Characterizing and Predicting Resource Demand by Periodicity Mining

We present algorithms for characterizing the demand behavior of applications and predicting demand by mining periodicities in historical data. Our algorithms are change-adaptive, automatically adjusting to new regularities in demand patterns while maintaining low algorithm running time. They are intended for applications in scientific computing clusters, enterprise data centers, and Grid and Utility environments that exhibit periodical behavior and may benefit significantly from automation. A case study incorporating data from an enterprise data center is used to evaluate the effectiveness of our technique.Artur Andrzejak received the PhD degree in computer science from the Swiss Federal Institute of Technology (ETH Zurich) in 2000. He is currently a researcher at Zuse-Institute Berlin, Germany. He was a postdoctoral researcher at the Hewlett-Packard Labs in Palo Alto, California, from 2001 to 2002. His research interests include systems management and modeling, and Grids.Mehmet Ceyran is working toward his Masters Degree in Computer Science at the Freie Universität Berlin, Germany. He has been employed as a student programmer at Zuse-Institute Berlin since 2003. His research interests include software engineering, systems management, and artificial intelligence.     

Synthesis,Biological Activity,and Molecular Dynamics Study of Novel Series of a Trimethoprim Analogs as Multi-Targeted Compounds: Dihydrofolate Reductase (DHFR) Inhibitors and DNA-Binding Agents

Eighteen previously undescribed trimethoprim (TMP) analogs containing amide bonds (1–18) were synthesized and compared with TMP, methotrexate (MTX), and netropsin (NT). These compounds were designed as potential minor groove binding agents (MGBAs) and inhibitors of human dihydrofolate reductase (hDHFR). The all-new derivatives were obtained via solid phase synthesis using 4-nitrophenyl Wang resin. Data from the ethidium displacement test confirmed their DNA-binding capacity. Compounds 13–14 (49.89% and 43.85%) and 17–18 (41.68% and 42.99%) showed a higher binding affinity to pBR322 plasmid than NT. The possibility of binding in a minor groove as well as determination of association constants were performed using calf thymus DNA, T4 coliphage DNA, poly (dA-dT)², and poly (dG-dC)². With the exception of compounds 9 (IC50 = 56.05 µM) and 11 (IC50 = 55.32 µM), all of the compounds showed better inhibitory properties against hDHFR than standard, which confirms that the addition of the amide bond into the TMP structures increases affinity towards hDHFR. Derivatives 2, 6, 13, 14, and 16 were found to be the most potent hDHFR inhibitors. This molecular modelling study shows that they interact strongly with a catalytically important residue Glu-30.     

Shape‐Dependent Interactions of Palladium Nanocrystals with Hydrogen

Elucidation of the nature of hydrogen interactions with palladium nanoparticles is expected to play an important role in the development of new catalysts and hydrogen‐storage nanomaterials. A facile scaled‐up synthesis of uniformly sized single‐crystalline palladium nanoparticles with various shapes, including regular nanocubes, nanocubes with protruded edges, rhombic dodecahedra, and branched nanoparticles, all stabilized with a mesoporous silica shell is developed. Interaction of hydrogen with these nanoparticles is studied by using temperature‐programmed desorption technique and by performing density functional theory modeling. It is found that due to favorable arrangement of Pd atoms on their surface, rhombic dodecahedral palladium nanoparticles enclosed by {110} planes release a larger volume of hydrogen and have a lower desorption energy than palladium nanocubes and branched nanoparticles. These results underline the important role of {110} surfaces in palladium nanoparticles in their interaction with hydrogen. This work provides insight into the mechanism of catalysis of hydrogenation/dehydrogenation reactions by palladium nanoparticles with different shapes.     

Disaggregation Modelling of Annual Flows into Daily Streamflows Using a New Approach of the Method of Fragments

For many decades, synthetic streamflow series have been utilized in hydrology to analyze numerous stochastic problems whose solutions depend on the values of the streamflows and their temporal pattern. The stochastic generation of synthetic streamflows at a given time level can adopt two general approaches: the generation at the required time level by applying an appropriate model; or the generation of annual flows using a suitable annual model, followed by their disaggregation into flows at the required time level. The first approach is feasible for a seasonal or monthly level, but not for a daily level, while the latter can be applied to any level. It also has the advantage of allowing the preservation of the historical statistical properties at both the upper (year) and the lower (season, month or day) time levels. One of the simplest disaggregation models is the method of fragments. Based on the extensive application of that method to the generation of monthly flow series in more than 50 Portuguese river gauges (Silva and Portela, 2011, Hydrol Sci J 57(5): 942–955. doi:  10.1080/02626667.2012.686695, 2012), it was possible to establish a deterministic criterion to define the classes of fragments and to select the fragments that proved to be very robust. That criterion was revisited and modified and applied to the generation of synthetic daily flow series, with even better results. This paper describes the revisited method, presents the results from its application to a few case studies and discusses its relevance to analyze the uncertainty due to the temporal variability of the flow regime.     

Semantic ambient media—an introduction

The medium is the message! And the message was literacy, media democracy and music charts. Mostly one single distinguishable medium such as TV, the Web, the radio, or books transmitted the message. Now in the age of ubiquitous and pervasive computing, where information flows through a plethora of distributed interlinked media—what is the message ambient media will tell us? What does semantic mean in this context? Which experiences will it open to us? What is content in the age of ambient media? Ambient media are embedded throughout the natural environment of the consumer—in his home, in his car, in restaurants, and on his mobile device. Predominant sample services are smart wallpapers in homes, location based services, RFID based entertainment services for children, or intelligent homes. The goal of this article is to define semantic ambient media and discuss the contributions to the Semantic Ambient Media Experience (SAME) workshop, which was held in conjunction with the ACM Multimedia conference in Vancouver in 2008. The results of the workshop can be found on: www.ambientmediaassociation.org.     

Birnbaum–Saunders nonlinear regression models

We introduce, for the first time, a new class of Birnbaum–Saunders nonlinear regression models potentially useful in lifetime data analysis. The class generalizes the regression model described by Rieck and Nedelman [Rieck, J.R., Nedelman, J.R., 1991. A log-linear model for the Birnbaum–Saunders distribution. Technometrics 33, 51–60]. We discuss maximum-likelihood estimation for the parameters of the model, and derive closed-form expressions for the second-order biases of these estimates. Our formulae are easily computed as ordinary linear regressions and are then used to define bias corrected maximum-likelihood estimates. Some simulation results show that the bias correction scheme yields nearly unbiased estimates without increasing the mean squared errors. Two empirical applications are analysed and discussed.     

MaZda--a software package for image texture analysis

MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures. However, it revealed its effectiveness in analysis of other types of textured images, including X-ray and camera images. The software was utilized by numerous researchers in diverse applications. It was proven to be an efficient and reliable tool for quantitative image analysis, even in more accurate and objective medical diagnosis. MaZda was also successfully used in food industry to assess food product quality. MaZda can be downloaded for public use from the Institute of Electronics, Technical University of Lodz webpage.     

Graphene-Based Cementitious Composites: Toward Next-Generation Construction Technologies

The search for technological solutions to the ever-increasing demand for ultra-high-quality concrete with the simultaneous construction boom represents one of the greatest challenges concrete researchers are facing nowadays. In view of their unique properties, graphene and related materials, when utilized to form graphene-based cementitious composites, appear to be powerful components to give a boost to today's concrete technology. In this review, the most enlightening recent advancements in the development of fabrication protocols for obtaining the homogenous dispersion of graphene and derivatives thereof within the cement matrix are showcased. The hydration process and basic properties of graphene-based cementitious materials are also discussed. The integration of graphene-family materials to concrete technology allows new functions to be imparted to cement composites toward the construction of smart and multifunctional buildings. Therefore, a specific focus is given to the electrical and piezoresistive behavior of graphene-cement composites, and ultimately their great potential for structural health monitoring applications. The approaches proposed in this review can be also extended to other 2D materials offering the broadest arsenal of physical properties, which can therefore be integrated on-demand in future smart structures and constructions.