Adding fever to WHO criteria for diagnosing pneumonia enhances the ability to identify pneumonia cases among wheezing children

Objective To examine the ability of the criteria proposed by the WHO to identify pneumonia among cases presenting with wheezing and the extent to which adding fever to the criteria alters their performance. Design Prospective classification of 390 children aged 2-59 months with lower respiratory tract disease into five diagnostic categories, including pneumonia. WHO criteria for the identification of pneumonia and a set of such criteria modified by adding fever were compared with radio-graphically diagnosed pneumonia as the gold standard. Results The sensitivity of the WHO criteria was 94% for children aged <24 months and 62% for those aged >= 24 months. The corresponding specificities were 20% and 16%. Adding fever to the WHO criteria improved specificity substantially (to 44% and 50%, respectively). The specificity of the WHO criteria was poor for children with wheezing (12%). Adding fever improved this substantially (to 42%). The addition of fever to the criteria apparently reduced their sensitivity only marginally (to 92% and 57%, respectively, in the two age groups). Conclusion The authors' results reaffirm that the current WHO criteria can detect pneumonia with high sensitivity, particularly among younger children. They present evidence that the ability of these criteria to distinguish between children with pneumonia and those with wheezing diseases might be greatly enhanced by the addition of fever.

KINETICS VERSUS HYDRODYNAMICS: GENERALIZATION OF LANDAU/COOPER-FRYE PRESCRIPTION FOR FREEZE-OUT

The problem of spectra formation in hydrodynamic approach to A + A collisions is considered within the Boltzmann equations. It is shown analytically and illustrated by numerical calculations that the particle momentum spectra can be presented in the Cooper-R-ye form despite freeze-out is not sharp and has the finite temporal width. The latter is equal to the inverse of the particle collision rate at points (t(sigma) (r, p), r) of the maximal emission at a fixed momentum p. The set of these points forms the hypersurfaces t(sigma)(r,p) which strongly depend on the values of p and typically do not enclose completely the initially dense matter. This is an important difference from the standard Cooper-Frye prescription (CFp), with a common freeze-out hypersurface for all p, that affects significantly the predicted spectra. Also, the well known problem of CFp as for negative contributions to the spectra from non-space-like parts of the freeze-out hypersurface is naturally eliminated in this improved prescription.

Crystallisation behaviour and glass-forming ability in Al-La-Ni system

The crystallisation behaviour for alloys in the Al-rich corner in the Al-La-Ni system is reported in this paper Alloys were selected based on the topological instability criterion (lambda criterion) calculated from the alloy composition and metallic radii of the alloying elements and aluminum Amorphous ribbons were produced by melt-spinning and the crystallisation reactions were analysed by X-ray diffraction and calorimetry The results showed that increasing the values of lambda from 0.072 to 0.16 resulted in the following changes in the crystallisation behaviour, as predicted by the lambda criterion (a) nanocrystallisation of alpha-Al for the alloy composition corresponding to lambda = 0 072 and (b) detection of the glass transition temperature, T(g), for the alloys with composition close to lambda approximate to 0.1 line. For compositions corresponding to both ends of the lambda approximate to 0 1 line (near the binaries lines) T(g) could be detected only in the ""intermediary"" central region, and the alloy we produced in this region was considered the best glass former for the Al-rich corner Also, except for the alloys with the highest NI content, crystallisation proceeded by two distinct exothermic peaks which are typical of nanocrystallisation transformation. These behaviours are discussed in terms of compositional (lambda parameter) and topological aspects to account for cluster formation in the amorphous phase. Crown Copyright (C) 2009 Published by Elsevier B V All rights reserved

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.

A new correlation between electronic parameters and glass forming ability of metallic alloys

A quantitative correlation between the glass forming ability and the electronic parameters of metallic alloys is presented. It is found that the critical cooling rate for glass formation (R(c)) correlates well with the average work function difference (Delta phi) and the average electron density difference (Delta n(ws)(1/3)) among the constituent elements of the investigated alloys. A correlation coefficient (R(2)) of 0.77 was found for 68 alloys in 30 metallic systems, which is better than the previous proposed correlation between the glass forming ability and the average Pauling electronegativity difference.

Topological instability, average electronegativity difference and glass forming ability of amorphous alloys

In this paper, we report the remarkable agreement of the glass forming ability of binary alloys with a new criterion that combines the topological instability parameter (lambda) and the average electronegativity difference among the elements of an alloy, assuming both exert a synergetic effect. The best glass forming compositions for Zr-Cu and Ti-Ni systems are well predicted by this new approach. Although the new criterion needs further refinement, it is concluded that the proposed approach is a promising and simple tool to guide and reduce the tedious and labour intensive work to find good glass former compositions in metallic systems. (C) 2008 Elsevier Ltd. All rights reserved.

Thermodynamic and topological instability approaches for forecasting glass-forming ability in the ternary Al-Ni-Y system

A thermodynamic approach to predict bulk glass-forming compositions in binary metallic systems was recently proposed. In this approach. the parameter gamma* = Delta H-amor/(Delta H-inter - Delta H-amor) indicates the glass-forming ability (GFA) from the standpoint of the driving force to form different competing phases, and Delta H-amor and Delta H-inter are the enthalpies for-lass and intermetallic formation, respectively. Good glass-forming compositions should have a large negative enthalpy for glass formation and a very small difference for intermetallic formation, thus making the glassy phase easily reachable even under low cooling rates. The gamma* parameter showed a good correlation with GFA experimental data in the Ni-Nb binary system. In this work, a simple extension of the gamma* parameter is applied in the ternary Al-Ni-Y system. The calculated gamma* isocontours in the ternary diagram are compared with experimental results of glass formation in that system. Despite sonic misfitting, the best glass formers are found quite close to the highest gamma* values, leading to the conclusion that this thermodynamic approach can lie extended to ternary systems, serving as a useful tool for the development of new glass-forming compositions. Finally the thermodynamic approach is compared with the topological instability criteria used to predict the thermal behavior of glassy Al alloys. (C) 2007 Elsevier B. V. All rights reserved.

Topological instability and electronegativity effects on the glass-forming ability of metallic alloys

The glass-forming ability (GFA) of metallic alloys is associated with a topological instability criterion combined with a new parameter based on the average electronegativity difference of an element and its surrounding neighbours. In this model, we assume that during solidification the glassy phase competes directly with the supersaturated solid solution having the lowest topological instability factor for a given composition. This criterion is combined with the average electronegativity difference among the elements in the alloy, which reflects the strength of the liquid. The GFA is successfully correlated with this combined criterion in several binary glass-forming systems.

A Family of Autoconnected Transformers for 12-and 18-Pulse Converters-Generalization for Delta and Wye Topologies

Multipulse rectifier topologies based on autoconnections are increasingly applied as interface stages between mains and power electronics converters. These topologies are attractive and cost-effective solutions for meeting the requirements of low total harmonic distortion of line current and high power factor. Furthermore, as only a small fraction of the total power required by the load is processed in the magnetic core, the overall resulting volume and weight are reduced. This paper proposes a mathematical analysis based on phasor diagrams that results in a single and general expression capable of unifying all delta and wye step-up or step-down autotransformer connections for 12-and 18-pulse ac-dc converters. The expression obtained allows the choice of a wide range of input/output voltage ratio for step-up or step-down autotransformer, and this general expression is also presented in a graphical form for each converter. Moreover, it simplifies the procedure for determining turn ratios and polarities for all windings of the autotransformer. A routine for easy and fast calculations is developed and validated by a design example. Finally, experimental results are presented along with comments on a 6-kW 220-V line voltage, 400-V rectified voltage, and 18-pulse delta-autoconnected prototype.

A generalized modified Weibull distribution for lifetime modeling

A four parameter generalization of the Weibull distribution capable of modeling a bathtub-shaped hazard rate function is defined and studied. The beauty and importance of this distribution lies in its ability to model monotone as well as non-monotone failure rates, which are quite common in lifetime problems and reliability. The new distribution has a number of well-known lifetime special sub-models, such as the Weibull, extreme value, exponentiated Weibull, generalized Rayleigh and modified Weibull distributions, among others. We derive two infinite sum representations for its moments. The density of the order statistics is obtained. The method of maximum likelihood is used for estimating the model parameters. Also, the observed information matrix is obtained. Two applications are presented to illustrate the proposed distribution. (C) 2008 Elsevier B.V. All rights reserved.

Effect of Lactobacillus rhamnosus GR-1 and Lactobacillus reuteri RC-14 on the ability of Candida albicans to infect cells and induce inflammation

Vulvovaginal candidiasis, a high prevailing infection worldwide, is mainly caused by Candida albicans. Probiotic Lactobacillus reuteri RC-14 and Lactobacillus rhamnosus GR-1 have been previously shown to be useful as adjuvants in the treatment of women with VVC. In order to demonstrate and better understand the anti-Candida activity of the probiotic microorganisms in an in vitro model simulating vaginal candidiasis, a human vaginal epithelial cell line (VK2/E6E7) was infected with C. albicans 3153a and then challenged with probiotic L. rhamnosus GR-1 and/or L. reuteri RC-14 or their respective CFS (alone or in combination). At each time point (0, 6, 12 and 24 hr), numbers of yeast, lactobacilli and viable VK2/E6E7 cells were determined and, at 0, 6 and 12 hr, the supernatants were measured for cytokine levels. We found that C. albicans induced a significant increase in IL-1 alpha and IL-8 production by VK2/E6E7 cells. After lactobacilli challenge, epithelial cells did not alter IL-6, IL-1 alpha, RANTES and VEGF levels. However, CFS from the probiotic microorganisms up-regulated IL-8 and IP-10 levels secreted by VK2/E6E7 cells infected with C. albicans. At 24 hr of co-incubation, L. reuteri RC-14 alone and in combination with L. rhamnosus GR-1 decreased the yeast population recoverable from the cells. In conclusion, L. reuteri RC-14 alone and together with L. rhamnosus GR-1 have the potential to inhibit the yeast growth and their CFS may up-regulate IL-8 and IP-10 secretion by VK2/E6E7 cells, which could possibly have played an important role in helping to clear VVC in vivo.

Measurement of functional ability following traumatic brain injury using the Clinical Outcomes Variable Scale: A reliability study

This study determined the inter-tester and intra-tester reliability of physiotherapists measuring functional motor ability of traumatic brain injury clients using the Clinical Outcomes Variable Scale (COVS). To test inter-tester reliability, 14 physiotherapists scored the ability of 16 videotaped patients to execute the items that comprise the COVS. Intra-tester reliability was determined by four physiotherapists repeating their assessments after one week, and three months later. The intra-class correlation coefficients (ICC) were very high for both inter-tester reliability (ICC > 0.97 for total COVS scores, ICC > 0.93 for individual COVS items) and intra-tester reliability (ICC > 0.97). This study demonstrates that physiotherapists are reliable in the administration of the COVS.

The case for the ability-based model of emotional intelligence in organizational behavior

In this second counterpoint article, we refute the claims of Landy, Locke, and Conte, and make the more specific case for our perspective, which is that ability-based models of emotional intelligence have value to add in the domain of organizational psychology. In this article, we address remaining issues, such as general concerns about the tenor and tone of the debates on this topic, a tendency for detractors to collapse across emotional intelligence models when reviewing the evidence and making judgments, and subsequent penchant to thereby discount all models, including the ability-based one, as lacking validity. We specifically refute the following three claims from our critics with the most recent empirically based evidence: (1) emotional intelligence is dominated by opportunistic academics-turned-consultants who have amassed much fame and fortune based on a concept that is shabby science at best; (2) the measurement of emotional intelligence is grounded in unstable, psychometrically flawed instruments, which have not demonstrated appropriate discriminant and predictive validity to warrant/justify their use; and (3) there is weak empirical evidence that emotional intelligence is related to anything of importance in organizations. We thus end with an overview of the empirical evidence supporting the role of emotional intelligence in organizational and social behavior.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).