Engineering New Microvascular Networks On-Chip: Ingredients,Assembly, and Best Practices

Tissue engineered grafts show great potential as regenerative implants for diseased or injured tissues within the human body. However, these grafts suffer from poor nutrient perfusion and waste transport, thus decreasing their viability post-transplantation. Graft vascularization is therefore a major area of focus within tissue engineering because biologically relevant conduits for nutrient and oxygen perfusion can improve viability post-implantation. Many researchers used microphysiological systems as testing platforms for potential grafts owing to an ability to integrate vascular networks as well as biological characteristics such as fluid perfusion, 3D architecture, compartmentalization of tissue-specific materials, and biophysical and biochemical cues. Although many methods of vascularizing these systems exist, microvascular self-assembly has great potential for bench-to-clinic translation as it relies on naturally occurring physiological events. In this review, the past decade of literature is highlighted, and the most important and tunable components yielding a self-assembled vascular network on chip are critically discussed: endothelial cell source, tissue-specific supporting cells, biomaterial scaffolds, biochemical cues, and biophysical forces. This paper discusses the bioengineered systems of angiogenesis, vasculogenesis, and lymphangiogenesis and includes a brief overview of multicellular systems. It concludes with future avenues of research to guide the next generation of vascularized microfluidic models.     

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the improved network structures and loss functions of GAN-based perceptual SISR. Subsequently, the advantages and disadvantages of different networks are analyzed by multiple comparative experiments. Finally, we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.     

Influences of magnetic fields on current–voltage characteristics of gold-DNA-gold structure with variable gaps

Achieving highly sensitive magnetic sensors by means of Metal-DNA-Metal (MDM) structure is a key issue. DNA, being a genetic information carrier in living cells reveals tunable semiconducting response in the presence of external electric and magnetic fields, which is promising for molecular electronics. The influence of magnetic fields up to 1200 mT on the current–voltage (I–V) behavior of Gold-DNA-Gold (GDG) structure having variable gap sizes from 20–50 μm are reported in this work. These structures were fabricated using UV lithography, DC magnetron sputtering and thermal evaporation techniques. DNA strands were extracted from Boesenbergia rotunda plant via standard protocol. The acquired I–V characteristics display the semiconducting diode nature of DNA in GDG structures. The potential barrier for all the structures exhibit an increasing trend with the increase of externally imposed magnetic field irrespective of variable gap sizes. Furthermore, the potential barrier in GDG junction at higher magnetic field strengths (>1000 mT) is found to be considerably enhanced. This enhancement in the junction barrier height at elevated magnetic fields is attributed to the reduction of carrier mobility and augmentation of resistance. The achieved admirable features of magnetic sensitivity suggest the viability of using these GDG sandwiches as a prospective magnetic sensor.     

基于感知评价的Internet语音终端流量控制

分析了影响因特网语音通信质量的主要因素，根据实时传输控制协议计算延迟、丢包率等网络参数，并利用RBF神经网络将参数映射为语音质量评分，根据评分值的变化动态地调整语音终端的编码速率，在保证一定语音质量的同时，缓解网络拥塞。实验证明了该方法的有效性。     

异构分层无线网络中基于逗留时间的动态流量均衡算法研究

为使异构分层无线网络能服务更多的移动用户,提出了一种基于逗留时间的动态流量均衡算法.该算法首先根据用户移动模型计算其在小区内的逗留时间,然后基于小区呼叫到达率和重叠覆盖小区的流量状态来确定一个周期内呼叫转移的数量,最后依据逗留时间门限值将重负载小区中满足条件的呼叫转移到轻负载的重叠覆盖小区中.为降低切换呼叫掉线率,还对异构网间的呼叫切换策略做了改进.仿真实验结果表明,本算法在新呼叫阻塞率和切换呼叫掉线率等性能指标上比传统方法有显著的提高.     

Relationship between poles and zeros of input–output and chain-scattering systems

A system is frequently represented by transfer functions in an input–output characterization. However, such a system (under mild assumptions) can also be represented by transfer functions in a port characterization, frequently referred to as a chain-scattering representation. Due to its cascade properties, the chain-scattering representation is used throughout many fields of engineering. This paper studies the relationship between poles and zeros of input–output and chain-scattering representations of the same system.     

半导体传感器在生物分析科学中的应用(英文)

回顾了半导体生化传感器 (ISFET 和 LA PS)在医学监测、免疫检测、免疫分析、D N A 杂交以及细胞培养等方面应用的进展。为使半导体器件适用于生化检测,对其中所采用的检测手段和取得的研究结果进行了分析。展望了通过与微流体网络相结合,基于半导体传感器的实验室芯片化的可行性。     

基于交会图和多元统计法的神经网络技术在火山岩识别中的应用

针对火山岩储层的特殊性(复杂性、离散性和随机性),应用BP神经网络技术对火山岩测井解释中岩性识别问题进行了研究。该方法的技术关键是样本集和初始权重的建立,以及模型的优选。本文提出了一种基于交会图和多元统计法的学习样本生成方法,即根据取心岩样的地球化学和岩石学研究,用交会图技术建立样本集,采用聚类分析和距离判别法确定初始权重。将研究方法应用在松辽盆地杏山地区火山岩岩性识别问题中,取得了很好的效果,岩性解释符合率高于90%。文中通过四种岩性识别处理模式的对比研究,表明赋权重处理模式为最优处理模型。在神经网络模型预测过程中,需充分利用已有的地质经验和测井曲线信息建立典型可靠的样本文件,同时考虑神经网络方法中各种因素的影响,优选模型和计算参数才能使预测结果符合实际情况。     

无线通信的跨层优化

传统的OSI分层结构无法适应无线网络环境，随着越来越多无线应用的出现，人们提出了跨层设计，其主要内容就是通过在协议栈的各层之间（主要是应用层、传输层、网络层、链路层和物理层）传递特定的信息，使协议栈能够根据无线环境的变化来实现对资源的自适应优化配置，从而更有效的利用无线网络资源，提高系统的性能。     

A Digital Deformation Vacuum Gauge with an LC-Generator

The design of a sylphon bellows sensor and the basic circuits of an LC-generator and of a microprocessor unit are presented. An analytical pressure–frequency conversion function and a special method of adjusting the sensor ensure an error of less than 0.05%. The dynamic range is up to 10⁵. The instruments developed cover the ranges 10³, 10⁴, and 10⁵ Pa.