摘要：A comparative optimal design of fluid-saturated prismatic cellular metal honeycombs （PCMHs） having different cell shapes is presented for thermal management applications. Based on the periodic topology of each PCMH, a unit cell （UC） for thermal transport analysis was selected to calculate its effective thermal conductivity. Without introducing any empirical coefficient, we modified and extended the analytical model of parallel-series thermal-electric network to a wider porosity range （0.7 ~ 0.98） by considering the effects of two-dimensional local heat conduction in solid ligaments inside each UC. Good agreement was achieved between analytical predictions and numerical simulations based on the method of finite volume. The concept of ligament heat conduction efficiency （LTCE） was proposed to physically explain the mechanisms underlying the effects of ligament configuration on effective thermal conductivity （ETC）. Based upon the proposed theory, a construct strategy was developed for designing the ETC by altering the equivalent interaction angle with the direction of heat flow： relatively small average interaction angle for thermal conduction and relatively large one for thermal insulation.

摘要：利用混沌系统广义同步定理,基于蔡电路系统构造了一个新的混沌系统,基于该系统设计了一个文本复合加密隐藏方案。该方案首先用复合系统产生的混沌码对明文加密,然后将密文隐藏到语音信号中。方案能够实现无失真解密。数值模拟显示新加密方案产生的混沌流具有良好的伪随机性。该方案对混沌系统的参数及初始条件极为敏感,表明该方案具有较高的安全性。数值模拟显示该方案能够有效地应用于网络通讯。

摘要：开发非贵金属催化剂,特别是用于碳上铁氮(FeNC)材料的催化剂,对于质子交换膜燃料电池(PEMFC)的广泛应用是一个迫切需求。然而,传统铁氮位点在酸性条件下的氧还原反应(ORR)活性较差,严重阻碍了其电池性能的进一步提高。本文通过限域的小分子合成策略,大规模合成了具有一氧化氮(NO)基团轴向修饰的FeN_(4)(表示为NO-FeN_(4))。得益于NO基团的强吸电子效应,与传统的FeN_(4)样品相比,富含电子中心FeN_(4)位点表现出超高的ORR活性,具有三倍高的质量活性(0.85 V时为1.1 A·g^(−1)),以及全四电子反应选择性。此外,用所制备的电催化剂组装的质子交换膜燃料电池也表现出显著增强的峰值功率密度(>725 mW·cm^(−2))。这项工作为合理设计用于氧还原的先进M-Nx非贵金属电催化剂提供了一种新的方法。

摘要：Light detection and ranging (LIDAR) based on time of flight (TOF) method is widely used in many fields related to distance measurement. LIDAR generally uses laser diode (LD) to emit the pulsed laser with high peak power and short duration to ensure a large distance measurement range and eye safety. To achieve this goal, we propose a pulsed LD drive method producing the drive current with high peak and narrow pulse width. We analyze the key issues and related theories of the drive current generation based on this method and design an LD driver. A model of drive current generation is established and the influence of operating frequency on drive current is discussed. The LD driver is simulated by software and verified by experiments. The working frequency of the driver changes from 20 kHz to 100 kHz and the charging voltage is set at 130 V. The current produced by this driver has a duration of 8.8 ns and a peak of about 35 A, and the peak output optical power of the LD exceeds 75 W.

摘要：以洛河特大桥实际工程为背景,利用Midas Civil软件建立了高墩桥在设计荷载作用下的模型,并分析了2种类型横断面高墩连续钢混构桥在不同荷载组合下的力学特性.经对比,在工况1,3中,A型横断面结构承载能力较B型有所提升,且A型横断面轴向应力最大值在1,3工况中较B型分别减少了28.54%,27.88%;在工况3中,A和B型高墩桥挠度最大值分别为3.953,3.643 mm,A型桥梁挠度较B型减少了7.84%,A型高墩桥结构刚度提高,结构变形降低.在各类工况中,高墩桥应力均满足强度要求.

摘要：Mobile edge computing (MEC) has a vital role in various delay-sensitive applications. With the increasing popularity of low-computing-capability Internet of Things (IoT) devices in industry 4.0 technology, MEC also facilitates wireless power transfer, enhancing efficiency and sustainability for these devices. The most related studies concerning the computation rate in MEC are based on the coordinate descent method, the alternating direction method of multipliers (ADMMs) and Lyapunov optimization. Nevertheless, these studies do not consider the buffer queue size. This research work concerns the computation rate maximization for wireless-powered and multiple-user MEC systems, specifically focusing on the computation rate of end devices and managing the task buffer queue before computation at the terminal devices. A deep reinforcement learning (RL)-based task offloading algorithm is proposed to maximize the computation rate of end devices and minimizes the buffer queue size at the terminal ***, considering the channel gain, the buffer queue size and wireless power transfer, it further formalizes the task offloading problem. The mode selection for task offloading is based on the individual channel gain, the buffer queue size and wireless power transfer maximization in a particular time *** central idea of this work is to explore the best optimal mode selection for IoT devices connected to the MEC system. The proposed algorithm optimizes computation delay by maximizing the computation rate of end devices and minimizing the buffer queue size before computation at the terminal devices. Then, the current study presents a deep RL-based task offloading algorithm to solve such a mixed-integer and non-convex optimization problem, aiming to get a better trade-off between the buffer queue size and the computation rate. The extensive simulation results reveal that the presented algorithm is much more efficient than the existing work to maintain a small buffer qu