Nutritional rehabilitation in cystic fibrosis: A 5 year follow-up study

Previously, we reported catch-up weight gain, growth, and improved lung function in a group of malnourished cystic fibrosis (CF) children receiving aggressive nutritional supplementation for 1 year compared with a forced expiratory volume in 1 s (FEV1)-, height-, and sex-matched comparison group receiving standard therapy. To evaluate long-term effects, the clinical progress of both groups has been studied over a 5 year period. The supplemented group (n = 10) received supplements for a median of 1.35 years to achieve nutritional rehabilitation. Compared with the nonsupplemented group (n = 14), the previously supplemented group had lower mortality (2 vs. 4, N.S.) and significantly greater weight and height z scores at 4 and 5 years. The progression of pulmonary function abnormalities as measured by FEV, and forced vital capacity (FVC) slopes was greater at 3 years in the nonsupplemented group (FEV1, p < 0.05) but no significant differences in rates of deterioration of pulmonary function were seen after 5 years in the two groups of survivors. We conclude that intensive nutritional support for 1 year has both short-and long-term effects on nutrition and growth, still evident some years after the cessation of this therapeutic modality. Supplementation for periods of longer than 1 year may produce greater gains and possibly prolong the improvement in pulmonary function observed in the earlier study. © 1992 Raven Press, Ltd., New York.

Linear filtering in DCT IV/DST IV and MDCT/MDST domain

In this paper, expressions for convolution multiplication properties of DCT IV and DST IV are derived starting from equivalent DFT representations. Using these expressions methods for implementing linear filtering through block convolution in the DCT IV and DST IV domain are proposed. Techniques developed for DCT IV and DST IV are further extended to MDCT and MDST where the filter implementation is near exact for symmetric filters and approximate for non-symmetric filters. No additional overlapping is required for implementing the symmetric filtering in the MDCT domain and hence the proposed algorithm is computationally competitive with DFT based systems. Moreover, inherent 50% overlap between the adjacent frames used for MDCT/MDST domain reduces the blocking artifacts due to block processing or quantization. The techniques are computationally efficient for symmetric filters and provides a new alternative to DFT based convolution.

Comparative immunogenicity of M. hyopneumoniae NrdF encoded in different expression systems delivered orally via attenuated S. typhimurium aroA in mice.

The Mycoplasma hyopneumoniae ribonucleotide reductase R2 subunit (NrdF) gene fragment was cloned into eukaryotic and prokaryotic expression vectors and its immunogenicity evaluated in mice immunized orally with attenuated Salmonella typhimurium aroA CS332 harboring either of the recombinant expression plasmids. We found that NrdF is highly conserved among M. hyopneumoniae strains. The immunogenicity of NrdF was examined by analyzing antibody responses in sera and lung washes, and the cell-mediated immune (CMI) response was assessed by determining the INF-[gamma] level produced by splenocytes upon in vitro stimulation with NrdF antigen. S. typhimurium expressing NrdF encoded by the prokaryotic expression plasmid (pTrcNrdF) failed to elicit an NrdF-specific serum or secretory antibody response, and IFN-[gamma] was not produced. Similarly, S. typhimurium carrying the eukaryotic recombinant plasmid encoding NrdF (pcNrdF) did not induce a serum or secretory antibody response, but did elicit significant NrdF-specific IFN-[gamma] production, indicating induction of a CMI response. However, analysis of immune responses against the live vector S. typhimurium aroA CS332 showed a serum IgG response but no mucosal IgA response in spite of its efficient invasiveness in vitro. In the present study we show that the DNA vaccine encoding the M. hyopneumoniae antigen delivered orally via a live attenuated S. typhimurium aroA can induce a cell-mediated immune response. We also indicate that different live bacterial vaccine carriers may have an influence on the type of the immune response induced.

Probabilistic load flow for distribution systems with uncertain PV generation

Large integration of solar Photo Voltaic (PV) in distribution network has resulted in over-voltage problems. Several control techniques are developed to address over-voltage problem using Deterministic Load Flow (DLF). However, intermittent characteristics of PV generation require Probabilistic Load Flow (PLF) to introduce variability in analysis that is ignored in DLF. The traditional PLF techniques are not suitable for distribution systems and suffer from several drawbacks such as computational burden (Monte Carlo, Conventional convolution), sensitive accuracy with the complexity of system (point estimation method), requirement of necessary linearization (multi-linear simulation) and convergence problem (Gram–Charlier expansion, Cornish Fisher expansion). In this research, Latin Hypercube Sampling with Cholesky Decomposition (LHS-CD) is used to quantify the over-voltage issues with and without the voltage control algorithm in the distribution network with active generation. LHS technique is verified with a test network and real system from an Australian distribution network service provider. Accuracy and computational burden of simulated results are also compared with Monte Carlo simulations.

Digital elevation model error in terrain analysis

Digital elevation models (DEMs) have been an important topic in geography and surveying sciences for decades due to their geomorphological importance as the reference surface for gravita-tion-driven material flow, as well as the wide range of uses and applications. When DEM is used in terrain analysis, for example in automatic drainage basin delineation, errors of the model collect in the analysis results. Investigation of this phenomenon is known as error propagation analysis, which has a direct influence on the decision-making process based on interpretations and applications of terrain analysis. Additionally, it may have an indirect influence on data acquisition and the DEM generation. The focus of the thesis was on the fine toposcale DEMs, which are typically represented in a 5-50m grid and used in the application scale 1:10 000-1:50 000. The thesis presents a three-step framework for investigating error propagation in DEM-based terrain analysis. The framework includes methods for visualising the morphological gross errors of DEMs, exploring the statistical and spatial characteristics of the DEM error, making analytical and simulation-based error propagation analysis and interpreting the error propagation analysis results. The DEM error model was built using geostatistical methods. The results show that appropriate and exhaustive reporting of various aspects of fine toposcale DEM error is a complex task. This is due to the high number of outliers in the error distribution and morphological gross errors, which are detectable with presented visualisation methods. In ad-dition, the use of global characterisation of DEM error is a gross generalisation of reality due to the small extent of the areas in which the decision of stationarity is not violated. This was shown using exhaustive high-quality reference DEM based on airborne laser scanning and local semivariogram analysis. The error propagation analysis revealed that, as expected, an increase in the DEM vertical error will increase the error in surface derivatives. However, contrary to expectations, the spatial au-tocorrelation of the model appears to have varying effects on the error propagation analysis depend-ing on the application. The use of a spatially uncorrelated DEM error model has been considered as a 'worst-case scenario', but this opinion is now challenged because none of the DEM derivatives investigated in the study had maximum variation with spatially uncorrelated random error. Sig-nificant performance improvement was achieved in simulation-based error propagation analysis by applying process convolution in generating realisations of the DEM error model. In addition, typology of uncertainty in drainage basin delineations is presented.

Boundedness of weakly singular integral operators on domains

The monograph dissertation deals with kernel integral operators and their mapping properties on Euclidean domains. The associated kernels are weakly singular and examples of such are given by Green functions of certain elliptic partial differential equations. It is well known that mapping properties of the corresponding Green operators can be used to deduce a priori estimates for the solutions of these equations. In the dissertation, natural size- and cancellation conditions are quantified for kernels defined in domains. These kernels induce integral operators which are then composed with any partial differential operator of prescribed order, depending on the size of the kernel. The main object of study in this dissertation being the boundedness properties of such compositions, the main result is the characterization of their Lp-boundedness on suitably regular domains. In case the aforementioned kernels are defined in the whole Euclidean space, their partial derivatives of prescribed order turn out to be so called standard kernels that arise in connection with singular integral operators. The Lp-boundedness of singular integrals is characterized by the T1 theorem, which is originally due to David and Journé and was published in 1984 (Ann. of Math. 120). The main result in the dissertation can be interpreted as a T1 theorem for weakly singular integral operators. The dissertation deals also with special convolution type weakly singular integral operators that are defined on Euclidean spaces.

Changes in CO2 emissions over business cycle recessions and expansions in the United States: A decomposition analysis

This paper examines the asymmetry of changes in CO2 emissions over business cycle recessions and expansions using yearly data from 1949 and monthly data from 1973 for the United States (US). In addition, decomposition analysis is applied to investigate the relative roles of various proximate contributing factors to observed changes in total and per capita CO2 emissions and emissions intensity, over business cycle phases. The results suggest, inter alia, that aggregate emissions and emissions intensity reduce much faster in contractions than they increase in expansions. In addition, unlike the three previous expansions, in the most recent post-GFC US expansion, emissions per capita have continued to decline, and at a rate very similar to the rate of reduction in preceding contractions. This suggests the real possibility that the most recent contraction may have had an ongoing impact on the path of per capita emissions well beyond the immediate impact experienced during the contraction itself.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

Stationary processes for object detection and non-rigid structure-from-motion

Stationary processes are random variables whose value is a signal and whose distribution is invariant to translation in the domain of the signal. They are intimately connected to convolution, and therefore to the Fourier transform, since the covariance matrix of a stationary process is a Toeplitz matrix, and Toeplitz matrices are the expression of convolution as a linear operator. This thesis utilises this connection in the study of i) efficient training algorithms for object detection and ii) trajectory-based non-rigid structure-from-motion.

A Legendre spectral element model for sloshing and acoustic analysis in nearly incompressible fluids

A new spectral finite element formulation is presented for modeling the sloshing and the acoustic waves in nearly incompressible fluids. The formulation makes use of the Legendre polynomials in deriving the finite element interpolation shape functions in the Lagrangian frame of reference. The formulated element uses Gauss-Lobatto-Legendre quadrature scheme for integrating the volumetric stiffness and the mass matrices while the conventional Gauss-Legendre quadrature scheme is used on the rotational stiffness matrix to completely eliminate the zero energy modes, which are normally associated with the Lagrangian FE formulation. The numerical performance of the spectral element formulated here is examined by doing the inf-sup test oil a standard rectangular rigid tank partially filled with liquid The eigenvalues obtained from the formulated spectral element are compared with the conventional equally spaced node locations of the h-type Lagrangian finite element and the predicted results show that these spectral elements are more accurate and give superior convergence The efficiency and robustness of the formulated elements are demonstrated by solving few standard problems involving free vibration and dynamic response analysis with undistorted and distorted spectral elements. and the obtained results are compared with available results in the published literature (C) 2009 Elsevier Inc All rights reserved

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Measurement of the top quark mass with dilepton events selected using neuroevolution at CDF

We report a measurement of the top quark mass $M_t$ in the dilepton decay channel $t\bar{t}\to b\ell'^{+}\nu'_\ell\bar{b}\ell^{-}\bar{\nu}_{\ell}$. Events are selected with a neural network which has been directly optimized for statistical precision in top quark mass using neuroevolution, a technique modeled on biological evolution. The top quark mass is extracted from per-event probability densities that are formed by the convolution of leading order matrix elements and detector resolution functions. The joint probability is the product of the probability densities from 344 candidate events in 2.0 fb$^{-1}$ of $p\bar{p}$ collisions collected with the CDF II detector, yielding a measurement of $M_t= 171.2\pm 2.7(\textrm{stat.})\pm 2.9(\textrm{syst.})\mathrm{GeV}/c^2$.

Measurement of the Top-Quark Mass with Dilepton Events Selected Using Neuroevolution at CDF

We report a measurement of the top quark mass $M_t$ in the dilepton decay channel $t\bar{t}\to b\ell'^{+}\nu'_\ell\bar{b}\ell^{-}\bar{\nu}_{\ell}$. Events are selected with a neural network which has been directly optimized for statistical precision in top quark mass using neuroevolution, a technique modeled on biological evolution. The top quark mass is extracted from per-event probability densities that are formed by the convolution of leading order matrix elements and detector resolution functions. The joint probability is the product of the probability densities from 344 candidate events in 2.0 fb$^{-1}$ of $p\bar{p}$ collisions collected with the CDF II detector, yielding a measurement of $M_t= 171.2\pm 2.7(\textrm{stat.})\pm 2.9(\textrm{syst.})\mathrm{GeV}/c^2$.

Bounded-input/bounded-output stability of linear multidimensional time- varying systems

The present study of the stability of systems governed by a linear multidimensional time-varying equation, which are encountered in spacecraft dynamics, economics, demographics, and biological systems, gives attention the lemma dealing with L(inf) stability of an integral equation that results from the differential equation of the system under consideration. Using the proof of this lemma, the main result on L(inf) stability is derived according; a corollary of the theorem deals with constant coefficient systems perturbed by small periodic terms. (O.C.)

PIV studies of large scale structures in the near field of small aspect ratio elliptic jets

The near flow field of small aspect ratio elliptic turbulent free jets (issuing from nozzle and orifice) was experimentally studied using a 2D PIV. Two point velocity correlations in these jets revealed the extent and orientation of the large scale structures in the major and minor planes. The spatial filtering of the instantaneous velocity field using Gaussian convolution kernel shows that while a single large vortex ring circumscribing the jet seems to be present at the exit of nozzle, the orifice jet exhibited a number of smaller vortex ring pairs close to jet exit. The smaller length scale observed in the case of the orifice jet is representative of the smaller azimuthal vortex rings that generate axial vortex field as they are convected. This results in the axis-switching in the case of orifice jet and may have a mechanism different from the self induction process as observed in the case of contoured nozzle jet flow.