Composite bending-dominated hollow nanolattices: A stiff,cyclable mechanical metamaterial

Manufacturing ultralight and mechanical reliable materials has been a long-time challenge. Ceramic-based mechanical metamaterials provide significant opportunities to reverse their brittle nature and unstable mechanical properties and have great potential as strong, ultralight, and ultrastiff materials. However, the failure of ceramics nanolattice and degradation of strength/modulus with decreasing density are caused by buckling of the struts and failure of the nodes within the nanolattices, especially during cyclic loading. Here, we explore a new class of 3D ceramic-based metamaterials with a high strength–density ratio, stiffness, recoverability, cyclability, and optimal scaling factor. Deformation mode of the fabricated nanolattices has been engineered through the unique material design and architecture tailoring. Bending-dominated hollow nanolattice (B-H-Lattice) structure is employed to take advantages of its flexibility, while a few nanometers of carbonized mussel-inspired bio-polymer (C-PDA) is coherently deposited on ceramics’ nanolayer to enable non-buckling struts and bendable nodes during deformation, resulting in reliable mechanical properties and outperforming the current bending-dominated lattices (B-Lattices) and carbon-based cellulose materials. Meanwhile, the structure has comparable stiffness to stretching-dominated lattices (S-Lattices) while with better cyclability and reliability. The B-H-Lattices exhibit high specific stiffness (>10⁶?Pa·kg^?1·m^?3), low-density (～30?kg/m³), buckling-free recovery at 55% strain, and stable cyclic loading behavior under up to 15% strain. As one of the B-Lattices, the modulus scaling factor reaches 1.27, which is lowest among current B-Lattices. This study suggests that non-buckling behavior and reliable nodes are the key factors that contribute to the outstanding mechanical performance of nanolattice materials. A new concept of engineering the internal deformation behavior of mechanical metamaterial is provided to optimize their mechanical properties in real service conditions.     

基于数字兆欧表自动切换档位的研究与实现

随着电力、电气设备的日益复杂化,对于其安全性能也有了更高的要求,数字兆欧表作为测量绝缘阻值的重要工具,已在工业领域中广泛应用;为了满足测量精度以及兆欧表的可靠性,通过基于S3C2440的嵌入式平台,提出了一种基于数字兆欧表的自动切换档位的实现方案,以达到更加精确的测量;此方案通过判断采集到的电压值选择相应的采样电阻来实现,并结合硬件平台和软件设计,分别介绍了驱动程序和应用程序的编写与测试,并很好地应用到了实际电路中;经测试,能够达到预期的效果,测量精度高,测量误差控制在±2%。     

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.     

GPS在航天器交会对接中的应用研究

ＧＰＳ动态定位技术应用于交会对接中与目前采用的测量手段相比具有较大优势。文章首先对交会对接各阶段测量精度要求与ＧＰＳ能达到的精度做出比较,在此基础上给邮了ＧＰＳ技术应用于航天器交会对接过程的基本方案,重点讨论各阶段的数学模型,最后论述了其优越性。     

智能型双电源开关控制器的设计

提出了一种以PIC16F884单片机为控制单元,以两台塑壳断路器双电源开关为控制对象的智能控制器的设计方案.介绍了控制器的工作原理和工作模式,并给出了硬件方案和软件设计思想以及抗干扰的方法.     

CAN总线网络测试研究

介绍了CAN总线网络开发方法,搭建了CAN总线集成开发测试环境,对实际网络进行了测试,给出了位定时参数计算方法,比较了不同模拟终端电阻下的总线的报文正确率和总线负载率等性能指标.     

A two-level strategy for segmenting center of interest from pictures

Segmenting center of interests (COIs) from pictures is an important but highly challenging problem for researchers in computer vision and image processing. The capability of understanding the meanings of pictures by computers can lead to breakthroughs in a wide range of applications including Web image search and online picture-sharing communities. In this paper, a two-level strategy is presented, which consists of a rough segmentation stage and a fine segmentation stage. In the first level, a picture is partitioned into four regions by using a block clustering method based on color and texture features, and the COI within the picture is distinguished from the background according to the principles of photographic composition. This stage aims to determine the approximate region of the target. In the second level, a novel active contour model is established based on shape information and vector method, where the image energy is defined by a hue gradient and the external energy is generated from either a triangular inner force or a supplementary force. This stage tries to extract the boundary of the target accurately. Experimental results on photos downloaded from the Internet show the feasibility and effectiveness of the proposed method.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

五年制高职动漫专业毕业设计考核评价探究

提升职业教育教学质量是高职院校的核心任务。毕业设计是专业教学的重要环节,它不仅关系到毕业生的质量,也关系到专业的未来发展。从考核评价的角度,对五年制高职动漫专业学生的毕业设计进行可操作性的探讨。