Magnetization driven metal - insulator transition in strongly disordered Ge:Mn magnetic semiconductors

We report on the temperature and field driven metal-insulator transition in disordered Ge:Mn magnetic semiconductors accompanied by magnetic ordering, magnetoresistance reaching thousands of percents and suppression of the extraordinary Hall effect by a magnetic field. Magnetoresistance isotherms are shown to obey a universal scaling law with a single scaling parameter depending on temperature and fabrication. We argue that the strong magnetic disorder leads to localization of charge carriers and is the origin of the unusual properties of Ge:Mn alloys.Comment: 10 pages, 5 figure

A local field emission study of partially aligned carbon-nanotubes by AFM probe

We report on the application of Atomic Force Microscopy (AFM) for studying the Field Emission (FE) properties of a dense array of long and vertically quasi-aligned multi-walled carbon nanotubes grown by catalytic Chemical Vapor Deposition on a silicon substrate. The use of nanometric probes enables local field emission measurements allowing investigation of effects non detectable with a conventional parallel plate setup, where the emission current is averaged on a large sample area. The micrometric inter-electrode distance let achieve high electric fields with a modest voltage source. Those features allowed us to characterize field emission for macroscopic electric fields up to 250 V/$\mu$m and attain current densities larger than 10$^5$ A/cm$^2$. FE behaviour is analyzed in the framework of the Fowler-Nordheim theory. A field enhancement factor $\gamma \approx$ 40-50 and a turn-on field $E_{turn-on} \sim$15 V/$\mu$m at an inter-electrode distance of 1 $\mu$m are estimated. Current saturation observed at high voltages in the I-V characteristics is explained in terms of a series resistance of the order of M$\Omega$. Additional effects as electrical conditioning, CNT degradation, response to laser irradiation and time stability are investigated and discussed

Generalized Nash equilibria for SaaS/PaaS Clouds

Cloud computing is an emerging technology that allows to access computing resources on a pay-per-use basis. The main challenges in this area are the efficient performance management and the energy costs minimization. In this paper we model the service provisioning problem of Cloud Platform-as-a-Service systems as a Generalized Nash Equilibrium Problem and show that a potential function for the game exists. Moreover, we prove that the social optimum problem is convex and we derive some properties of social optima from the corresponding Karush-Kuhn-Tucker system. Next, we propose a distributed solution algorithm based on the best response dynamics and we prove its convergence to generalized Nash equilibria. Finally, we numerically evaluate equilibria in terms of their efficiency with respect to the social optimum of the Cloud by varying our algorithm initial solution. Numerical results show that our algorithm is scalable and very efficient and thus can be adopted for the run-time management of very large scale systems

Magneto-optical characterization of MnxGe1-x alloys obtained by ion implantation

Magneto-optical Kerr effect hysteresis loops at various wavelengths in the visible/near-infrared range have been used to characterize the magnetic properties of alloys obtained by implanting Mn ions at fixed energy in a Ge matrix. The details of the hysteresis loops reveal the presence of multiple magnetic contributions. They may be attributed to the inhomogeneous distribution of the magnetic atoms and, in particular, to the known coexistence of diluted Mn in the Ge matrix and metallic Mn-rich nanoparticles embedded in it [Phys. Rev. B 73, 195207(2006)].Comment: 2 pages, 2 figures. Proceeding of the International Conference on Magnetism. Kyoto, August 20-25 200

Field emission from single multi-wall carbon nanotubes

Electron field emission characteristics of individual multiwalled carbon nanotubes have been investigated by a piezoelectric nanomanipulation system operating inside a scanning electron microscopy chamber. The experimental setup ensures a high control capability on the geometric parameters of the field emission system (CNT length, diameter and anode-cathode distance). For several multiwalled carbon nanotubes, reproducible and quite stable emission current behaviour has been obtained with a dependence on the applied voltage well described by a series resistance modified Fowler-Nordheim model. A turn-on field of about 30 V/um and a field enhancement factor of around 100 at a cathode-anode distance of the order of 1 um have been evaluated. Finally, the effect of selective electron beam irradiation on the nanotube field emission capabilities has been extensively investigated.Comment: 16 pages, 5 figure

Optimal Map Reduce Job Capacity Allocation in Cloud Systems.

We are entering a Big Data world. Many sectors of our economy are now guided by data-driven decision processes. Big Data and business intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions for allocating on demand large clusters. Capacity allocation in such systems is a key challenge to provide performance for MapReduce jobs and minimize cloud resource costs. The contribution of this paper is twofold: (i) we provide new upper and lower bounds for MapReduce job execution time in shared Hadoop clusters, (ii) we formulate a linear programming model able to minimize cloud resources costs and job rejection penalties for the execution of jobs of multiple classes with (soft) deadline guarantees. Simulation results show how the execution time of MapReduce jobs falls within 14% of our upper bound on average. Moreover, numerical analyses demonstrate that our method is able to determine the global optimal solution of the linear problem for systems including up to 1,000 user classes in less than 0.5 seconds

Existence and solution methods for equilibria

Equilibrium problems provide a mathematical framework which includes optimization, variational inequalities, fixed-point and saddle point problems, and noncooperative games as particular cases. This general format received an increasing interest in the last decade mainly because many theoretical and algorithmic results developed for one of these models can be often extended to the others through the unifying language provided by this common format. This survey paper aims at covering the main results concerning the existence of equilibria and the solution methods for finding them

An optimization framework for the capacity allocation and admission control of MapReduce jobs in cloud systems

Nowadays, we live in a Big Data world and many sectors of our economy are guided by data-driven decision processes. Big Data and Business Intelligence applications are facilitated by the MapReduce programming model, while, at infrastructural layer, cloud computing provides flexible and cost-effective solutions to provide on-demand large clusters. Capacity allocation in such systems, meant as the problem of providing computational power to support concurrent MapReduce applications in a cost-effective fashion, represents a challenge of paramount importance. In this paper we lay the foundation for a solution implementing admission control and capacity allocation for MapReduce jobs with a priori deadline guarantees. In particular, shared Hadoop 2.x clusters supporting batch and/or interactive jobs are targeted. We formulate a linear programming model able to minimize cloud resources costs and rejection penalties for the execution of jobs belonging to multiple classes with deadline guarantees. Scalability analyses demonstrated that the proposed method is able to determine the global optimal solution of the linear problem for systems including up to 10,000 classes in less than 1 s

Local probing of the field emission stability of vertically aligned multiwalled carbon nanotubes

Metallic cantilever in high vacuum atomic force microscope has been used as anode for field emission experiments from densely packed vertically aligned multi-walled carbon nanotubes. The high spatial resolution provided by the scanning probe technique allowed precise setting of the tip-sample distance in the submicron region. The dimension of the probe (curvature radius below 50nm) allowed to measure current contribution from sample areas smaller than 1um^2. The study of long-term stability evidenced that on these small areas the field emission current remains stable (within 10% fluctuations) several hours (at least up to 72 hours) at current intensities between 10-5A and 10-8A. Improvement of the current stability has been observed after performing long-time Joule heating conditioning to completely remove possible adsorbates on the nanotubes.Comment: 15 pages, 7 figure

Synthesis of hydrophilic carbon nanotube sponge via post-growth thermal treatment

Clean water is vital for healthy ecosystems, for human life and, in a broader sense, it is directly linked to our socio-economic development. Nevertheless, climate change, pollution and increasing world population will likely make clean water scarcer in the near future. Consequently, it becomes imperative to develop novel materials and more efficient ways of treating waste and contaminated water. Carbon nanotube (CNT) sponges, for example, are excellent in removing oleophilic contaminants; however, due to their super-hydrophobic nature, they are not as efficient when it comes to absorbing water-soluble substances. Here, by means of a scalable method consisting of simply treating CNT sponges at mild temperatures in air, we attach oxygen-containing functional groups to the CNT surface. The functionalized sponge becomes hydrophilic while preserving its micro- and macro-structure and can therefore be used to successfully remove toxic contaminants, such as pesticides, that are dissolved in water. This discovery expands the current range of applications of CNT sponges to those fields in which a hydrophilic character of the sponge is more suitable