Energy efficiency in cloud computing based on mixture power spectral density prediction

Due to the budget and environmental issues, adaptive energy efficiency receives a lot of attention these days, especially for cloud computing. In the previous research, we developed a combined methodology based on nonparametric prediction and convex optimization to produce proactive energy efficiency-oriented solution. In this work, the predictive analysis was further enhanced by deriving the mixture power spectral density to model the complex cloud monitoring statistics. By engaging the improved technique to the predictive analysis, the prediction process was more adaptive to handle the fluctuation in system utilization. As a consequence, the optimization process could subsequently produce more appropriate setting for energy savings. After the infrastructure setting has been made available, the instruction of virtual machine migration was created and implemented by the cloud orchestrator. This instruction condensed the services into the pool of active facilities, satisfying the objective of power efficiency. Eventually, any physical machine out of the power configuration would be gradually terminated. Compared to our former method, the effectiveness of the proposed technique has been proven by cutting down 4.92% of energy consumption, while still maintaining a similar quality of services.

     

Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters

Neural Computing and Applications - Renewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient...     

Performance implications of internet channels in financial services: A comprehensive perspective

Advances in information technology (IT) have forced financial services firms to explore new organizational forms and deliver service innovation. Given the obvious differences in the business model in which the financial services sector provides online services, it is natural to ask whether the emergence of Internet channels leads to superior achievement. Using a sample of twenty-four Taiwanese publicly traded financial services firms from 1997 to 2003, this empirical study attempts to assess the Internet channel??s effect on firm performance by means of applying event study methodology and data envelopment analysis. Results show that the magnitudes of average abnormal returns are uniformly positive and increase the operating efficiency of firms following announcements via Internet channels. This work therefore concludes that Internet channels have positive influences on firm performance.     

CFAR processing with switching exponential smoothers for nonhomogeneous environments

Conventional constant false alarm rate (CFAR) methods use a fixed number of cells to estimate the background variance. For homogeneous environments, it is desirable to increase the number of cells, at the cost of increased computation and memory requirements, in order to improve the estimation performance. For nonhomogeneous environments, it is desirable to use less number of cells in order to reduce the number of false alarms around the clutter edges. In this work, we present a solution with two exponential smoothers (first order IIR filters) having different time-constants to leverage the conflicting requirements of homogeneous and nonhomogeneous environments. The system is designed to use the filter having the large time-constant in homogeneous environments and to promptly switch to the filter having the small time constant once a clutter edge is encountered. The main advantages of proposed Switching IIR CFAR method are computational simplicity, small memory requirement (in comparison to windowing based methods) and its good performance in homogeneous environments (due to the large time-constant smoother) and rapid adaptation to clutter edges (due to the small time-constant smoother).     

Exploring brain connectivity with two-dimensional neural maps

We introduce two-dimensional neural maps for exploring connectivity in the brain. For this, we create standard streamtube models from diffusion-weighted brain imaging data sets along with neural paths hierarchically projected into the plane. These planar neural maps combine desirable properties of low-dimensional representations, such as visual clarity and ease of tract-of-interest selection, with the anatomical familiarity of 3D brain models and planar sectional views. We distribute this type of visualization both in a traditional stand-alone interactive application and as a novel, lightweight web-accessible system. The web interface integrates precomputed neural-path representations into a geographical digital-maps framework with associated labels, metrics, statistics, and linkouts. Anecdotal and quantitative comparisons of the present method with a recently proposed 2D point representation suggest that our representation is more intuitive and easier to use and learn. Similarly, users are faster and more accurate in selecting bundles using the 2D path representation than the 2D point representation. Finally, expert feedback on the web interface suggests that it can be useful for collaboration as well as quick exploration of data.     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.     

Electrical properties of monoazacrown ether-substituted phthalocyanines

D.c. conductivities of polycrystalline monoazacrown ether-substituted phthalocyanines (M=2H, Ni, Zn, Pb, Cu) and diphthalocyanine (M=Lu) are measured as Au-MPc-Au sandwiches to be of the order 10^–10–10^–12 S m^–1. Chemical doping with oxidants (e.g. NOBF₄) and enhancing the stacking of planar phthalocyanine moieties through the formation of alkali metal adducts with sodium and potassium ions leads to increase in conductivity of the order 10¹–10². The low conductivity and the diamagneticity of the bis(phthalocyaninato)-lutetium can be ascribed to the lack of radical nature in LuH(Pc)₂. For the a.c. conductivities, lead and lutetium complexes form a group with higher conductivities and the rest show lower conductivity. The conduction activation energies calculated from Arrhenius plots exhibit the lowest value (0.40 eV) for the lutetium compound.Part of this work was presented at NATO-ASI on Semiconductor Materials and Processing Technologies, Erice, Sicily, 1–13 July 1991.     

Utilizing an Animal Model to Identify Brain Neurodegeneration-Related Biomarkers in Aging

Glycine N-methyltransferase (GNMT) regulates S-adenosylmethionine (SAMe), a methyl donor in methylation. Over-expressed SAMe may cause neurogenic capacity reduction and memory impairment. GNMT knockout mice (GNMT-KO) was applied as an experimental model to evaluate its effect on neurons. In this study, proteins from brain tissues were studied using proteomic approaches, Haemotoxylin and Eosin staining, immunohistochemistry, Western blotting, and ingenuity pathway analysis. The expression of Receptor-interacting protein 1(RIPK1) and Caspase 3 were up-regulated and activity-dependent neuroprotective protein (ADNP) was down-regulated in GNMT-KO mice regardless of the age. Besides, proteins related to neuropathology, such as excitatory amino acid transporter 2, calcium/calmodulin-dependent protein kinase type II subunit alpha, and Cu-Zn superoxide dismutase were found only in the group of aged wild-type mice; 4-aminobutyrate amino transferase, limbic system-associated membrane protein, sodium- and chloride-dependent GABA transporter 3 and ProSAAS were found only in the group of young GNMT-KO mice and are related to function of neurons; serum albumin and Rho GDP dissociation inhibitor 1 were found only in the group of aged GNMT-KO mice and are connected to neurodegenerative disorders. With proteomic analyses, a pathway involving Gonadotropin-releasing hormone (GnRH) signal was found to be associated with aging. The GnRH pathway could provide additional information on the mechanism of aging and non-aging related neurodegeneration, and these protein markers may be served in developing future therapeutic treatments to ameliorate aging and prevent diseases.     

脉压和脉压指数与糖尿病心肌病患者心脏结构和功能的相关性研究

探讨脉压及脉压指数与糖尿病心肌病(Diabetic cardiomyopathy,DCM)患者心脏结构及功能的相关性.选取规律服药且排除其他心血管疾病的2型糖尿病心肌病患者66例为研究对象,根据脉压将患者分为糖尿病心肌病正常脉压组(19例)和糖尿病心肌病高脉压组(45例),同时选取40例年龄等相匹配且24 h脉压正常的健康志愿者作为对照组.比较各组患者的血糖、血压和脉压水平,并对各组患者心脏结构和功能的心超参数进行分析.DCM组和对照组基线资料比较:DCM组pro-BNP、baP-WV、24 h收缩压、24 h脉压和糖化血红蛋白水平均高于对照组(P<0.05);DCM组和对照组心超参数比较:DCM组患者左心房内径、室间隔厚度、左心室后壁厚度和E/e′高于对照组(P<0.05),e′低于对照组(P<0.05);DCM正常脉压组和DCM高脉压组与对照组患者的脉压和心超参数比较:与对照组和DCM正常脉压组比较,DCM高脉压组患者24 h收缩压及24 h脉压显著增加(P<0.05),24 h舒张压显著降低(P<0.05),而空腹血糖较对照组显著升高(P<0.001).DCM正常脉压组空腹血糖、24 H收缩压及24 h舒张压高于对照组(P均<0.05),差异有统计学意义;DCM组患者的脉压及脉压指数与其心超参数相关性分析:DCM组患者脉压及脉压指数与室间隔厚度(r=0.40、0.38,P<0.01)、左心室后壁厚度(r=0.29、0.24,P<0.01)、E/e′(r=0.22、0.15,P<0.05)呈正相关,与e′(r=-0.44、-0.48,P<0.01)呈负相关.DCM患者相关因素的Logistic回归分析:对DCM患者有影响的变量进行单因素分析显示,脉压及脉压指数是DCM患者心脏重塑的独立危险因素.脉压和脉压指数与糖尿病心肌病患者心脏结构和舒张功能有相关性,是糖尿病心肌病患者心脏重构的独立危险因素.     

Ti(C,N)粉末制备技术的研究及进展

概括了国内外Ti(C,N)粉末的制备技术,特别系统地介绍了Ti(C,N)粉末的制备技术的最新进展,分析了这些制备技术的工艺过程和工艺特点,讨论了其存在的问题和发展趋势。