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This article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances. [ABSTRACT FROM AUTHOR]

The green evaluation of urban power distribution network is the main reference basis for power grid transformation and upgrading. Based on this, this article constructs a comprehensive evaluation index system from the four dimensions of power supply side, consumer side, platform area and related policies; To comprehensively consider the difference and relevance of indicators, this paper adopts the order relationship analysis method and the CRITIC weighting method for subjective and objective combination weighting, and uses linear weighting to score indicators; Finally, combining the four evaluation dimensions to analyze the application approach of the comprehensive evaluation model to verify the practicability of the model.

Due to the higher switching speed and lower threshold voltage of SiC MOSFET, its crosstalk issue is more serious than that of Si devices. Firstly, the mechanism and process of crosstalk under resistive load and inductive load are compared and analyzed. Then, a parameter decoupling method based on energy conservation is proposed, and an analytical model of the crosstalk voltage under resistive load is established. Moreover, an analytical model of the crosstalk voltage under inductive load with only device and circuit parameters is introduced. Furthermore, the influence of the drive resistance and the common source inductance on the crosstalk voltage under different loads is evaluated through the analytical model. Finally, a double pulse test is carried out to verify the validity of the analysis. The results presented here can provide guidance for the design of gate circuit parameters to regulate the crosstalk issue of SiC MOSFETs under different loads. [ABSTRACT FROM AUTHOR]

• A dataset with 4400 images of 11 common fruits was proposed. • Four fast real-time grasp detection models were proposed. • The performance of the four models under different evaluation thresholds were studied. • The analysis can provide theoretical guidance for fruit grasp detection. The automatic picking, sorting, and packaging of fruits require robots to accurately detect the grasping position of fruits. However, accurate detection of grasping positions is challenging due to the diversity of fruit shapes and sizes. At present, research objects of grasping detection are mainly daily necessities and office items, and few studies on fruit-grasping detection are available. To solve these problems, four end-to-end detection models were designed based on three convolutional neural network architectures: Xception, MobileNetV3, and DenseNet. In addition, considering the large amount of data required for deep learning, data augmentation and transfer learning techniques were applied to improve model accuracy and generalization performance. The most widely applied evaluation criteria were used to evaluate the models, and the accuracy of the four models ranged within 83.86%–93.64%. All the models were capable of rapid real-time detection. To verify the robustness, the models were tested under different evaluation thresholds, and the results showed that the models performed well under higher evaluation criteria. Additionally, a dataset containing 4400 images of 11 common fruits was established due to the current lack of data for fruit grasp detection. [ABSTRACT FROM AUTHOR]

The synergies of nanoconfinement and catalysis is an effective strategy to improve the kinetic and thermodynamic properties of Mg-based materials. However, obtaining Mg-based materials with high loading, anti-aggregation, and containing nanocatalysts to achieve dehydrogenation at room temperature remains a huge challenge. Herein, a novel and universal preparation strategy for Mg-Co@C nanocomposites with 9.5 nm Mg nanoparticles and 9.4 nm Co nanocatalysts embedded in carbon scaffold is reported. The 9.3 nm MgBu 2 nanosheets precipitated by solvent displacement are encapsulated in ZIF-67 to prepare MgBu 2 @ZIF-67 precursors, then removing excess MgBu 2 on the precursor surface and pyrolysis to obtain Mg-Co@C. It is worth noting that the Mg loading rate of Mg-Co@C is as high as rare 69.7%. Excitingly, the Mg-Co@C begins to dehydrogenate at room temperature with saturate capacity of 5.1 wt.%. Meanwhile, its dehydrogenation activation energy (Ea (des) = 68.8 kJ mol -1 ) and enthalpy (ΔH (des) = 61.6 kJ mol -1 ) significantly decrease compared to bulk Mg. First principles calculations indicate that the hydrogen adsorption energy on the Mg 2 CoH 5 surface is only -0.681 eV. This work provides a universally applicable novel method for the preparation of nanoscale Mg-based materials with various nanocatalysts added, and provides new ideas for Mg-based materials to achieve room temperature hydrogen storage., (© 2024 Wiley‐VCH GmbH.)

The construction of stable and efficient nanocomposites with low addition and light weight has always been the goal pursued in the field of electromagnetic wave (EMW) absorption. In this study, the Co@CNTs nanocomposites with Co nanoparticles (13 nm) nanoconfined in the carbon nanotube (CNT) are successfully synthesized by a simple hydrothermal method and phenolic assisted pyrolysis method. The degree of graphitization of CNTs and the microstructure of Co nanoparticles can be effectively regulated by controlling the calcination temperature. The sample calcined at 700 °C can obtain excellent absorption performance at a low filling capacity of 10 wt.%: the minimum reflection loss (RL) is -41.2 dB and the effective absorption bandwidth (EAB) reaches a maximum width of 14.2 GHz. When the sample thickness is only 2.2 mm, the EAB of <-20 dB reaches 8.3 GHz, which is the maximum EAB of most current Co-based absorbers. In particular, the polarization and ferromagnetic coupling behaviors are elucidated in depth with the aid of electromagnetic field simulations using the High-Frequency Structure Simulator (HFSS). This work provides a new nanoconfinement strategy for constructing the Co@CNTs nanocomposites as lightweight and ultra-broadband absorbing materials for EMW protection and EMW pollution control., (© 2024 Wiley‐VCH GmbH.)