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This paper examines the impact of media communication on the stock market in the context of the digital age, particularly its role in stock price volatility. With the significant increase in the speed, breadth and depth of media communication, the efficiency of stock market information transmission has been dramatically improved, significantly impacting stock price volatility. The article first explores the specific impact of media coverage characteristics and methods on stock price volatility by quantitatively analyzing media coverage’s breadth, manner and content. Then, the article utilizes multiple linear regression models to deeply explore the impact of various news texts on stock price volatility in different reporting modes (e.g., live reporting, television, and text reporting) in the Shanghai and Shenzhen stock markets. The findings show a significant association between the breadth of media coverage and stock prices at the 1% significance level. In addition, the regression coefficients of content and manner of coverage on stock price changes are 0.096 and 0.229, respectively, which are both significant at the 5% significance level. The research results of this paper not only reveal the close relationship between media communication and stock market volatility and provide theoretical support for predicting dramatic stock market volatility, which helps to promote the construction of a healthy and orderly capital market order.

The robot-based automatic power grid defect elimination device represents the future direction of power grid inspection and defect elimination devices. However, the presence of strong electromagnetic interference in the vicinity of power grids poses potential risks to the application of automatic defect elimination devices. This study initially conducts theoretical analysis and calculations to analyse the spatial distribution of electromagnetic fields around 220kV 100MW high-voltage lines in a power grid. The results indicate that electric field intensity can exceed 70kV/m in areas near these high-voltage lines. Subsequently, electromagnetic fields within the operational range of the elimination device are simulated and analysed, leading to selection and optimization of shielding materials and structures. When non-metallic insulating materials are used, equipment is fully exposed to the power grid’s electromagnetic field, making internal devices susceptible to interference; however, employing metal aluminum materials achieves better shielding effects. A continuous metal shielding layer effectively shields against electric fields, while openings or gaps in the shell lead to deteriorated shielding performance. These findings demonstrate that appropriate selection of shielding materials and suitable structural design are crucial factors for achieving excellent shielding effects. To ensure both safety and efficient electromagnetic shielding, a multi-layer composite shielding structure is necessary for power grid elimination devices. Overall, these analysis results provide valuable guidance for designing, operating, and maintaining such devices.

The traditional RSA information encryption algorithm uses one-dimensional chaotic equations to generate pseudo-random sequences that meet the encryption requirements. This encryption method is too simple and the security performance is poor. A multi-level encryption algorithm for user-related information across social networks is proposed, and a user association model across social networks is constructed to obtain user-related information across social networks. This multi-level chaotic encryption algorithm based on neural network is used to select three different chaotic mapping models based on user-related information, and a multi-level chaotic encryption algorithm is designed. According to the characteristics of error sensitivity of chaotic system, the neural network is used to inversely propagate the error. A chaotic encryption algorithm that implements multi-level encryption of user-related information across social networks is optimized. The experimental results show that the average rate for which the proposed algorithm correctly identified the user-related information across social networks was 97.6%, the highest frequency of average character distribution probability in cipher text was 0.021, and the average time for encryption was 18.45 Mbps. The average time for decryption was 21.90Mbps.

In view of problems of redundant search point and poor real-time performance when encountering sudden threat existed in application of traditional algorithm in route planning of unmanned aerial vehicle in environment detection for post-disaster underground, a reverse variable weight sparse algorithm was proposed. According to performance constraint of unmanned aerial vehicle and threat model in post-disaster underground, global static route planning is carried out from target point to start point, so as to avoid a large number of invalid searches. Different weight coefficients are set according to mission requirement of unmanned aerial vehicle, so as to obtain route focuses on distance or safety. Only the path covered by sudden threat is corrected by introducing sub-target point strategy, so as to avoid sudden threat effectively in short time. The simulation results show that the algorithm used in route planning can save time of route planning and reduce threat to unmanned aerial vehicle, which can effectively guarantee real-time performance and safety of route planning.

For problem of inconvenient energy supplement of node in ZigBee wireless sensor network of underground coal mine, a ZigBee routing algorithm adapted to underground coal mine was proposed. In the algorithm, neighbor table is adopted to reduce data transmission hops among nodes, and residual energy of node in the network is sufficiently utilized by use of dynamic control of node residual energy threshold. The simulation results show that the algorithm reduces energy consumption of node in routing process and extends running time of the network.

To compare the role of 99mTc-MIBI SPECT-CT image and US in reoperation of persistent secondary hyperparathyroidism patients. A total of 8 persistent secondary hyperparathyroidism patients underwent parathyroidectomy. The sensitivity and accuracy of US and 99mTc-MIBI images before operation were determined. 9 of 14 surgical resection tissues from 8 persistent secondary hyperparathyroidism patients were confirmed to parathyroid hyperplasia. The results showed that the sensitivities were 77.8% and 100%, respectively, for US and 99mTc-MIBI SPECT-CT images. And the accuracies of US and 99mTc-MIBI dual time planar image and SPECT-CT were 50%, 78.6%, respectively. There was significantly difference between two procedures (P=0.021). The superior and inferior localization of glands were both detected by the ultrasound and scintigraphy. Two ectopic parathyroidism nodules were found by 99mTc- MIBI SPECT-CT imaging, while US provided no consistent findings. There was no significantly difference between two procedures (P=0.3). Although two cases of them were not confirmed by pathology, the iPTH of them (800 and 1429 respectively) were much higher than other four cases (