Search

A series of red-luminescence bismuthate phosphors of double perovskite BaBiNaTeO6 doped with trivalent europium (Eu3+) were prepared by the solid-state diffusion method. The crystal structure and phase purity of the phosphors were analyzed. The photoluminescence (PL) properties of BaBiNaTeO6:Eu3+ were studied in detail. Upon blue light (466 nm) excitation, the powders showed brilliant red light at around 619 nm, and it was attributed to the electric dipole (ED) 5D0 → 7F2 transition of Eu3+. The optimal dopant concentration (x) of the as-prepared phosphor was x = 0.60 mol and the concentration quenching mechanism was evaluated as the electric quadrupole–quadrupole interaction. The BaBiNaTeO6:Eu3+ exhibited good thermal stability and the corresponding activation energy (Ea) was obtained to be 0.27 eV. The chromaticity points of phosphors were near the edge of the red region of the Commission Internationale De L’Eclairage (CIE) 1931 chromaticity diagram. As-prepared white LED based on the GaN blue chip exhibited relatively low correlated color temperature (CCT) of 5042.5 K and high color rendering index (CRI) of 91.6. All characterizations suggest that these double perovskite BaBiNaTeO6:Eu3+ red-emitting phosphors have promising applications in illumination and display field based on blue LEDs. [ABSTRACT FROM AUTHOR]

There are limited studies on the impact of domain-specific self-efficacy on academic achievements. The geospatial thinking ability is paramount to understand the relationship between geographical self-efficacy and academic achievements in geography. This study aims to explore the mediating effect of geospatial thinking on the relationship between geographical self-efficacy and academic achievements, and the moderating role of gender and attendance type. A total of 749 Chinese high school students, working as participants, anonymously completed a questionnaire covering topics like geographical spatial thinking, geographical self-efficacy, academic achievements in geography, gender, attendance type, and place of residence. The analysis using MPLUS 8.3 software indicates that geographical self-efficacy significantly predicts academic achievements in geography. Geospatial thinking plays a significant mediating role in this pathway, with gender and type of attendance having moderating effects. This study enhances the understanding between domain-specific self-efficacy and academic achievements, providing crucial guidance for educational practices, such as emphasizing geospatial thinking training for high school students, focusing on encouraging female students, and properly scheduling rest times for boarders, which will significantly contribute to the sustainable development of geography education. [ABSTRACT FROM AUTHOR]

In order to solve the problem of uneven distribution of air conditioning temperature and air velocity in the ward, it is easy to produce the uncomfortable feeling of cold and hot and wind blowing. Taking the standard double room general ward of a newly built hospital as the research object, a mathematical model was established, and CFD software was used to numerically simulate the temperature field and velocity field of the air in the ward, optimize the location of the air outlet, and put forward the suggestion of the optimal installation location of the hospital bed. The results show that when the fan coiler air supply outlet is close to the west wall and the fresh air outlet is far away from the west wall, the inhomogeneity of the distribution of the temperature field and velocity field in the ward is reduced, and at this time, the temperature and velocity distribution of the air in the ward is more uniform, so that the patients' overall feeling in the ward is more comfortable, and at this time, the installation position of the air outlet is more reasonable; and it is determined that the optimal placement of the beds in the room is at the locations of x = 2 m, y = 2.5 m and x = 2 m, y = 4.5 m, and at this time the two beds are at the same time, which is more reasonable. When y = 4.5 m, the temperature difference between the two beds is less than 0.5 K, the wind speed difference is not more than 0.08 m/s, and the velocity gradient direction is the same. [ABSTRACT FROM AUTHOR]

This research focuses on the dynamic modeling of Delta 3D printer multi-energy domain mechanical-electrical coupling system. Based on the advantages of screw bond graph in the dynamic modeling of parallel robot，the dynamic model of the printer's mechanical ontology subsystem is constructed，and the dynamic model of the driving subsystem is established by combining with the traditional bond graph，and the global dynamic model of the printer's mechanical-electrical coupling is obtained. For a given moving platform trajectory，comparing the results of Adams numerical simulation，Matlab theoretical calculation and experimental driving force measurement show that the full solution model of multi-energy domain system dynamics of the mechanism is correct. The results lay a foundation for the following dynamic parameter identification and dynamic control research，and also provide a new idea for other mechanical system dynamics modeling.

A numerical method is proposed to evaluate the effect of non‐Gaussian roughness on point contact elastohydrodynamic lubrication (EHL). A mixed EHL model of point contact, considering non‐Gaussian roughness, is developed. The oil film pressure in the model was controlled by the Reynolds equation of average flow, and the contact pressure of asperity was obtained by the rough surface micro‐contact model. The influence of non‐Gaussian roughness parameters on contact pressure distribution, film thickness profile and the ratio of asperity pressure to contact load (asperity load ratio) are investigated based on the mixed EHL model. The results show that the asperity pressure increases as root mean square (RMS) roughness increases, skewness and kurtosis. The film thickness also increases with RMS roughness and skewness, but is not sensitive to kurtosis. [ABSTRACT FROM AUTHOR]

Edge intelligence has emerged as a promising paradigm to accelerate DNN inference by model partitioning, which is particularly useful for intelligent scenarios that demand high accuracy and low latency. However, the dynamic nature of the edge environment and the diversity of end devices pose a significant challenge for DNN model partitioning strategies. Meanwhile, limited resources of the edge server make it difficult to manage resource allocation efficiently among multiple devices. In addition, most of the existing studies disregard the different service requirements of the DNN inference tasks, such as its high accuracy-sensitive or high latency-sensitive. To address these challenges, we propose a Multi-Compression Scale DNN Inference Acceleration (MCIA) based on cloud-edge-end collaboration. We model this problem as a mixed-integer multi-dimensional optimization problem, jointly optimizing the DNN model version choice, the partitioning choice, and the allocation of computational and bandwidth resources to maximize the tradeoff between inference accuracy and latency depending on the property of the tasks. Initially, we train multiple versions of DNN inference models with different compression scales in the cloud, and deploy them to end devices and edge server. Next, a deep reinforcement learning-based algorithm is developed for joint decision making of adaptive collaborative inference and resource allocation based on the current multi-compression scale models and the task property. Experimental results show that MCIA can adapt to heterogeneous devices and dynamic networks, and has superior performance compared with other methods. [ABSTRACT FROM AUTHOR]