Group mobility assisted network selection framework in 5G vehicular cognitive radio networks

The heterogeneity nature of networks is the most eminent characteristic in 5G vehicular cognitive radio networks across complex radio environments. Since multiple communicating radios may be in motion at the same time in a vehicle. So, group mobility is the most prominent characteristic that requires to be a deep investigation. Therefore, different communication radios that are moving on a train/bus needed to select the networks simultaneously. Without considering the group mobility feature, there is a possibility that the same network may be selected by each moving node and cause congestion in a particular network. To overcome this problem, a novel network selection technique considering the group mobility feature is proposed to improve the throughput of the network. In this work, a 5G vehicular cognitive radio network scenario is also realized using USRP-2954 and LabVIEW communications system design suite testbed. The performance metrics like transmission delay, packet loss rate, reject rate and, channel utilization for vehicular nodes, are gained to analyze the proposed technique in vehicular cognitive radio networks environment. The proposed technique demonstrates a remarkable improvement in channel utilization for vehicular nodes and outperformed conventional schemes.     

改革开放以来高中化学教科书物质结构内容选择及演变

以1978年改革开放以来人民教育出版社出版的5套高中化学教科书作为研究对象，梳理其物质结构内容的演变，发现近40年来高中化学教科书中物质结构内容选择呈钟摆现象，同时其内容组织的结构化理念日益清晰。此外，为新时期我国高中化学教科书的编写提供了一些建议。     

热光伏能量转换器件的热力学极限与优化性能预测

受不可逆损失的影响,热光伏能量转换器件在高品位热能回收与利用方面受到限制.本文揭示不可逆损失来源,提供热光伏能量转换器件性能提升方案.利用半导体物理和普朗克热辐射理论,确定热光伏能量转换器件在理想条件下的最大效率.进而考虑Auger与Shockley-Reed-Hall非辐射复合和不可逆传热损失对光伏电池的电学、光学和热学特性的影响,预测热光伏器件优化性能.确定功率密度、效率和光子截止能量的优化区间.结果表明:相比于理想热光伏器件,非理想热光伏器件的开路电压、短路电流密度和效率有所降低;优化热光伏电池电压、光子截止能量和热源温度,可提升器件的功率密度和效率.通过对比发现理论与实验结果较一致,所得结果可为实际热光伏能量转换器件的研制提供理论指导.     

Numerical optimisation of chemotherapy dosage under antiangiogenic treatment in the presence of drug resistance

We consider a two-compartment model of chemotherapy resistant tumour growth under angiogenic signalling. Our model is based on the one proposed by Hahnfeldt et al. (1999), but we divide tumour cells into sensitive and resistant subpopulations. We study the influence of antiangiogenic treatment in combination with chemotherapy. The main goal is to investigate how sensitive are the theoretically optimal protocols to changes in parameters quantifying the interactions between tumour cells in the sensitive and resistant compartments, that is, the competition coefficients and mutation rates, and whether inclusion of an antiangiogenic treatment affects these results. Global existence and positivity of solutions and bifurcations (including bistability and hysteresis) with respect to the chemotherapy dose are studied. We assume that the antiangiogenic agents are supplied indefinitely and at a constant rate. Two optimisation problems are then considered. In the first problem a constant, indefinite chemotherapy dose is optimised to maximise the time needed for the tumour to reach a critical (fatal) volume. It is shown that maximum survival time is generally obtained for intermediate drug dose. Moreover, the competition coefficients have a more visible influence on survival time than the mutation rates. In the second problem, an optimal dosage over a short, 30-day time period, is found. A novel, explicit running penalty for drug resistance is included in the objective functional. It is concluded that, after an initial full-dose interval, an administration of intermediate dose is optimal over a broad range of parameters. Moreover, mutation rates play an important role in deciding which short-term protocol is optimal. These results are independent of whether antiangiogenic treatment is applied or not.     

基于噪声误差模型的不规则区域室内定位和锚点分布优化

结合实际工业背景,研究了一类在不规则区域且误差不服从高斯分布的室内无线定位问题.给出了噪声误差模型,在对多个传统定位算法进行性能分析的基础上,研究了待定位区域内锚点阵列的分布,改进了多锚点阵列下的定位方法,并提出基于Delaunay三角剖分锚点分布优化模型和求解方法.     

Adaptive signal enhancement for overlapped peaks based on weighting factor selection

Peak-sharpening is an effective method for the peak position detection of overlapped spectra. However, the weighing factor parameter strongly affects the sharpening performance, and the derivative adopted in the peak-sharpening method is sensitive to noise. In this paper, an adaptive peak-sharpening method based on weighting factor selection is proposed. The relationship between the sharpening ratio and weighting factor is studied. In addition, the Savitzky–Golay filter is adopted due to its excellent noise reduction and peak shape retention abilities. First, the smoothed signal and second-order derivative signal are obtained by the Savitzky–Golay filter. Then, the parameters of the overlapped peaks are estimated for the weighting factor selection. Next, the peak position is detected by the peak-sharpening method. After that step, the estimated parameters are updated, and the above steps are iterated until the detection of the peak position converges. Finally, the converged results are considered to be the final detection results. The experimental results using a simulated dataset, a virtual mass spectra dataset and a polarography dataset show that the proposed method is effective for peak position detection.     

Label‐ and modification‐free‐based in situ selection of bovine serum albumin specific aptamer

Systematic evolution of ligands by exponential enrichment is a traditional approach to select aptamer, which has a great potential in biosensing field. However, chemical modifications of DNA library or targets before selection might block the real recognition and binding sites between aptamers and their targets. In this study, a label‐ and modification‐free‐based in situ selection strategy was developed to overcome this limitation. The strategy is an attempt to screen bovine serum albumin aptamers according to the principle of electrophoretic mobility shift assay, and allowed single‐stranded DNA sequence to be fully exposed to interact with bovine serum albumin which was mixed with the agarose gel beforehand. After eight rounds of selection, specific aptamer with low dissociation constant (K_d) value of 69.44 ± 7.60 nM was selected and used for subsequent establishment of fluorescence biosensor. After optimization, the optimal aptasensor exhibited a high sensitivity toward bovine serum albumin with a limit of detection of 0.24 ng/mL (linear range from 1 to 120 ng/mL). These results indicated that the label‐ and modification‐free‐based in situ selection strategy proposed in this work could effectively select specific aptamer to develop aptasensor for sensitive detection of bovine serum albumin or other targets in actual complicated samples.     

An Uzawa-type algorithm for the coupled Stokes equations

An Uzawa-type algorithm is designed for the coupled Stokes equations discretized by the mixed finite element method. The velocity solved by the presented algorithm is weakly divergence-free, which is different from the one solved by the common Uzawa method. Besides, an optimal relaxation parameter of the presented algorithm is provided.     

改进的PNC方法及其在非线性偏微分方程多解问题中的应用

2017年，李昭祥等提出了一种偏牛顿-校正法（Partial Newton-Correction Method，简记为PNC方法），并利用它成功地计算出了三类非线性偏微分方程的多重不稳定解.本文在PNC方法的基础上，提出并发展了一种改进的PNC方法.首先，利用Nehari流形$\mathcal{N}$与零平凡解的可分离性，建立并证明了$\mathcal{N}$的某特殊子流形$\mathcal{M}$上的全局分离定理及其推广（即局部分离定理）.全局分离定理只跟非线性偏微分算子或相应的非线性泛函本身有关，而与具体的计算方法无关.对一些典型的非线性偏微分方程多解问题（比如，Henon方程问题），该全局分离定理的分离条件，经验证是成立的.另一个方面，通过修改或补充原辅助变换的定义，去掉了原辅助变换的奇异性；接着建立并证明了某些非线性偏微分方程问题的新未知解与该非线性偏微分算子零核空间的密切关系；在证明中，去掉了在原奇异变换下所需的标准收敛（standard convergence）假设.最后，计算实例与数值结果验证了改进的PNC方法的可行性和有效性；同时表明子流形$\mathcal{M}$与已知解的可分离性是PNC方法和本文新方法能成功找到多解的关键.     

A class imbalance-aware Relief algorithm for the classification of tumors using microarray gene expression data

DNA microarray data has been widely used in cancer research due to the significant advantage helped to successfully distinguish between tumor classes. However, typical gene expression data usually presents a high-dimensional imbalanced characteristic, which poses severe challenge for traditional machine learning methods to construct a robust classifier performing well on both the minority and majority classes. As one of the most successful feature weighting techniques, Relief is considered to particularly suit to handle high-dimensional problems. Unfortunately, almost all relief-based methods have not taken the class imbalance distribution into account. This study identifies that existing Relief-based algorithms may underestimate the features with the discernibility ability of minority classes, and ignore the distribution characteristic of minority class samples. As a result, an additional bias towards being classified into the majority classes can be introduced. To this end, a new method, named imRelief, is proposed for efficiently handling high-dimensional imbalanced gene expression data. imRelief can correct the bias towards to the majority classes, and consider the scattered distributional characteristic of minority class samples in the process of estimating feature weights. This way, imRelief has the ability to reward the features which perform well at separating the minority classes from other classes. Experiments on four microarray gene expression data sets demonstrate the effectiveness of imRelief in both feature weighting and feature subset selection applications.