The association between social media use and well-being during quarantine period: testing a moderated mediation model

ObjectivesSocial media use (SMU) increased dramatically during COVID-19 due to policies such as long-term quarantine. Given that SMU has complex effects on individuals’ well-being, this study aimed to explore the relationship between SMU and subjective well-being and the influencing factors in the context of the pandemic in China.MethodsA total of 895 adults (413 males) in different risk areas across China participated in this study. They provided self-reported data on subjective well-being, social media use, adaptive humor, and other demographic variables.ResultsIt revealed that SMU was positively associated with individual well-being, an effect partially mediated by the score of adaptive humor. Furthermore, the effect of SMU on adaptive humor was moderated by trait optimism, with the effect more robust in high (vs. low) optimistic individuals.ConclusionThis study explored the positive effects of SMU on individuals’ well-being, suggesting that individuals may better cope with negative experiences and maintain well-being under quarantine by showing more adaptive humor on social media

Research on Predicting the Safety Factor of Plain Shotcrete Support in Laneways Based on BO-CatBoost Model

In general, the design of a safe and rational laneway support scheme signifies a crucial prerequisite for ensuring the security and efficiency of mining exploitation in mines. Nevertheless, the conventional empirical support system for mining laneways faces challenges in assessing the rationality of support methods, which can compromise the safety and reliability of the laneways. To address this issue, the safety factor was incorporated into research on laneway support, and a safety evaluation method for laneway support in line with the safety factor was established. In light of the data from a specific iron mine laneway in central China, the CRITIC method was employed to preprocess the sample data. Going one step further, a Bayesian algorithm was utilized to optimize the hyperparameters of the CatBoost model, followed by proposing a prediction model based on the BO-CatBoost model for evaluating laneway safety factors of plain shotcrete support. Furthermore, the performance indexes, such as the root mean square error (RMSE), the mean absolute error (MAE), the correlation coefficient (R2), the variance accounts for (VAF), and the a-20 index, were determined to examine the predictive performance of each proposed model. In contrast to the other models, the BO-CatBoost model demonstrated the optimal predictive output item for safety factors with the lowest RMSE and MAE, the largest R2 and VAF, and an appropriate a-20 index value of 0.5688, 0.4074, 0.9553, 95.25%, and 0.9167 in the test set, respectively. Therefore, the BO-CatBoost model was proven to be the most appropriate machine learning method that can more accurately predict the safety factor, which will provide a novel approach for optimizing laneway support design and laneway safety evaluation

Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing

With the convergence of the Internet of Things, 5G, and artificial intelligence, limited network bandwidth and bursts of incoming service requests seem to be the most important factors affecting user experience. Therefore, caching technology was introduced. In this paper, a caching placement optimization strategy based on comprehensive utility (CPOSCU) in edge computing is proposed. Firstly, the strategy involves quantifying the placement factors of data blocks, which include the popularity of data blocks, the remaining validity ratio of data blocks, and the substitution rate of servers. By analyzing the characteristics of cache objects and servers, these placement factors are modeled to determine the cache value of data blocks. Then, the optimization problem for cache placement is quantified comprehensively based on the cache value of data blocks, data block retrieval costs, data block placement costs, and replacement costs. Finally, to break out of the partial optimal solution for cache placement, a penalty strategy is introduced, and an improved tabu search algorithm is used to find the best edge server placement for cached objects. Experimental results demonstrate that the proposed caching strategy enhances the cache service rate, reduces user request latency and system overhead, and enhances the user experience

Dynamic Selection Slicing-Based Offloading Algorithm for In-Vehicle Tasks in Mobile Edge Computing

With the surge in tasks for in-vehicle terminals, the resulting network congestion and time delay cannot meet the service needs of users. Offloading algorithms are introduced to handle vehicular tasks, which will greatly improve the above problems. In this paper, the dependencies of vehicular tasks are represented as directed acyclic graphs, and network slices are integrated within the edge server. The Dynamic Selection Slicing-based Offloading Algorithm for in-vehicle tasks in MEC (DSSO) is proposed. First, a computational offloading model for vehicular tasks is established based on available resources, wireless channel state, and vehicle loading level. Second, the solution of the model is transformed into a Markov decision process, and the combination of the DQN algorithm and Dueling Network from deep reinforcement learning is used to select the appropriate slices and dynamically update the optimal offloading strategy for in-vehicle tasks in the effective interval. Finally, an experimental environment is set up to compare the DSSO algorithm with LOCAL, MINCO, and DJROM, the results show that the system energy consumption of DSSO algorithm resources is reduced by 10.31%, the time latency is decreased by 22.75%, and the ratio of dropped tasks is decreased by 28.71%

Development and Application of Triangular Main Transformer On-load Tap Changer Hoisting Equipment for Overhaul

On-load tap changer is an important part of transformer. It is playing an important role in voltage regulation, and its safe and stable operation affects the reliability of power supply of the power grid. The tap changer has to maintenance after a certain number of operations. In the maintenance process, there are problems such as long time consumption and low efficiency. On this basis, this paper proposes a development on the main transformer on-load tap changer hoisting equipment. It has been verified in practice that the equipment can significantly shorten the working time and improve the working efficiency under the premise of ensuring the work safety

Development and Application of Triangular Main Transformer On-load Tap Changer Hoisting Equipment for Overhaul

On-load tap changer is an important part of transformer. It is playing an important role in voltage regulation, and its safe and stable operation affects the reliability of power supply of the power grid. The tap changer has to maintenance after a certain number of operations. In the maintenance process, there are problems such as long time consumption and low efficiency. On this basis, this paper proposes a development on the main transformer on-load tap changer hoisting equipment. It has been verified in practice that the equipment can significantly shorten the working time and improve the working efficiency under the premise of ensuring the work safety

Performance Evaluation and Comparison between Direct and Chemical-Assisted Picosecond Laser Micro-Trepanning of Single Crystalline Silicon

The fabrication of micro-holes in silicon substrates that have a proper taper, higher depth-to-diameter ratio, and better surface quality has been attracting intense interest for a long time due to its importance in the semiconductor and MEMS (Micro-Electro-Mechanical System) industry. In this paper, an experimental investigation of the machining performance of the direct and chemical-assisted picosecond laser trepanning of single crystalline silicon is conducted, with a view to assess the two machining methods. The relevant parameters affecting the trepanning process are considered, employing the orthogonal experimental design scheme. It is found that the direct laser trepanning results are associated with evident thermal defects, while the chemical-assisted method is capable of machining micro-holes with negligible thermal damage. Range analysis is then carried out, and the effects of the processing parameters on the hole characteristics are amply discussed to obtain the recommended parameters. Finally, the material removal mechanisms that are involved in the two machining methods are adequately analyzed. For the chemical-assisted trepanning case, the enhanced material removal rate may be attributed to the serious mechanical effects caused by the liquid-confined plasma and cavitation bubbles, and the chemical etching effect provided by NaOH solution

Safety assessment for autonomous vehicles: A reference driver model for highway merging scenarios

Driver models are crucial for the safety assessment of autonomous vehicles (AVs) because of their role as reference models. Specifically, an AV is expected to achieve at least the same level of safety performance as a careful and competent driver model. To make this comparison possible, quantitative modeling of careful and competent driver models is essential. Thus, the UNECE Regulation No. 157 proposes two driver models as benchmarks for AVs, enabling safety assessment of AV longitudinal behaviors. However, these two driver models are unable to be applied in non-car-following scenarios, limiting their applications in scenarios such as highway merging. To this end, we propose a careful and competent driver model for highway merging (CCDM2) scenarios using interpretable reinforcement learning-based decision-making and safety constraint control. We compare our model's safe driving capabilities with human drivers in challenging merging scenarios and demonstrate the "careful" and "competent" characteristics of our model while ensuring its interpretability. The results indicate the model's capability to handle merging scenarios with even better safety performance than human drivers. This model is of great value for AV safety assessment in merging scenarios and contributes to future reference driver models to be included in AV safety regulations

FedPRM:Federated Personalized Mixture Representation for Driver Intention Prediction

Driver intention prediction has the potential to greatly improve the ability of autonomous vehicles (AVs) to effectively handle risky driving behaviors, thereby ensuring driving safety. Conventional data-driven approaches for driver intention prediction models typically involve gathering extensive driver-related data, which raises significant privacy concerns. With the development of the Internet of Vehicles (IoV), federated learning (FL) has emerged as a prominent privacy-preserving learning paradigm, garnering considerable attention. However, FL encounters challenges in driver intention prediction due to the heterogeneity of driver client data and the limited computational resources of vehicles. To address these challenges, this paper proposes the FedPMR framework, comprising a computationally efficient model for predicting driver intentions. Moreover, to tackle the problem of data heterogeneity, it leverages personalized mixture representation to provide a personalized model adapted to the local data distribution of each driver client. We conducted extensive experiments on the Brain4Cars dataset, achieving an F1-score of 95.24&amp;#x0025; and a comprehensive evaluation metric of 0.9663, exceeding state-of-the-art. The experimental results demonstrate that the proposed FedPMR effectively addresses the challenges encountered when applying FL to driver intention prediction.</p