Sound Absorption Performance and Mechanism of Aluminum Foams with Double Main Pore-Porous Cell Wall Structure

To enhance the sound absorption performance of open-cell aluminum foam, the double main pores-porous cell walls (DMP-PCW) aluminum foams via infiltration casting of preforms mixed with two sizes of NaCl particles are prepared. The pore structure, sound absorption performance, and mechanism of DMP-PCW aluminum foam are investigated. The pore structure consists of double-sized main pores similar to the NaCl particles and the cell wall pores formed by the connections between NaCl particles. It is found that the static flow resistivity of DMP-PCW aluminum foam reaches an optimum value of 28105 Pa.s m⁻² when the volume proportion of small main pores increases, the size of cell wall pores decreases, and the number of cell wall pores per unit main pore surface area (NPPA) increases. At 800–6300 Hz, the average absorption coefficient is 0.89. In addition, the Wilson model predicts the sound absorption properties of DMP-PCW aluminum foam. The predicted values agree well with the measured values. The finite-element acoustic simulations and dynamic viscous-thermal permeability calculations reveal that the improved sound absorption performance of DMP-PCW aluminum foam is correlated to the enhanced sound transmission caused by increased NPPA and increased viscous-thermal loss due to the double main pore structure.     

A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the resource consumption caused by frequent data communication while alleviating the effect of device heterogeneity. FixedAsynFL uses fixed-point quantization to compress the local and global models in AsynFL. In order to balance energy consumption and learning accuracy, this paper proposed a quantization scale selection mechanism. This paper examines the mathematical relationship between the quantization scale and energy consumption of the computation/communication process in the FixedAsynFL. Based on considering the upper bound of quantization noise, this paper optimizes the quantization scale by minimizing communication and computation consumption. This paper performs pertinent experiments on the MNIST dataset with several edge nodes of different computing efficiency. The results show that the FixedAsynFL algorithm with an 8-bit quantization can significantly reduce the communication data size by 81.3% and save the computation energy in the training phase by 74.9% without significant loss of accuracy. According to the experimental results, we can see that the proposed AsynFixedFL algorithm can effectively solve the problem of device heterogeneity and energy consumption limitation of edge nodes.     

Peptidyl Virus-Like Nanovesicles as Reconfigurable “Trojan Horse” for Targeted siRNA Delivery and Synergistic Inhibition of Cancer Cells

The self-assembly of peptidyl virus-like nanovesicles (pVLNs) composed of highly ordered peptide bilayer membranes that encapsulate the small interfering RNA (siRNA) is reported. The targeting and enzyme-responsive sequences on the bilayer's surface allow the pVLNs to enter cancer cells with high efficiency and control the release of genetic drugs in response to the subcellular environment. By transforming its structure in response to the highly expressed enzyme matrix metalloproteinase 7 (MMP-7) in cancer cells, it helps the siRNA escape from the lysosomes, resulting in a final silencing efficiency of 92%. Moreover, the pVLNs can serve as reconfigurable “Trojan horse” by transforming into membranes triggered by the MMP-7 and disrupting the cytoplasmic structure, thereby achieving synergistic anticancer effects and 96% cancer cell mortality with little damage to normal cells. The pVLNs benefit from their biocompatibility, targeting, and enzyme responsiveness, making them a promising platform for gene therapy and anticancer therapy.     

多元随机回归系数和参数的线性估计可容许的几个结果

在两种矩阵损失函数下讨论随机效应多元线性模型中回归系数和参数的线性估计的可容许性,并且在齐次和非齐次线性估计类中分别得到了可容许估计的充要条件。     

长江防洪系统联合调度研究

应用系统分析的方法,通过系统解耦将以三峡为中心的长江防洪系统分解为３个子系统,建立了大系统解耦模型．根据子系统之间的耦合关联,提出了各子系统模型解算及全系统的总体协调的解算方法．对长江３次大洪水进行防洪系统联合调度解算,其结果表明所提出的模型和算法是合理的,可明显提高防洪效益．     

陶瓷刀具模糊可靠性及其与刀具材料力学性能的关系

基于模糊数学和可靠性数学的基本理论 ,阐述了刀具模糊可靠性的基本概念和评价方法 ,并进一步应用于陶瓷刀具研究领域 ,建立了陶瓷刀具模糊可靠性与刀具材料力学性能的关系 ,从而使得不做或少做切削实验就能评价陶瓷刀具可靠性的设想成为可能     

可视化供配电CAD系统的研究开发

基于科学可视化,对象建模,面向对象的软件开发技术,在解决了图形系统快速开发的几个关键技术,对象关联实现方法及电网中电气元件潜在拓扑描述等问题的基础上,开发了功能较烷 备的实用化供配电ＣＡＤ及管理系统软件。同时,通过开放数据库互联技术实现了图形系统、计算接口与数据管理系统的集成,解决了动态扩充功能,对开发专业领域ＣＡＤ软件具有借鉴作用。     

砂轮平衡精度的最小二乘测试计算法

提出一种测算砂轮不平衡精度的最小二乘新方法 ,给出基本原理和具体算法 .该方法能有效抑制测量误差的影响 ,大大提高测试精度 ;并能同时测出残余不平衡量的大小和相位 .可用于各类平衡器的精度测定     

便携式危险气体报警器的研究与设计

通过测定瓦斯的主要成分甲烷的浓度来间接测量瓦斯浓度是解决目前我国煤矿瓦斯事故多发的有效途径;但大多数测定瓦斯浓度的设备具有体积大、难移动的缺点;针对以上情况,基于对MSP430系列单片机超低功耗和片内外设丰富的特点的深入了解和研究,设计了以甲烷为被检测气体,以MSP430F135为微控制器的便携式危险气体报警器;通过它能够实时监测矿井中甲烷的浓度,并在浓度超过报警上限值时启动蜂鸣、闪光和振动为一体的报警功能;其声、光和震动三为一体的报警功能具有鲜明的独创性和重要性。     

大型机载数据链系统箱内测试的研究与实现

随着各种电子系统的大量使用,其日常维护任务日益加重,维护人员的大部分精力都消耗在定期维护测试中,维护效率低下已成为制约电子系统正常使用的重要因素之一;现以某大型机载数据链系统为研究背景,结合其自身工作特点和设计经验,将箱内测试技术与机内测试技术相结合,设计实现了该大型机载数据链系统的箱内测试;通过实验实践和使用维护证明,对该大型机载数据链系统使用箱内测试技术有效提高了系统的维护效率和维护工作便捷性,实现了全寿命周期内大幅缩减设计和使用维护成本的目的.