New approach for power transformer protection based on intelligent hybrid systems

A power transformer needs continuous monitoring and fast protection as it is a very expensive piece of equipment and an essential element in an electrical power system. The most common protection technique used is the percentage differential logic, which provides discrimination between an internal fault and different operating conditions. Unfortunately, there are some operating conditions of power transformers that can mislead the conventional protection affecting the power system stability negatively. This study proposes the development of a new algorithm to improve the protection performance by using fuzzy logic, artificial neural networks and genetic algorithms. An electrical power system was modelled using Alternative Transients Program software to obtain the operational conditions and fault situations needed to test the algorithm developed, as well as a commercial differential relay. Results show improved reliability, as well as a fast response of the proposed technique when compared with conventional ones.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Video Quality Assessments on Digital TV and Video Streaming Services Using Objective Metrics

There is a wide range of telecommunications services that transmit voice, video and data through complex transmission networks and in some cases, the service has not an acceptable quality level for the end user. In this sense the study of methods for assessing video quality and voice have a very important role. This paper presents a classification scheme, based on different criteria, of the methods and metrics that are being studied in recent years. This paper presents how the video quality is affected by degradation in the transmission channel in two kinds of services: Digital TV (ISDB-TB) due the fading in the air interface and video streaming service on an IP network due packet loss. For Digital TV tests was set up a scenario where the digital TV transmitter is connected to an RF channel emulator, where are inserted different fading models and at the end, the videos are saved in a mobile device. The tests of streaming video were performed in an isolated scenario of IP network, which are scheduled several network conditions, resulting in different qualities of video reception. The video quality assessment is performed using objective assessment methods: PSNR, SSIM and VQM. The results show how the losses in the transmission channel affects the quality of end-user experience on both services studied.

Analysis of dispersive and dissipative media with optical resonances

In this paper we analyze the problem of light-matter interaction when absorptive resonances are imbedded in the material dispersion. We apply an improved approach to aluminum (Al) in the optical frequency range to investigate the impact of these resonances on the operating characteristics of Al-based nanoscale devices. Quantities such as group velocity, stored energy density, and energy velocity, normally obtained using a single resonance model [Wave Propagation and Group Velocity (Academic Press, 1960), Nat. Mater. 11, 208 (2012)], are now accurately calculated regardless of the medium adopted. We adapt the Loudon approach [Nat. Mater. 11, 208 (2012)] to media with several optical resonances and present the details of the extended model. We also show pertinent results for Al-based metal-dielectric-metal (MDM) waveguides, around spectral resonances. The model delineated here can be applied readily to any metal accurately characterized by Drude-Lorentz spectral resonance features. (C) 2012 Optical Society of America

Unveiling the relationship between complex networks metrics and word senses

The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts. Copyright (C) EPLA, 2012

Lyotropic mixture made of potassium laurate/1-undecanol/K2SO4/water presenting high birefringences and large biaxial nematic phase domain: a laser conoscopy study

The lyotropic liquid crystalline quaternary mixture made of potassium laurate (KL), potassium sulphate, 1-undecanol and water was investigated by experimental optical methods (optical microscopy and laser conoscopy). In a particular temperature and relative concentrations range, the three nematic phases (two uniaxial and one biaxial) were identified. The biaxial domain in the temperature/KL concentration surface is larger when compared to other lyotropic mixtures. Moreover, this new mixture gives nematic phases with higher birefringence than similar systems. The behavior of the symmetric tensor order parameter invariants sigma(3) and sigma(2) calculated from the measured optical birefringences supports that the uniaxial-to-biaxial transitions are of second order, described by a mean-field theory.

Reference model for implementing an MRP system in a highly diverse component and seasonal lean production environment

Companies are currently choosing to integrate logics and systems to achieve better solutions. These combinations also include companies striving to join the logic of material requirement planning (MRP) system with the systems of lean production. The purpose of this article was to design an MRP as part of the implementation of an enterprise resource planning (ERP) in a company that produces agricultural implements, which has used the lean production system since 1998. This proposal is based on the innovation theory, theory networks, lean production systems, ERP systems and the hybrid production systems, which use both components and MRP systems, as concepts of lean production systems. The analytical approach of innovation networks enables verification of the links and relationships among the companies and departments of the same corporation. The analysis begins with the MRP implementation project carried out in a Brazilian metallurgical company and follows through the operationalisation of the MRP project, until its production stabilisation. The main point is that the MRP system should help the company's operations with regard to its effective agility to respond in time to demand fluctuations, facilitating the creation process and controlling the branch offices in other countries that use components produced in the matrix, hence ensuring more accurate estimates of stockpiles. Consequently, it presents the enterprise knowledge development organisational modelling methodology in order to represent further models (goals, actors and resources, business rules, business process and concepts) that should be included in this MRP implementation process for the new configuration of the production system.

A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabasi-Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

Social Networks, Segregation and Poverty in Sao Paulo

The associations between segregation and urban poverty have been intensely scrutinized by the sociology and urban studies literatures. More recently, several studies have emphasized the importance of social networks for living conditions. Yet relatively few studies have tested the precise effects of social networks, and fewer still have focused on the joint effects of residential segregation and social networks on living conditions. This article explores the associations between networks, segregation and some of the most important dimensions of access to goods and services obtained in markets: escaping from social precariousness and obtaining monetary income. It is based on a study of the personal networks of 209 individuals living in situations of poverty in seven locales in the metropolitan area of Sao Paulo. Using network analysis and multivariate techniques, I show that relational settings strongly influence the access individuals have to markets, leading some individuals into worse living conditions and poverty. At the same time, although segregation plays an important role in poverty, its effects tend to be mediated by the networks in which individuals are embedded. Networks in this sense may enhance or mitigate the effects of isolation produced by space.

Application of the Z-scan technique to determine the optical Kerr coefficient and two-photon absorption coefficient of magnetite nanoparticles colloidal suspension

We investigate the occurrence of the optical Kerr effect and two-photon absorption when an oil-based magnetic Fe3O4 nanoparticles colloidal suspension is illuminated with high intensity femtosecond laser pulses. The frequency of the pulses is controlled and the Z-scan technique is employed in our measurements of the nonlinear optical Kerr coefficient (n(2)) and two-photon absorption coefficient (beta). From these values it was possible to calculate the real and imaginary parts of the third-order susceptibility. We observed that increasing the pulse frequency, additional physical processes take place, increasing artificially the absolute values of n(2) and beta. The experimental conditions are discussed to assure the obtention of reliable values of these nonlinear optical parameters, which may be useful in all-optical switching and optical power limiting applications. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4723829]

Cationic nanoparticles for delivery of amphotericin B: preparation, characterization and activity in vitro

Abstract Background Particulate systems are well known to be able to deliver drugs with high efficiency and fewer adverse side effects, possibly by endocytosis of the drug carriers. On the other hand, cationic compounds and assemblies exhibit a general antimicrobial action. In this work, cationic nanoparticles built from drug, cationic lipid and polyelectrolytes are shown to be excellent and active carriers of amphotericin B against C. albicans. Results Assemblies of amphotericin B and cationic lipid at extreme drug to lipid molar ratios were wrapped by polyelectrolytes forming cationic nanoparticles of high colloid stability and fungicidal activity against Candida albicans. Experimental strategy involved dynamic light scattering for particle sizing, zeta-potential analysis, colloid stability, determination of AmB aggregation state by optical spectra and determination of activity against Candida albicans in vitro from cfu countings. Conclusion Novel and effective cationic particles delivered amphotericin B to C. albicans in vitro with optimal efficiency seldom achieved from drug, cationic lipid or cationic polyelectrolyte in separate. The multiple assembly of antibiotic, cationic lipid and cationic polyelctrolyte, consecutively nanostructured in each particle produced a strategical and effective attack against the fungus cells.

Efficient 1535 nm light emission from an all-Si-based optical micro-cavity containing Er3+ and Yb3+ ions

This work reports on the construction and spectroscopic analyses of optical micro-cavities (OMCs) that efficiently emit at ~1535 nm. The emission wavelength matches the third transmission window of commercial optical fibers and the OMCs were entirely based on silicon. The sputtering deposition method was adopted in the preparation of the OMCs, which comprised two Bragg reflectors and one spacer layer made of either Er- or ErYb-doped amorphous silicon nitride. The luminescence signal extracted from the OMCs originated from the 4I13/2→4I15/2 transition (due to Er3+ ions) and its intensity showed to be highly dependent on the presence of Yb3+ ions.According to the results, the Er3+-related light emission was improved by a factor of 48 when combined with Yb3+ ions and inserted in the spacer layer of the OMC. The results also showed the effectiveness of the present experimental approach in producing Si-based light-emitting structures in which the main characteristics are: (a) compatibility with the actual microelectronics industry, (b) the deposition of optical quality layers with accurate composition control, and (c) no need of uncommon elements-compounds nor extensive thermal treatments. Along with the fundamental characteristics of the OMCs, this work also discusses the impact of the Er3+-Yb3+ ion interaction on the emission intensity as well as the potential of the present findings.

Experimental Investigation of Three-Dimensional Single and Double optical Lattices

A complete laser cooling setup was built, with focus on threedimensional near-resonant optical lattices for cesium. These consist of regularly ordered micropotentials, created by the interference of four laser beams. One key feature of optical lattices is an inherent ”Sisyphus cooling” process. It efficiently extracts kinetic energy from the atoms, leading to equilibrium temperatures of a few µK. The corresponding kinetic energy is lower than the depth of the potential wells, so that atoms can be trapped. We performed detailed studies of the cooling processes in optical lattices by using the time-of-flight and absorption-imaging techniques. We investigated the dependence of the equilibrium temperature on the optical lattice parameters, such as detuning, optical potential and lattice geometry. The presence of neighbouring transitions in the cesium hyperfine level structure was used to break symmetries in order to identify, which role “red” and “blue” transitions play in the cooling. We also examined the limits for the cooling process in optical lattices, and the possible difference in steady-state velocity distributions for different directions. Moreover, in collaboration with ´Ecole Normale Sup´erieure in Paris, numerical simulations were performed in order to get more insight in the cooling dynamics of optical lattices. Optical lattices can keep atoms almost perfectly isolated from the environment and have therefore been suggested as a platform for a host of possible experiments aimed at coherent quantum manipulations, such as spin-squeezing and the implementation of quantum logic-gates. We developed a novel way to trap two different cesium ground states in two distinct, interpenetrating optical lattices, and to change the distance between sites of one lattice relative to sites of the other lattice. This is a first step towards the implementation of quantum simulation schemes in optical lattices.