) are used to identify the rice geographical origin. The results show that the accuracy of LIBS and HSI analysis with the SVM machine learning algorithm can reach 93.06% and 88.07%, respectively, and the accuracy of combined LIBS and HSI data fusion recognition can reach 99.85%. Besides, the classification accuracy of the three models measured after pretreatment is basically all above 95%, and up to 99.85%. This study proves the effectiveness of using the combined LIBS and HSI with the machine learning algorithm in rice geographical origin identification, which can achieve rapid and accurate rice quality and identity detection.

The motivations of Chinese firms in response to the Carbon Disclosure Project.

Through an analysis of data gathered from Chinese firms surveyed by the Carbon Disclosure Project (CDP), this paper studies the motivations of Chinese firms to respond to the CDP. The results indicate that (1) Chinese firms are more inclined to respond to the CDP survey for the sense-making motivation; (2) Chinese firms are less inclined to respond to the CDP survey due to the existence of proprietary costs for information disclosure; (3) self-interested political motivation is a negative motivation for Chinese firms to respond to the CDP survey; state-owned enterprises (SOEs) are less inclined to respond to the CDP survey than are non-SOEs; and (4) Chinese firms did not consider a financing motivation when deciding whether to respond to the CDP survey. However, the results of our further research show that if firms actively respond to the CDP survey, their financing constraints can be significantly reduced. This paper studies the four motivations for Chinese firms to respond to the CDP survey, contributing to the research of carbon emission disclosure. This paper highlights the importance of corporate carbon awareness for carbon emission disclosure, builds an understanding of the internal driving forces of response to the CDP survey among Chinese firms, and thus promotes the increase of Chinese corporate disclosure of carbon emission.

Surface tension driven aggregation of organic nanowires via lab in a droplet.

Directing the architecture of complex organic nanostructures is desirable and still remains a challenge in areas of materials science due to their structure-dependent collective optoelectronic properties. Herein, we demonstrate a simple and versatile solution strategy that allows surface tension to drive low-dimensional nanostructures to aggregate into complex structures via a lab in a droplet technique. By selecting a suitable combination of a solvent and an anti-solvent with controllable surface tension difference, the droplets can be automatically cracked into micro-droplets, which provides an aggregation force directed toward the centre of the droplet to drive the low-dimensional building blocks to form the special aggregations during the self-assembly process. This synthetic strategy has been shown to be universal for organic materials, which is beneficial for further optimizing the optoelectronic properties. These results contribute to gaining an insightful understanding on the detailed growth mechanism of complex organic nanostructures and greatly promoting the development of organic nanophotonics.