Analysis of the trajectory and influencing factors of kinesiophobia in elderly patients during the rehabilitation phase of limb fractures.

Objectives: To explore the trajectory and influencing factors of kinetophobia in elderly patients with limb fracture during the rehabilitation period.

Methods: In this retrospective study, we retrieved the follow-up records of 150 elderly patients with limb fractures from our hospital's electronic medical record system. We collected the demographic data and Tampa Scale for Kinesiophobia (TSK) scores of patients at postoperative day 1 (T0), 1 week (T1), 3 weeks (T2), 6 weeks (T3), and 12 weeks (T4) to track changes in kinesiophobia over time.

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.

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.

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 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.

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.