Effects of privatization of companies of strategic importance for Serbian economy

Приватизација означава трансфер средстава која су некада била у државном и друштвеном власништву у руке приватног сектора. Државна и друштвена власничка структура била је уобичајена и имала је далеко већи број облика у бившим социјалистичким земљама него што је то случај са земљама запада. Циљеви приватизације су разноврсни и бројни, тако да се разликују пре свега економски, политички и социјални, а затим циљеви на макро и микро нивоу. Нови Закон о приватизацији усвојила је Скупштина Србије 2001. године и тиме поставила темељ своје стратегије својинских промена. У раду се говори о концептима приватизације у складу са законом, тендерској продаји и поступцима реструктурирања, као моделима за приватизацију предузећа од стратешког интереса у Републици Србији. Ефекти приватизације су сагледани у ширем и ужем смислу, како утицаја на општи привредни развој, тако и последица посматраних на групи приватизованих предузећа од стратешког интереса у домену њихове оперативности и реализације, социјалног аспекта (запослених) и модернизације (прилив инвестиција). Један од циљева рада је да кроз свеобухватно сагледавање досадашњих резултата приватизације предузећа од стратешког интереса и утицаја истих на структурне промене, да смернице за наставак и одабир даљих активности у функцији успешног завршетка приватизационог процеса у Републици Србији. Посебан осврт у раду дат је на приватизацији јавних предузећа, која је тек у иницијалној фази у Србији, уз претходно сагледавање сличних примера из других земаља у транзицији и праксе развијених земаља запада.Privatization determines transfer of funds, that were once state-owned or socially-owned, into the private ownership. State- and socially-owned company structures used to be more common and more diverse in former socialist countries than it was the case in western countries. Privatization targets are various and numerous, differing primarily in terms of economic, political and social aspects, but also on macro and micro levels. New Privatization Law was adopted by the Assembly of Serbia in 2001 which thus laid foundations for change strategy of companies' ownership structure. This thesis is about privatization concepts which are in accordance with the law, tender sale and restructuring procedures, and are used as models for company privatization of strategic importance for the Republic of Serbia. Privatization effects are put into broad and narrow prospective of both impact on the overall economic development and the consequences observed on a group of privatized companies of strategic importance within their effectiveness and realization, social aspect (employees) and modernization (investment inflow). One of the above mentioned targets is to provide directions for continuation and selection of further activities regarding successful completion of the privatization process in the Republic Serbia, and that by means of comprehensive observation of the existing results of the privatization of companies of strategic importance and their impact on the structural changes. This thesis puts special emphasis on privatization of public companies, the process which is in initial phase in Serbia, with previous consideration of similar examples from other countries in transition and best practice of the developed western countries

Theory and modeling of the magnetic field measurement in LISA PathFinder

The magnetic diagnostics subsystem of the LISA Technology Package (LTP) on board the LISA PathFinder (LPF) spacecraft includes a set of four tri-axial fluxgate magnetometers, intended to measure with high precision the magnetic field at their respective positions. However, their readouts do not provide a direct measurement of the magnetic field at the positions of the test masses, and hence an interpolation method must be designed and implemented to obtain the values of the magnetic field at these positions. However, such interpolation process faces serious difficulties. Indeed, the size of the interpolation region is excessive for a linear interpolation to be reliable while, on the other hand, the number of magnetometer channels does not provide sufficient data to go beyond the linear approximation. We describe an alternative method to address this issue, by means of neural network algorithms. The key point in this approach is the ability of neural networks to learn from suitable training data representing the behavior of the magnetic field. Despite the relatively large distance between the test masses and the magnetometers, and the insufficient number of data channels, we find that our artificial neural network algorithm is able to reduce the estimation errors of the field and gradient down to levels below 10%, a quite satisfactory result. Learning efficiency can be best improved by making use of data obtained in on-ground measurements prior to mission launch in all relevant satellite locations and in real operation conditions. Reliable information on that appears to be essential for a meaningful assessment of magnetic noise in the LTP.Comment: 10 pages, 8 figures, 2 tables, submitted to Physical Review

Exploring synergetic effects of dimensionality reduction and resampling tools on hyperspectral imagery data classification

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA and a supervised filter are applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of combining several techniques to tackle the imbalance and the high dimensionality problems, and also to evaluate the order of application that leads to the best classification performance. Experimental results demonstrate the significance of using together these two preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order corresponds to first a resampling strategy and then a feature (or extraction) selection algorithm, this is a question that still needs a much more thorough investigation in the futureThis work has partially been supported by the Spanish Ministry of Education and Science under grants CSD2007–00018, AYA2008–05965–0596 and TIN2009–14205, the Fundació Caixa Castelló–Bancaixa under grant P1–1B2009–04, and the Generalitat Valenciana under grant PROMETEO/2010/02

The Low-Energy Telescope (LET) and SEP Central Electronics for the STEREO Mission

The Low-Energy Telescope (LET) is one of four sensors that make up the Solar Energetic Particle (SEP) instrument of the IMPACT investigation for NASA’s STEREO mission. The LET is designed to measure the elemental composition, energy spectra, angular distributions, and arrival times of H to Ni ions over the energy range from ∼3 to ∼30 MeV/nucleon. It will also identify the rare isotope ^(3)He and trans-iron nuclei with 30≤Z≤83. The SEP measurements from the two STEREO spacecraft will be combined with data from ACE and other 1-AU spacecraft to provide multipoint investigations of the energetic particles that result from interplanetary shocks driven by coronal mass ejections (CMEs) and from solar flare events. The multipoint in situ observations of SEPs and solar-wind plasma will complement STEREO images of CMEs in order to investigate their role in space weather. Each LET instrument includes a sensor system made up of an array of 14 solid-state detectors composed of 54 segments that are individually analyzed by custom Pulse Height Analysis System Integrated Circuits (PHASICs). The signals from four PHASIC chips in each LET are used by a Minimal Instruction Set Computer (MISC) to provide onboard particle identification of a dozen species in ∼12 energy intervals at event rates of ∼1,000 events/sec. An additional control unit, called SEP Central, gathers data from the four SEP sensors, controls the SEP bias supply, and manages the interfaces to the sensors and the SEP interface to the Instrument Data Processing Unit (IDPU). This article outlines the scientific objectives that LET will address, describes the design and operation of LET and the SEP Central electronics, and discusses the data products that will result

Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer

Towards Comprehensive Foundations of Computational Intelligence

Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.

Learning and soft computing : support vector machines, neural networks, and fuzzy logic models

xxxii, 541 p. : ill. ; 24 cm