An Analysis of Public Clouds Elasticity in the Execution of Scientific Applications: a Survey

Elasticity can be seen as the ability of a system to increase or decrease the computing resources allocated in a dynamic and on demand way. It is an important feature provided by cloud computing, that has been widely used in web applications and is also gaining attention in the scientific community. Considering the possibilities of using elasticity in this context, a question arises: “Are the available public cloud solutions suitable to provide elasticity to scientific applications?” To answer the question, in a first moment we present a survey on the use of cloud computing in scientific scenarios, providing an overview of the subject. Next, we describe the elasticity mechanisms offered by major public cloud providers and analyzes the limitations of the solutions in providing elasticity for scientific applications. As the main contribution of the article, we also present an analysis over some initiatives that are being developed to overcome the current challenges. In our opinion, current computational clouds are developing rapidly but have not yet reached the necessary maturity level to meet all scientific applications elasticity requirements. We expect that in the coming years the efforts being taken by numerous researchers in this area identify and address these challenges and lead to better and more mature technologies that will improve cloud computing practices.     

Increasing the Level of Automation in the Forestry Logging Process with Crane Trajectory Planning and Control

Working with forestry machines requires a great deal of training to be sufficiently skilled to operate forestry cranes. In view of this, it would be desirable within the forestry industry to introduce automated motions, such as those seen in robotic arms, to shorten the training time and make the work of the operator easier. Motivated by this fact, we have developed two experimental platforms for testing control systems and motion‐planning algorithms in real time. They correspond to a laboratory setup and a commercial version of a hydraulic manipulator used in forwarder machines. The aim of this article is to present the results of this development by providing an overview of our trajectory‐planning algorithm and motion‐control method, with a subsequent view of the experimental results. For motion control, we design feedback controllers that are able to track reference trajectories based on sensor measurements. Likewise, we provide arguments to design controllers in an open‐loop for machines that lack sensing devices. Relying on the tracking efficiency of these controllers, we design time‐efficient reference trajectories of motions that correspond to logging tasks. To demonstrate performance, we provide an overview of extensive testing done on these machines.     

Low-Oxidation State Indium-Catalyzed C-C Bond Formation

The development of innovative metal catalysis for selective bond formation is an important task in organic chemistry. The group 13 metal indium is appealing for catalysis because indium-based reagents are minimally toxic, selective, and tolerant toward various functional groups. Among elements in this group, the most stable oxidation state is typically +3, but in molecules with larger group 13 atoms, the chemistry of the +1 oxidation state is also important. The use of indium(III) compounds in organic synthesis has been well-established as Lewis acid catalysts including asymmetric versions thereof. In contrast, only sporadic examples of the use of indium(I) as a stoichiometric reagent have been reported: to the best of our knowledge, our investigations represent the first synthetic method that uses a catalytic amount of indium(I). Depending on the nature of the ligand or the counteranion to which it is coordinated, indium(I) can act as both a Lewis acid and a Lewis base because it has both vacant p orbitals and a lone pair of electrons. This potential ambiphilicity may offer unique reactivity and unusual selectivity in synthesis and may have significant implications for catalysis, particularly for dual catalytic processes. We envisioned that indium(I) could be employed as a metallic Lewis base catalyst to activate Lewis acidic boron-based pronucleophiles for selective bond formation with suitable electrophiles. Alternatively, indium(I) could serve as an ambiphilic catalyst that activates both reagents at a single center. In this Account, we describe the development of low-oxidation state indium catalysts for carbon-carbon bond formation between boron-based pronucleophiles and various electrophiles. We discovered that indium(I) iodide was an excellent catalyst for α-selective allylations of C(sp(2)) electrophiles such as ketones and hydrazones. Using a combination of this low-oxidation state indium compound and a chiral semicorrin ligand, we developed catalytic highly enantioselective allylation, crotylation, and α-chloroallylation reactions of hydrazones. These transformations proceeded with rare constitutional selectivities and remarkable diastereoselectivities. Furthermore, indium(I) triflate served as the most effective catalyst for allylations and propargylations of C(sp(3)) electrophiles such as O,O-acetals, N,O-aminals, and ethers, and we applied this methodology to carbohydrate chemistry. In addition, a catalyst system composed of indium(I) chloride and a chiral silver BINOL-phosphate facilitated the highly enantioselective allylation and allenylation of N,O-aminals. Overall, these discoveries demonstrate the versatility, efficiency, and sensitivity of low-oxidation state indium catalysts in organic synthesis.     

Closed‐Loop Transmit Diversity Techniques for Small Wireless Terminals and Their Performance Assessment in a Flat Fading Channel

Closed‐loop transmit diversity is considered an important technique for improving the link budget in the third generation and future wireless communication standards. This paper proposes several transmit diversity algorithms suitable for small wireless terminals and presents performance assessment in terms of average signal‐to‐noise ratio (SNR) and outage improvement, convergence, and complexity of operations. The algorithms presented herein are verified using data from measured indoor channels with variable antenna spacing and the results explained using measured radiation patterns for a two‐element array. It is shown that for a two‐element array, the best among the proposed techniques provides SNR improvement of about 3 dB in a tightly spaced array (inter‐element spacing of 0.1 wavelength at 2 GHz) typical of small wireless devices. Additionally, these techniques are shown to perform significantly better than a single antenna device in an indoor channel considering realistic values of latency and propagation errors.     

Modelling of orthogonal cutting processes with the method of smoothed particle hydrodynamics

In the last decades, mesh-free methods for simulating various cutting processes have been used very widely as they can eliminate numerical problems in the simulation of material failure and large plastic deformations. This paper deals with the results from modelling the orthogonal cutting of AISI 1045 steel using smoothed particle hydrodynamics (SPH) method. Moreover, it is determined how the parameters of the SPH solver such as initial smoothing length, initial particle density and coefficient for the timestep increase affect the prediction error for the values of cutting force and chip compression ratio as well as computing time. The optimum values of the SPH solver parameters are determined by minimising an objective function. The best balance between the prediction error of machining variables and computing time is achieved for an initial particle density of 40 μm and a coefficient for the timestep increase of 0.4.     

Block adaptive kernel principal component analysis for nonlinear process monitoring

On‐line modeling of multivariate nonlinear system based on multivariate statistical methods has been studied extensively due to its industrial requirements. In order to further improve the modeling efficiency, a fast Block Adaptive Kernel Principal Component Analysis algorithm is proposed. Comparing with the existing work, the proposed algorithm (1) does not rely on iterative computation in the calculating process, (2) combines the up‐ and downdating operations to become a single one (3) and describes the adaptation of the Gram matrix as a series of rank‐1 modification. In addition, (4) the updation of the eigenvalues and eigenvectors is of and high‐precision. The computational complexity analysis and the numerical study show that the derived strategy possesses better ability to model the time‐varying nonlinear variable interrelationships in process monitoring. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4334–4345, 2016     

Melt spun matrix fibers of toughened polypropylene copolymers modified by high energy electrons

Binary blends of polypropylene (PP) and ethylene‐octene copolymer (EOC) are prepared by continuous electron‐induced reactive processing at various mass ratios of the blend components and various doses without adding of any grafting agents. The influence of mass ratio and dose is investigated in order to get the optimum processing behavior of toughened PP as well as optimum properties of resulting fibers. It is found that toughened PP with a PP/EOC blend ratio of 97.5–2.5  mass % can be used advantageously as a matrix component for the process of online spinning of glass fiber/toughened PP hybrid yarns. Such hybrid yarns belong to one of the most advanced production methods for the manufacturing of fiber reinforced thermoplastic composites with an increased mechanical performance. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44011.