The reconstruction of the institutional system during the process of transition to the market economy

In the socio-economic system, the institutions represent a real structural frame on which the entire economic edifice is standing, inclusively the mechanism of functioning of the economy. The economic processes cannot take place in a normal way without an institutional system made up of a complex network of rules and organizations. Because the transition to the market economy also implies the edification of a new institutional system based on new principles and realities, one also imposes a theoretical and practical analysis of the institutions, of their transformation and adaptation to the new circumstances in order to make the normal economic life possible.institutional system, transition, reform, privatization

The Costs of the Sustainable Development at the Beginning of the 3rd Millennium

Presently the climate changes at a global level show, from the point of view of the effects that they generate a great importance. The real threat for the human survival is not the terrorism, but the climate changes determined by the global warmth. In this article starting from the definition of the concept sustainable development and of the theoretical preoccupations and practices from this field, we would like to approach some aspects related to the costs that the humankind has to bear at the beginning of this millennium in order to fight against the global warming. The different environment projects are very expensive and produce effects in time. Because, these projects were not treated as they should be, the humankind faces now a situation where important financial resources have to be used in order to solve the environment problems. In other words, one has to solve the environment problems from the past in order to produce in a competitive way for the future.sustainable development, Kyoto protocol, carbon credits

Principles and applications of CVD powder technology

Chemical vapor deposition (CVD) is an important technique for surface modification of powders through either grafting or deposition of films and coatings. The efficiency of this complex process primarily depends on appropriate contact between the reactive gas phase and the solid particles to be treated. Based on this requirement, the first part of this review focuses on the ways to ensure such contact and particularly on the formation of fluidized beds. Combination of constraints due to both fluidization and chemical vapor deposition leads to the definition of different types of reactors as an alternative to classical fluidized beds, such as spouted beds, circulating beds operating in turbulent and fast-transport regimes or vibro-fluidized beds. They operate under thermal but also plasma activation of the reactive gas and their design mainly depends on the type of powders to be treated. Modeling of both reactors and operating conditions is a valuable tool for understanding and optimizing these complex processes and materials. In the second part of the review, the state of the art on materials produced by fluidized bed chemical vapor deposition is presented. Beyond pioneering applications in the nuclear power industry, application domains, such as heterogeneous catalysis, microelectronics, photovoltaics and protection against wear, oxidation and heat are potentially concerned by processes involving chemical vapor deposition on powders. Moreover, simple and reduced cost FBCVD processes where the material to coat is immersed in the FB, allow the production of coatings for metals with different wear, oxidation and corrosion resistance. Finally, large-scale production of advanced nanomaterials is a promising area for the future extension and development of this technique

Optimizing for a Many-Core Architecture without Compromising Ease-of-Programming

Faced with nearly stagnant clock speed advances, chip manufacturers have turned to parallelism as the source for continuing performance improvements. But even though numerous parallel architectures have already been brought to market, a universally accepted methodology for programming them for general purpose applications has yet to emerge. Existing solutions tend to be hardware-specific, rendering them difficult to use for the majority of application programmers and domain experts, and not providing scalability guarantees for future generations of the hardware. This dissertation advances the validation of the following thesis: it is possible to develop efficient general-purpose programs for a many-core platform using a model recognized for its simplicity. To prove this thesis, we refer to the eXplicit Multi-Threading (XMT) architecture designed and built at the University of Maryland. XMT is an attempt at re-inventing parallel computing with a solid theoretical foundation and an aggressive scalable design. Algorithmically, XMT is inspired by the PRAM (Parallel Random Access Machine) model and the architecture design is focused on reducing inter-task communication and synchronization overheads and providing an easy-to-program parallel model. This thesis builds upon the existing XMT infrastructure to improve support for efficient execution with a focus on ease-of-programming. Our contributions aim at reducing the programmer's effort in developing XMT applications and improving the overall performance. More concretely, we: (1) present a work-flow guiding programmers to produce efficient parallel solutions starting from a high-level problem; (2) introduce an analytical performance model for XMT programs and provide a methodology to project running time from an implementation; (3) propose and evaluate RAP -- an improved resource-aware compiler loop prefetching algorithm targeted at fine-grained many-core architectures; we demonstrate performance improvements of up to 34.79% on average over the GCC loop prefetching implementation and up to 24.61% on average over a simple hardware prefetching scheme; and (4) implement a number of parallel benchmarks and evaluate the overall performance of XMT relative to existing serial and parallel solutions, showing speedups of up to 13.89x vs.~ a serial processor and 8.10x vs.~parallel code optimized for an existing many-core (GPU). We also discuss the implementation and optimization of the Max-Flow algorithm on XMT, a problem which is among the more advanced in terms of complexity, benchmarking and research interest in the parallel algorithms community. We demonstrate better speed-ups compared to a best serial solution than previous attempts on other parallel platforms

Learning with Delayed Synaptic Plasticity

The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their configuration. Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i.e. rules that update synapses based on the neuron activations and reinforcement signals. However, the distal reward problem arises when the reinforcement signals are not available immediately after each network output to associate the neuron activations that contributed to receiving the reinforcement signal. In this work, we extend Hebbian plasticity rules to allow learning in distal reward cases. We propose the use of neuron activation traces (NATs) to provide additional data storage in each synapse to keep track of the activation of the neurons. Delayed reinforcement signals are provided after each episode relative to the networks' performance during the previous episode. We employ genetic algorithms to evolve delayed synaptic plasticity (DSP) rules and perform synaptic updates based on NATs and delayed reinforcement signals. We compare DSP with an analogous hill climbing algorithm that does not incorporate domain knowledge introduced with the NATs, and show that the synaptic updates performed by the DSP rules demonstrate more effective training performance relative to the HC algorithm.Comment: GECCO201

Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution

Many real-world control and classification tasks involve a large number of features. When artificial neural networks (ANNs) are used for modeling these tasks, the network architectures tend to be large. Neuroevolution is an effective approach for optimizing ANNs; however, there are two bottlenecks that make their application challenging in case of high-dimensional networks using direct encoding. First, classic evolutionary algorithms tend not to scale well for searching large parameter spaces; second, the network evaluation over a large number of training instances is in general time-consuming. In this work, we propose an approach called the Limited Evaluation Cooperative Co-evolutionary Differential Evolution algorithm (LECCDE) to optimize high-dimensional ANNs. The proposed method aims to optimize the pre-synaptic weights of each post-synaptic neuron in different subpopulations using a Cooperative Co-evolutionary Differential Evolution algorithm, and employs a limited evaluation scheme where fitness evaluation is performed on a relatively small number of training instances based on fitness inheritance. We test LECCDE on three datasets with various sizes, and our results show that cooperative co-evolution significantly improves the test error comparing to standard Differential Evolution, while the limited evaluation scheme facilitates a significant reduction in computing time

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.Comment: Evolutionary Computation Journa

Simple and accurate analytical model of planar grids and high-impedance surfaces comprising metal strips or patches

This paper introduces simple analytical formulas for the grid impedance of electrically dense arrays of square patches and for the surface impedance of high-impedance surfaces based on the dense arrays of metal strips or square patches over ground planes. Emphasis is on the oblique-incidence excitation. The approach is based on the known analytical models for strip grids combined with the approximate Babinet principle for planar grids located at a dielectric interface. Analytical expressions for the surface impedance and reflection coefficient resulting from our analysis are thoroughly verified by full-wave simulations and compared with available data in open literature for particular cases. The results can be used in the design of various antennas and microwave or millimeter wave devices which use artificial impedance surfaces and artificial magnetic conductors (reflect-array antennas, tunable phase shifters, etc.), as well as for the derivation of accurate higher-order impedance boundary conditions for artificial (high-) impedance surfaces. As an example, the propagation properties of surface waves along the high-impedance surfaces are studied.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Antennas and Propagatio

Tribological Behavior of Soybean Oil

This chapter presents experimental data in the favor of using soybean oil, additivated or not, as lubricants, the market share of the soybean oil on the lubricants’ market, a SWOT analysis for better configuring the tribological characteristics of the soybean oil and tribological parameters as friction coefficient, wear scar diameter, wear rate of wear scar diameter, etc. and their dependence on testing regime (load and speed). Also, the influence of temperature, shear rate, and oxidation parameters on the soybean oil viscosity is discussed