Virtualized Web server cluster self-configuration to optimize resource and power use

This work proposes a reusable architecture that enables the self-configuration of a supporting infrastructure for Web server clusters using virtual machines. The goal of the architecture is to ensure service quality, evaluating how broadly it complies with the application's operating restrictions and proportionally acting on the configuration of physical servers (hosts) or virtual machines. In addition, through the rational use of resources, the proposal aims at saving energy. A prototype of the architecture was developed and a performance evaluation carried out with two different resource management approaches. This evaluation shows how fully functional and advantageous the proposal is in terms of using resources, avoiding waste, yet maintaining the application's quality of service within acceptable levels. The architecture also shows to be flexible enough to accept, with a reasonable amount of effort, different resource self-configuration policies.     

Sensitive and Reversible Detection of Methanol and Water Vapor by In Situ Electrochemically Grown CuBTC MOFs on Interdigitated Electrodes

The in situ electrochemical growth of Cu benzene‐1,3,5‐tricarboxylate (CuBTC) metal–organic frameworks, as an affinity layer, directly on custom‐fabricated Cu interdigitated electrodes (IDEs) is described, acting as a transducer. Crystalline 5–7 µm thick CuBTC layers are grown on IDEs consisting of 100 electrodes with a width and a gap of both 50 µm and a height of 6–8 µm. These capacitive sensors are exposed to methanol and water vapor at 30 °C. The affinities show to be completely reversible with higher affinity toward water compared to methanol. For exposure to 1000 ppm methanol, a fast response is observed with a capacitance change of 5.57 pF at equilibrium. The capacitance increases in time followed diffusion‐controlled kinetics (k = 2.9 mmol s^?0.5 g^?1_CuBTC). The observed capacitance change with methanol concentration follows a Langmuir adsorption isotherm, with a value for the equilibrium affinity K _e = 174.8 bar^?1. A volume fraction f _MeOH = 0.038 is occupied upon exposure to 1000 ppm of methanol. The thin CuBTC affinity layer on the Cu‐IDEs shows fast, reversible, and sensitive responses to methanol and water vapor, enabling quantitative detection in the range of 100–8000 ppm.     

Faster to Sound Parts by Advanced Forming Process Simulation ‐ Advanced Forming Process Model Including the Elastic Effects of the Forming Press and Tool

This article addresses process, stamping, and manufacturing engineers, as well as tool designers (prototype and series production tools), and press shop planners in the range of metal forming. The paper deals with methods of modelling and simulating the metal forming process and their application in product design, production, and forming process planning. In models usually applied major effects on the forming process are neglected. For instance, the elastic behaviour of presses and die tools is not considered in process and tool planning. Thus, reworking of tools is a consequence of this model oversimplification. The paper illustrates how interactions between forming press, die tool and metal forming processes can be modelled by enhancing conventional FE models. Several examples demonstrate the information value of the Advanced Forming Process Model (AFPM).     

A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming

This paper evaluates different forms of rank-based selection that are used with genetic algorithms and genetic programming. Many types of rank based selection have exactly the same expected value in terms of the sampling rate allocated to each member of the population. However, the variance associated with that sampling rate can vary depending on how selection is implemented. We examine two forms of tournament selection and compare these to linear rank-based selection using an explicit formula. Because selective pressure has a direct impact on population diversity, we also examine the interaction between selective pressure and different mutation strategies.     

Face Editing Using Part-Based Optimization of the Latent Space

We propose an approach for interactive 3D face editing based on deep generative models. Most of the current face modeling methods rely on linear methods and cannot express complex and non-linear deformations. In contrast to 3D morphable face models based on Principal Component Analysis (PCA), we introduce a novel architecture based on variational autoencoders. Our architecture has multiple encoders (one for each part of the face, such as the nose and mouth) which feed a single decoder. As a result, each sub-vector of the latent vector represents one part. We train our model with a novel loss function that further disentangles the space based on different parts of the face. The output of the network is a whole 3D face. Hence, unlike part-based PCA methods, our model learns to merge the parts intrinsically and does not require an additional merging process. To achieve interactive face modeling, we optimize for the latent variables given vertex positional constraints provided by a user. To avoid unwanted global changes elsewhere on the face, we only optimize the subset of the latent vector that corresponds to the part of the face being modified. Our editing optimization converges in less than a second. Our results show that the proposed approach supports a broader range of editing constraints and generates more realistic 3D faces.     

Specifying Real-Time Finite-State Systems in Linear Logic (Extended Abstract)

Abstract

Real-time finite-state systems may be specified in linear logic by means of linear implications between conjunctions of fixed finite length. In this setting, where time is treated as a dense linear ordering, safety properties may be expressed as certain provability problems. These provability problems are shown to be in pspace. They are solvable, with some guidance, by finite proof search in concurrent logic programming environments based on linear logic and acting as sort of model-checkers. One advantage of our approach is that either it provides unsafe runs or it actually establishes safety.     

IMPROVEMENT OF THE BITTERNESS OF HOPS: PHOTOREACTIONS OF ALPHA ACIDS

The phototransformation of the α-acids (humulone) of hop resins has been studied in order to improve their bitter properties and to establish the optimal experimental conditions.     

A hybrid model for a multiproduct pipeline planning and scheduling problem

Brazilian petrobras is one of the world largest oil companies. Recurrently, it faces a very difficult planning and scheduling problem: how to operate a large pipeline network in order to adequately transport oil derivatives and biofuels from refineries to local markets. In spite of being more economical and environmentally safer, the use of a complex pipeline network poses serious operational difficulties related to resource allocation and temporal constraints. The current approaches known from the literature only consider a few types of constraints and restricted topologies, hence they are far from being applicable to real instances from petrobras. We propose a hybrid framework based on a two-phase problem decomposition strategy. A novel Constraint Programming (CP) model plays a key role in modelling operational constraints that are usually overlooked in literature, but that are essential in order to guarantee viable solutions. The full strategy was implemented and produced very adequate results when tested over large real instances.