Availability analysis of heat recovery steam generators used in thermal power plants

The combined-cycle gas and steam turbine power plant presents three main pieces of equipment: gas turbines, steam turbines and heat recovery steam generator (HRSG). In case of HRSG failure the steam cycle is shut down, reducing the power plant output. Considering that the technology for design, construction and operation of high capacity HRSGs is quite recent its availability should be carefully evaluated in order to foresee the performance of the power plant. This study presents a method for reliability and availability evaluation of HRSGs installed in combined-cycle power plant. The method`s first step consists in the elaboration of the steam generator functional tree and development of failure mode and effects analysis. The next step involves a reliability and availability analysis based on the time to failure and time to repair data recorded during the steam generator operation. The third step, aiming at availability improvement, recommends the fault-tree analysis development to identify components the failure (or combination of failures) of which can cause the HRSG shutdown. Those components maintenance policy can be improved through the use of reliability centered maintenance (RCM) concepts. The method is applied on the analysis of two HRSGs installed in a 500 MW combined-cycle power plant. (C) 2010 Elsevier Ltd. All rights reserved.

Signals in Communication Engineering History

This paper is a study of various electric signals, which have been employed throughout the history of communication engineering in its two main landmarks: the telegraph and the telephone. The signals are presented in their time and frequency domain representations. The historical order has been followed in the presentation: wired systems, spark gap wireless, continuous wave (CW) and amplitude modulation (AM), detection by rectification, and frequency modulation (FM). The analysis of these signals is meant to lead into a better understanding of the evolution of communication technology. The material presented in this work could be used to illustrate ""Signals and Systems"" and ""Communication Systems"" courses by taking advantage of its technical as well as historical contents.

Improved effective charge density in MOS capacitors with PECVD SiOxNy dielectric layer obtained at low RF power

In this work SiOxNy films are produced and characterized. Series of samples were deposited by the plasma enhanced chemical vapor deposition (PECVD) technique at low temperatures from silane (SiH4), nitrous oxide (N2O) and helium (He) precursor gaseous mixtures, at different deposition power in order to analyze the effect of this parameter on the films structural properties, on the SiOxNy/Si interface quality and on the SiOxNy effective charge density. In order to compare the film structural properties with the interface (SiOxNy/Si) quality and effective charge density, MOS capacitors were fabricated using these films as dielectric layer. X-ray absorption near-edge spectroscopy (XANES), at the Si-K edge, was utilized to investigate the structure of the films and the material bonding characteristics were analyzed through Fourier transform infrared spectroscopy (FTIR). The MOS capacitors were characterized by low and high frequency capacitance (C-V) measurements, in order to obtain the interface state density (D-it) and the effective charge density (N-ss). An effective charge density linear reduction for decreasing deposition power was observed, result that is attributed to the smaller amount of ions present in the plasma for low RF power. (C) 2008 Elsevier B.V. All rights reserved.

Distributed power control algorithm for multiple access systems based on Verhulst model

In this paper the continuous Verhulst dynamic model is used to synthesize a new distributed power control algorithm (DPCA) for use in direct sequence code division multiple access (DS-CDMA) systems. The Verhulst model was initially designed to describe the population growth of biological species under food and physical space restrictions. The discretization of the corresponding differential equation is accomplished via the Euler numeric integration (ENI) method. Analytical convergence conditions for the proposed DPCA are also established. Several properties of the proposed recursive algorithm, such as Euclidean distance from optimum vector after convergence, convergence speed, normalized mean squared error (NSE), average power consumption per user, performance under dynamics channels, and implementation complexity aspects, are analyzed through simulations. The simulation results are compared with two other DPCAs: the classic algorithm derived by Foschini and Miljanic and the sigmoidal of Uykan and Koivo. Under estimated errors conditions, the proposed DPCA exhibits smaller discrepancy from the optimum power vector solution and better convergence (under fixed and adaptive convergence factor) than the classic and sigmoidal DPCAs. (C) 2010 Elsevier GmbH. All rights reserved.

Thermodynamic information system for diagnosis and prognosis of power plant operation condition

A thermodynamic information system for diagnosis and prognosis of an existing power plant was developed. The system is based on an analytic approach that informs the current thermodynamic condition of all cycle components, as well as the improvement that can be obtained in the cycle performance by the elimination of the discovered anomalies. The effects induced by components anomalies and repairs in other components efficiency, which have proven to be one of the main drawbacks in the diagnosis and prognosis analyses, are taken into consideration owing to the use of performance curves and corrected performance curves together with the thermodynamic data collected from the distributed control system. The approach used to develop the system is explained, the system implementation in a real gas turbine cogeneration combined cycle is described and the results are discussed. (C) 2011 Elsevier Ltd. All rights reserved.

Bias-corrected Pearson estimating functions for Taylor`s power law applied to benthic macrofauna data

Estimation of Taylor`s power law for species abundance data may be performed by linear regression of the log empirical variances on the log means, but this method suffers from a problem of bias for sparse data. We show that the bias may be reduced by using a bias-corrected Pearson estimating function. Furthermore, we investigate a more general regression model allowing for site-specific covariates. This method may be efficiently implemented using a Newton scoring algorithm, with standard errors calculated from the inverse Godambe information matrix. The method is applied to a set of biomass data for benthic macrofauna from two Danish estuaries. (C) 2011 Elsevier B.V. All rights reserved.

Chemical Signals in the Stingless Bee, Frieseomelitta varia, Indicate Caste, Gender, Age, and Reproductive Status

Chemical compounds on the cuticle are a rich source of information used during interactions among social insects. Despite the multitude of studies on these substances and their function in ants, wasps, and honeybees, little is known about this subject in stingless bees (Hymenoptera: Apidae, Meliponini). We studied the chemical composition of the cuticle of the stingless bee, Frieseomelitta varia, by gas chromatography-mass spectrometry (GC-MS), to investigate potential chemical variation among castes, gender, age, and reproductive status. We found differences in the cuticular hydrocarbon composition among workers, males, and queens, recording both qualitative and quantitative differences among individuals of different ages and gender. The cuticle of physogastric queens presented a chemical profile that was distinct from all other groups in the analysis, with high relative abundances of alkenes and alkadienes with 27, 29, and 31 carbon atoms. We discuss the possibility that these compounds signal a queen`s presence to the colony, thereby initiating all vital worker-queen interactions.

IDENTIFICATION OF THE STATE OF A WET SPOUTED BED THROUGH TIME-FREQUENCY ANALYSIS OF PRESSURE FLUCTUATION TIME SERIES

The feasibility of detecting instability in wet spouted beds via pressure fluctuation (PF) time-series analyses was investigated. Experiments were carried out in a cylindrical Plexiglas column of diameter 150 mm with a conical base of internal angle 60 degrees, an inlet orifice diameter of 25 mm and glass beads of diameter 2.4 mm. Transducers at several axial positions measured PF time series with incremental addition of aqueous sucrose solutions of different concentrations. Liquid addition affected the spouted bed dynamics, causing irregular spouting, increased voidage in the annulus, increased fountain height, irregular annulus height, channelling, agglomeration, and adhesion of particles to the column walls. Autocorrelations indicated the appearance of periodicities in the PF signals with increasing sucrose addition. Dominant peaks in power-spectral density developed at low frequencies with changing system dynamics. The results indicate that PF signals furnish relevant information on system dynamics, useful for monitoring and control of spouted bed operations such as particle coating and drying of paste-like materials.

INVESTIGATION OF ACOUSTIC SIGNALS AS A TOOL FOR CHARACTERIZING SPOUTED BED DYNAMICS

The feasibility of characterizing the dynamics of a spouted bed based on acoustic emission (AE) signals is evaluated. Acoustic emission signals were measured in a semi-cylindrical Plexiglas column of diameter 150 mm and height 1000 mm with a conical base of internal angle 60 degrees and 25 mm inlet orifice diameter. Data were obtained for U/U(ms), from 0.3 to 2.0, static bed height from 250 to 500 mm, and glass beads of diameter 1.2 and 2.4 mm. AE signals reflected the effects of particle size and U/U(ms), but in general were insensitive to bed depth, even when there were drastic changes in spouting flow patterns. The results indicate that the AE signals were insensitive to the spouted bed hydrodynamics for the conditions studied. Overall, it appears that the AE analysis is unlikely to be a suitable technique for discriminating spouted bed flow regimes, at least for the range of frequencies and operating conditions investigated.

Education in Human Rights: Reflections on Power, Violence and Authority in the School

This article explores human rights and education based on an intervention experience conducted in three schools located in Sao Paulo City, which had as its main goal a substantial reduction in violence (2004-2005). The guideline was that education should be considered a basic human right, taking into consideration the power and authority relations that exist within this institution. What are the problems that we face, nowadays, to consider education as a human right, in the difficult Brazilian history? Is it possible to think about some kind of democratic authority within the school, when our vision of authority is linked to despotic leaders, or even when there is no space for any authority? How does this discussion associate with the violence in our daily life in school? These are some of the questions included in the debate proposed by this article.

Nestmate recognition in the stingless bee Frieseomelitta varia (Hymenoptera, Apidae, Meliponini): sources of chemical signals

Social insects use cuticular lipids for nestmate recognition. These lipids are chiefly hydrocarbons that can be endogenously produced or acquired from the environment. Although these compounds are already described as coming from different sources for different groups of social insects, nothing is known about the source of cuticular hydrocarbons in stingless bees. We used behavioural recognition tests and cuticle chemical investigation to elucidate the role of endogenous and environmentally based cues for nestmate recognition in the stingless bee Frieseomelitta varia. We found that although newly emerged workers present specific cuticle patterns according to their nest origin, these compounds are not used for nestmate recognition, since newly emerged workers are broadly accepted in different colonies. The cerumen used in nest construction played an important role in recognition behaviour. Twenty minutes of contact with foreign cerumen was sufficient to increase the rejection rates of nestmates and separate the groups of workers according to their chemical profile. On the other hand, tests of feeding on a common diet showed no effect on chemical cuticle pattern or recognition behaviour. (C) 2010 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

The look of royalty: visual and odour signals of reproductive status in a paper wasp

Reproductive conflicts within animal societies occur when all females can potentially reproduce. In social insects, these conflicts are regulated largely by behaviour and chemical signalling. There is evidence that presence of signals, which provide direct information about the quality of the reproductive females would increase the fitness of all parties. In this study, we present an association between visual and chemical signals in the paper wasp Polistes satan. Our results showed that in nest-founding phase colonies, variation of visual signals is linked to relative fertility, while chemical signals are related to dominance status. In addition, experiments revealed that higher hierarchical positions were occupied by subordinates with distinct proportions of cuticular hydrocarbons and distinct visual marks. Therefore, these wasps present cues that convey reliable information of their reproductive status.

Germline mutations in TMEM127 confer susceptibility to pheochromocytoma

Pheochromocytomas, which are catecholamine-secreting tumors of neural crest origin, are frequently hereditary(1). However, the molecular basis of the majority of these tumors is unknown(2). We identified the transmembrane-encoding gene TMEM127 on chromosome 2q11 as a new pheochromocytoma susceptibility gene. In a cohort of 103 samples, we detected truncating germline TMEM127 mutations in approximately 30% of familial tumors and about 3% of sporadic-appearing pheochromocytomas without a known genetic cause. The wild-type allele was consistently deleted in tumor DNA, suggesting a classic mechanism of tumor suppressor gene inactivation. Pheochromocytomas with mutations in TMEM127 are transcriptionally related to tumors bearing NF1 mutations and, similarly, show hyperphosphorylation of mammalian target of rapamycin (mTOR) effector proteins. Accordingly, in vitro gain-of-function and loss-of-function analyses indicate that TMEM127 is a negative regulator of mTOR. TMEM127 dynamically associates with the endomembrane system and colocalizes with perinuclear (activated) mTOR, suggesting a subcompartmental-specific effect. Our studies identify TMEM127 as a tumor suppressor gene and validate the power of hereditary tumors to elucidate cancer pathogenesis.

Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI

Functional magnetic resonance imaging (fMRI) based on BOLD signal has been used to indirectly measure the local neural activity induced by cognitive tasks or stimulation. Most fMRI data analysis is carried out using the general linear model (GLM), a statistical approach which predicts the changes in the observed BOLD response based on an expected hemodynamic response function (HRF). In cases when the task is cognitively complex or in cases of diseases, variations in shape and/or delay may reduce the reliability of results. A novel exploratory method using fMRI data, which attempts to discriminate between neurophysiological signals induced by the stimulation protocol from artifacts or other confounding factors, is introduced in this paper. This new method is based on the fusion between correlation analysis and the discrete wavelet transform, to identify similarities in the time course of the BOLD signal in a group of volunteers. We illustrate the usefulness of this approach by analyzing fMRI data from normal subjects presented with standardized human face pictures expressing different degrees of sadness. The results show that the proposed wavelet correlation analysis has greater statistical power than conventional GLM or time domain intersubject correlation analysis. (C) 2010 Elsevier B.V. All rights reserved.

Analyzing the connectivity between regions of interest: An approach based on cluster Granger causality for NRI data analysis

The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single ""representative"" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI. (c) 2010 Elsevier Inc. All rights reserved.