A lightweight cell switching and traffic offloading scheme for energy optimization in ultra-dense heterogeneous networks

One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in a tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network, but most of the proposed methods are computationally demanding, which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that THESIS significantly reduces the energy consumption of the UDHN and can reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.     

Similar,or dissimilar,that is the question. How different are methods for comparison of compounds similarity?

Comparison of compounds similarity is one of the main strategies of virtual screening protocols. Both similarity and dissimilarity concepts are of great importance during the search for new active compounds. Similarity is important due to the assumption that underlies the process of searching for new drug candidates: structurally similar compounds should induce similar biological response. On the other hand, we are also interested in dissimilarity, as we usually aim to find structurally novel ligands. In the study, we compared several approaches of evaluating compound similarity. Various representations and metrics were applied and we indicated the rate of variation of the results that can occur when shifting from one strategy to another. We compared both general similarity of datasets using different approaches, as well as examined the changes in the set of nearest neighbors when changing one compound representation into another, and the influence of representation/metric settings on the clustering outcome. We hope that the study will be of great help during the preparation of virtual screening experiments, stressing the need for careful selection of the way, the compound similarity is assessed. The differences in the results that can be obtained via the application of particular strategy can significantly influence the outcome of comparison studies; therefore, its settings should be carefully selected beforerunning the comparison.     

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods

BackgroundIdentification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction.ResultsIn this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions.ConclusionsCompared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.     

Magnetic properties of lithium intercalation compounds

Magnetic experiments are powerful tools to study fundamental properties and to check the qualities of samples. Temperature, stress, and impurities of materials can all affect magnetic properties and play an important role in the utilization of these materials for engineering applications. The estimation and analysis of the spontaneous magnetization can reveal ferromagnetic particles as impurities in samples. The shape of the temperature dependence of magnetization is indicative of the origin of the magnetic properties. However, it is necessary to correlate the χ _m (T) curves and isothermal M(H) plots to achieve a complete analysis of the electronic properties of the materials. Highlights of magnetic properties of lithium intercalation compounds are briefly described. Intrinsic and extrinsic properties are considered as useful parameters to determine the purity of electrode materials for rechargeable Li-ion batteries.     

Granulocyte luminescence curves: Is anything there?

This problem (proposed for the 4th International Symposium on Applied Stochastic Models and Data Analysis) concerns an exploratory study related to the presently accepted hypothesis that temporal curves of the forced pulse luminescence of specialized blood cells, granulocytes, may indicate the level of effectiveness of the immunological system. This paper presents a new set of empirical data and an analysis of its structure.     

FCM-FastICA的点蚀声发射信号分离识别方法*

金属点蚀是一种破坏性和隐患较大的设备损伤形式。点蚀会产生声发射信号。点蚀过程中产生的多种声源类型会造成信号混叠,影响腐蚀进程的判断。针对点蚀信号混叠问题,提出一种模糊C均值聚类与快速独立分量分析算法相结合的点蚀信号分离识别方法。通过分析单、双点蚀声发射数据将点蚀分为钝化膜破裂阶段、点蚀诱导成核及发展阶段,由聚类确定信号类别并用快速独立分量分析分离混合信号,利用相关性函数验证分离效果。结果表明：单点蚀过程存在3类原信号,双点蚀过程存在7类信号,其中包含单个信号与混合信号;单个信号与原信号相关性极高,达到0.8以上,混合信号的分离分量与原信号相关性达到0.6以上,分离效果较好。该方法可对点蚀混合信号进行有效分离和识别,为腐蚀进程判断提供支持。     

基于聚类分区的风电场无功电压优化控制策略

随着风电渗透率的不断增大,并网风电场的机端电压稳定面临巨大挑战。研究了风电场内部各节点的无功电压特性,提出一种基于机组有功出力聚类分区的无功电压优化控制策略：通过自动电压控制系统获取风电场当前的电压指标与实际运行数据,计算出风电场的无功需求;以历史有功出力数据对场内机组进行模糊C聚类分区;利用改进的粒子群算法寻找最优无功分配方案,对每一区域进行无功电压控制。在MATLAB上进行仿真,与传统根据各机组无功容量进行分配的方案进行对比,仿真结果表明,所提控制策略能够有效改善风电场并网点电压控制性能,减小风电场内机组机端电压的波动。     

中药注射剂荧光光谱三维聚类图分析研究

测定了5种56个批次的中药注射剂的二维荧光光谱,提出了激发光谱的第二特征峰的概念,首次以激发波长、发射波长和激发光谱的第二特征峰波长作为基,建立了三维聚类图分析法.结果表明:激发光谱的第二特征峰波长具有较好的特征性;三维聚类图有较好的分类和预测作用,并有较强的可视性.该法可用于中药注射剂的鉴别和聚类分析.     

面向LAMOST的天体光谱离群数据挖掘系统研究

在宇宙中寻求未知天体是人类探索宇宙奥妙所追求的目标之一,离群数据挖掘是发现未知天体光谱数据的一种有效途径。文章首先以VC++和Oracle9i为开发工具,设计与实现了面向LAMOST的恒星光谱离群数据挖掘系统,并给出了其软件体系结构和模块功能。其次,对基于中值滤波器的恒星光谱数据预处理、基于距离的恒星光谱数据聚类、基于距离支持度的恒星光谱数据离群数据挖掘、基于主分量分析法PCA的恒星光谱数据离群数据的三维可视化等主要关键技术进行了详细描述。最后,基于SDSS恒星光谱数据的运行结果表明,利用该系统寻找天体光谱离群数据是可行的,从而为寻找未知的、特殊的天体光谱数据提供了一种新途径。     

基于光谱技术的桔子汁品种鉴别方法的研究

为了实现桔子汁不同品种的快速光谱鉴别,首先采用主成分分析法对光谱数据进行聚类分析,从定性分析的角度得到四种不同品种桔子汁的特征差异。同时将小波变换用于对大量光谱数据的压缩, 并结合RBF神经网络建立桔子汁品种鉴别的定量分析模型。该模型将小波压缩后的数据作为神经网络的输入向量,建立径向基函数RBF神经网络。4个品种共240个样本用来建立RBF神经网络的训练模型,剩余的60个样本用于预测。预测结果表明,小波变换结合RBF神经网络的桔子汁品种鉴别的准确率达到100%。说明文章提出的基于光谱技术的鉴别方法具有很好的分类能力,它为桔子汁品种的快速鉴别提供了一种新方法。