Fatigue in Brazilian cancer patients, caregivers, and nursing students: a psychometric validation study of the Piper Fatigue Scale-Revised

The objective of this study was to validate the Piper Fatigue Scale-Revised (PFS-R) for use in Brazilian culture. Translation of the PFS-R into Portuguese and validity and reliability tests were performed. Convenience samples in Brazil we as follows: 584 cancer patients (mean age 57 +/- 13 years; 51.3% female); 184 caregivers (mean age 50 +/- 12.7 years; 65.8% female); and 189 undergraduate nursing students (mean age 21.6 +/- 2.8 years; 96.2% female); Instruments used were as follows: Brazilian PFS, Beck Depression Inventory (BDI), and Karnofsky Performance Scale (KPS). The 22 items of the Brazilian PFS loaded well (factor loading > 0.35) on three dimensions identified by factor analysis (behavioral, affective, and sensorial-psychological). These dimensions explained 65% of the variance. Internal consistency reliability was very good (Cronbach`s alpha ranged from 0.841 to 0.943 for the total scale and its dimensions). Cancer patients and their caregivers completed the Brazilian PFS twice for test-retest reliability and results showed good stability (Pearson`s r a parts per thousand yenaEuro parts per thousand 0,60, p < 0,001). Correlations among the Brazilian PFS and other scales were significant, in hypothesized directions, and mostly moderate contributing to divergent (Brazilian PFS x KPS) and convergent validity (Brazilian PFS x BDI). Mild, moderate, and severe fatigue in patients were reported by 73 (12.5%), 167 (28.6%), and 83 (14.2%), respectively. Surprisingly, students had the highest mean total fatigue scores; no significant differences were observed between patients and caregivers showing poor discriminant validity. While the Brazilian PFS is a reliable and valid instrument to measure fatigue in Brazilian cancer patients, further work is needed to evaluate the discriminant validity of the scale in Brazil.

Electrodeposition of copper on titanium wires: Taguchi experimental design approach

Electrodeposition of thin copper layer was carried out on titanium wires in acidic sulphate bath. The influence of titanium surface preparation, cathodic current density, copper sulphate and sulphuric acid concentrations, electrical charge density and stirring of the solution on the adhesion of the electrodeposits was studied using the Taguchi statistical method. A L(16) orthogonal array with the six factors of control at two levels each and three interactions was employed. The analysis of variance of the mean adhesion response and signal-to-noise ratio showed the great influence of cathodic current density on adhesion. on the contrary, the other factors as well as the three investigated interactions revealed low or no significant effect. From this study optimized electrolysis conditions were defined. The copper electrocoating improved the electrical conductivity of the titanium wire. This shows that copper electrocoated titanium wires could be employed for both electrical purpose and mechanical reinforcement in superconducting magnets. (C) 2008 Elsevier B.V. All rights reserved.

Digital frequency relaying based on the modified least mean square method

This research presents a method for frequency estimation in power systems using an adaptive filter based on the Least Mean Square Algorithm (LMS). In order to analyze a power system, three-phase voltages were converted into a complex signal applying the alpha beta-transform and the results were used in an adaptive filtering algorithm. Although the use of the complex LMS algorithm is described in the literature, this paper deals with some practical aspects of the algorithm implementation. In order to reduce computing time, a coefficient generator was implemented. For the algorithm validation, a computing simulation of a power system was carried Out using the ATP software. Many different situations were Simulated for the performance analysis of the proposed methodology. The results were compared to a commercial relay for validation, showing the advantages of the new method. (C) 2009 Elsevier Ltd. All rights reserved.

Reducing mean flow time in permutation flow shop

This paper deals with the traditional permutation flow shop scheduling problem with the objective of minimizing mean flowtime, therefore reducing in-process inventory. A new heuristic method is proposed for the scheduling problem solution. The proposed heuristic is compared with the best one considered in the literature. Experimental results show that the new heuristic provides better solutions regarding both the solution quality and computational effort.

The random barrier-crossing problem

This paper addresses the time-variant reliability analysis of structures with random resistance or random system parameters. It deals with the problem of a random load process crossing a random barrier level. The implications of approximating the arrival rate of the first overload by an ensemble-crossing rate are studied. The error involved in this so-called ""ensemble-crossing rate"" approximation is described in terms of load process and barrier distribution parameters, and in terms of the number of load cycles. Existing results are reviewed, and significant improvements involving load process bandwidth, mean-crossing frequency and time are presented. The paper shows that the ensemble-crossing rate approximation can be accurate enough for problems where load process variance is large in comparison to barrier variance, but especially when the number of load cycles is small. This includes important practical applications like random vibration due to impact loadings and earthquake loading. Two application examples are presented, one involving earthquake loading and one involving a frame structure subject to wind and snow loadings. (C) 2007 Elsevier Ltd. All rights reserved.

Mean sea-level rise impacts on Santos Bay, Southeastern Brazil - physical modelling study

The greenhouse effect and resulting increase in the Earth`s temperature may accelerate the mean sea-level rise. The natural response of bays and estuaries to this rise, such as this case study of Santos Bay (Brazil), will include change in shoreline position, land flooding and wetlands impacts. The main impacts of this scenario were studied in a physical model built in the Coastal and Harbour Division of Hydraulic Laboratory, University of Sao Paulo, and the main conclusions are presented in this paper. The model reproduces near 1,000 km(2) of the study area, including Santos, Sao Vicente, Praia Grande, Cubatao, Guaruja and Bertioga cities.

Volumetric reconstruction of the mean flow around circular cylinders fitted with strakes

The volumetric reconstruction technique presented in this paper employs a two-camera stereoscopic particle image velocimetry (SPIV) system in order to reconstruct the mean flow behind a fixed cylinder fitted with helical strakes, which are commonly used to suppress vortex-induced vibrations (VIV). The technique is based on the measurement of velocity fields at equivalent adjacent planes that results in pseudo volumetric fields. The main advantage over proper volumetric techniques is the avoidance of additional equipment and complexity. The averaged velocity fields behind the straked cylinders and the geometrical periodicity of the three-start configuration are used to further simplify the reconstruction process. Two straked cylindrical models with the same pitch (p = 10d) and two different heights (h = 0.1 and 0.2d) are tested. The reconstructed flow shows that the strakes introduce in the wake flow a well-defined wavelength of one-third of the pitch. Measurements of hydrodynamic forces, fluctuating velocity, vortex formation length, and vortex shedding frequency show the interdependence of the wake parameters. The vortex formation length is increased by the strakes, which is an important effect for the suppression of vortex-induced vibrations. The results presented complement previous investigations concerning the effectiveness of strakes as VIV suppressors and provide a basis of comparison to numerical simulations.

Numerical prediction of gas concentrations and fluctuations above a triangular hill within a turbulent boundary layer

The objective of the present work is to propose a numerical and statistical approach, using computational fluid dynamics, for the study of the atmospheric pollutant dispersion. Modifications in the standard k-epsilon turbulence model and additional equations for the calculation of the variance of concentration are introduced to enhance the prediction of the flow field and scalar quantities. The flow field, the mean concentration and the variance of a flow over a two-dimensional triangular hill, with a finite-size point pollutant source, are calculated by a finite volume code and compared with published experimental results. A modified low Reynolds k-epsilon turbulence model was employed in this work, using the constant of the k-epsilon model C(mu)=0.03 to take into account the inactive atmospheric turbulence. The numerical results for the velocity profiles and the position of the reattachment point are in good agreement with the experimental results. The results for the mean and the variance of the concentration are also in good agreement with experimental results from the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Scheduling in a two-machine flowshop for the minimization of the mean absolute deviation from a common due date

This paper addresses the minimization of the mean absolute deviation from a common due date in a two-machine flowshop scheduling problem. We present heuristics that use an algorithm, based on proposed properties, which obtains an optimal schedule fora given job sequence. A new set of benchmark problems is presented with the purpose of evaluating the heuristics. Computational experiments show that the developed heuristics outperform results found in the literature for problems up to 500 jobs. (C) 2007 Elsevier Ltd. All rights reserved.

Generation of self-similar Gaussian time series by means of the DWT and DWPT variance maps

Due to the several kinds of services that use the Internet and data networks infra-structures, the present networks are characterized by the diversity of types of traffic that have statistical properties as complex temporal correlation and non-gaussian distribution. The networks complex temporal correlation may be characterized by the Short Range Dependence (SRD) and the Long Range Dependence - (LRD). Models as the fGN (Fractional Gaussian Noise) may capture the LRD but not the SRD. This work presents two methods for traffic generation that synthesize approximate realizations of the self-similar fGN with SRD random process. The first one employs the IDWT (Inverse Discrete Wavelet Transform) and the second the IDWPT (Inverse Discrete Wavelet Packet Transform). It has been developed the variance map concept that allows to associate the LRD and SRD behaviors directly to the wavelet transform coefficients. The developed methods are extremely flexible and allow the generation of Gaussian time series with complex statistical behaviors.

Generalized Coupled Algebraic Riccati Equations for Discrete-time Markov Jump with Multiplicative Noise Systems

In this paper we consider the existence of the maximal and mean square stabilizing solutions for a set of generalized coupled algebraic Riccati equations (GCARE for short) associated to the infinite-horizon stochastic optimal control problem of discrete-time Markov jump with multiplicative noise linear systems. The weighting matrices of the state and control for the quadratic part are allowed to be indefinite. We present a sufficient condition, based only on some positive semi-definite and kernel restrictions on some matrices, under which there exists the maximal solution and a necessary and sufficient condition under which there exists the mean square stabilizing solution fir the GCARE. We also present a solution for the discounted and long run average cost problems when the performance criterion is assumed be composed by a linear combination of an indefinite quadratic part and a linear part in the state and control variables. The paper is concluded with a numerical example for pension fund with regime switching.

Linear minimum mean square filter for discrete-time linear systems with Markov jumps and multiplicative noises

In this paper we obtain the linear minimum mean square estimator (LMMSE) for discrete-time linear systems subject to state and measurement multiplicative noises and Markov jumps on the parameters. It is assumed that the Markov chain is not available. By using geometric arguments we obtain a Kalman type filter conveniently implementable in a recurrence form. The stationary case is also studied and a proof for the convergence of the error covariance matrix of the LMMSE to a stationary value under the assumption of mean square stability of the system and ergodicity of the associated Markov chain is obtained. It is shown that there exists a unique positive semi-definite solution for the stationary Riccati-like filter equation and, moreover, this solution is the limit of the error covariance matrix of the LMMSE. The advantage of this scheme is that it is very easy to implement and all calculations can be performed offline. (c) 2011 Elsevier Ltd. All rights reserved.

The Poisson-exponential lifetime distribution

In this paper we proposed a new two-parameters lifetime distribution with increasing failure rate. The new distribution arises on a latent complementary risk problem base. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulae for its reliability and failure rate functions, quantiles and moments, including the mean and variance. A simple EM-type algorithm for iteratively computing maximum likelihood estimates is presented. The Fisher information matrix is derived analytically in order to obtaining the asymptotic covariance matrix. The methodology is illustrated on a real data set. (C) 2010 Elsevier B.V. All rights reserved.

Extensions of discrete triangular distributions and boundary bias in kernel estimation for discrete functions

Asymmetric discrete triangular distributions are introduced in order to extend the symmetric ones serving for discrete associated kernels in the nonparametric estimation for discrete functions. The extension from one to two orders around the mode provides a large family of discrete distributions having a finite support. Establishing a bridge between Dirac and discrete uniform distributions, some different shapes are also obtained and their properties are investigated. In particular, the mean and variance are pointed out. Applications to discrete kernel estimators are given with a solution to a boundary bias problem. (C) 2010 Elsevier B.V. All rights reserved.

Using a New Criterion to Identify Sites for Mean Soil Water Storage Evaluation

Establishing a few sites in which measurements of soil water storage (SWS) are time stable significantly reduces the efforts involved in determining average values of SWS. This study aimed to apply a new criterion the mean absolute bias error (MABE)-to identify temporally stable sites for mean SWS evaluation. The performance of MABE was compared with that of the commonly used criterion, the standard deviation of relative difference (SDRD). From October 2004 to October 2008, SWS of four soil layers (0-1.0, 1.0-2.0,2.0-3.0, and 3.0-4.0 m) was measured, using a neutron probe, at 28 sites on a hillslope of the Loess Plateau, China. A total of 37 SWS data sets taken over time were divided into two subsets, the first consisting of 22 dates collected during the calibration period from October 2004 to September 2006, and the second with 15 dates collected during the validation period from October 2006 to October 2008. The results showed that if a critical value of 5% for MABE was defined, more than half the sites were temporally stable for both periods, and the number of temporally stable sires generally increased with soil depth. Compared with SDRD, MABE was more suitable for the identification of time-stable sites for mean SS prediction. Since the absolute prediction error of drier sites is more sensitive to changes in relative difference in terms of mean SWS prediction, the sites of wet sectors should be preferable for mean SWS prediction for the same changes in relative difference.