Embedded holonic fault diagnosis of complex transportation systems

The use of electronic equipment and embedded computing technologies in modern complex transportation systems continues to grow in a highly competitive market, in which product maintainability and availability is vital. These technological advances also make fault diagnosis and maintenance interventions much more challenging, since these operations require a deep understanding of the entire system. This paper proposes a holonic cooperative fault diagnosis approach, along with a generic architecture, to increase the embedded diagnosis capabilities of complex transportation systems. This concept is applied to the fault diagnosis of door systems of a railway transportation system.     

Time series classification for the prediction of dialysis in critically ill patients using echo statenetworks

Objective: Time series often appear in medical databases, but only few machine learning methods exist that process this kind of data properly. Most modeling techniques have been designed with a static data model in mind and are not suitable for coping with the dynamic nature of time series. Recurrent neural networks (RNNs) are often used to process time series, but only a few training algorithms exist for RNNs which are complex and often yield poor results. Therefore, researchers often turn to traditional machine learning approaches, such as support vector machines (SVMs), which can easily be set up and trained and combine them with feature extraction (FE) and selection (FS) to process the high-dimensional temporal data. Recently, a new approach, called echo state networks (ESNs), has been developed to simplify the training process of RNNs. This approach allows modeling the dynamics of a system based on time series data in a straightforwardway.The objective of this study is to explore the advantages of using ESN instead of other traditional classifiers combined with FE and FS in classification problems in the intensive care unit (ICU) when the input data consists of time series. While ESNs have mostly been used to predict the future course of a time series, we use the ESN model for classification instead. Although time series often appear in medical data, little medical applications of ESNs have been studiedyet.Methods and material: ESN is used to predict the need for dialysis between the fifth and tenth day after admission in the ICU. The input time series consist of measured diuresis and creatinine values during the first 3days after admission. Data about 830 patients was used for the study, of which 82 needed dialysis between the fifth and tenth day after admission. ESN is compared to traditional classifiers, a sophisticated and a simple one, namely support vector machines and the naive Bayes (NB) classifier. Prior to the use of the SVM and NB classifier, FE and FS is required to reduce the number of input features and thus alleviate the curse dimensionality. Extensive feature extraction was applied to capture both the overall properties of the time series and the correlation between the different measurements in the time series. The feature selection method consists of a greedy hybrid filter-wrapper method using a NB classifier, which selects in each iteration the feature that improves prediction the best and shows little multicollinearity with the already selected set. Least squares regression with noise was used to train the linear readout function of the ESN to mitigate sensitivity to noise and overfitting. Fisher labeling was used to deal with the unbalanced data set. Parameter sweeps were performed to determine the optimal parameter values for the different classifiers. The area under the curve (AUC) and maximum balanced accuracy are used as performance measures. The required execution time was also measured.Results: The classification performance of the ESN shows significant difference at the 5% level compared to the performance of the SVM or the NB classifier combined with FE and FS. The NB+FE+FS, with an average AUC of 0.874, has the best classification performance. This classifier is followed by the ESN, which has an average AUC of 0.849. The SVM+FE+FS has the worst performance with an average AUC of 0.838. The computation time needed to pre-process the data and to train and test the classifier is significantly less for the ESN compared to the SVM andNB.Conclusion: It can be concluded that the use of ESN has an added value in predicting the need for dialysis through the analysis of time series data. The ESN requires significantly less processing time, needs no domain knowledge, is easy to implement, and can be configured using rules ofthumb.     

Multi-month memory effects on early summer vegetative activity in semi-arid South Africa and their spatial heterogeneity

In semi-arid African regions (annual rainfall between 200 and 600 mm), variability of vegetative activity is mainly due to the rainfall of the current rainy season. In most of South Africa, the rainy season occurs from October to March. On average, vegetative activity lags rainfall by 1 to 2 months. The interannual variability in early summer (December to September) normalized difference vegetation index (NDVI) depends primarily on precipitation at the beginning (October to November) of the rainy season. However, once this primary control is removed, the residual interannual variability in NDVI highlights a double memory effect: a 1-year effect, referred to as Mem₁, and a 7- to 10-month effect, referred to as Mem₂. This article aims at better describing the influence of soil and vegetation characteristics on these two memory effects. The data sets used in this study are as follows: (1) a 19-year NDVI time series from National Oceanic and Atmospheric Administration (NOAA) satellites, (2) rainfall records from a network of 1160 rain-gauge stations compiled by the Water Research Commission (WRC), (3) vegetation types from Global Land Cover (GLC) 2000 and (4) soil characteristics from the soil and terrain database for Southern Africa (SOTERSAF). Results indicate that among 20–30% of NDVI variance that is not explained by the concurrent rainfall, one-third is explained by the two memory effects. Mem₁ is found to have maximum effect in the northwest of our study domain, near the Botswana boundary, in the South Kalahari. Associated conditions are open grasslands growing on Arenosols. Mem₁ is less important in the southeast, particularly in open grassland with shrubs growing on Cambisols. Thus, Mem₁ mainly depends on soil texture. Mem₂ is more widespread and its influence is the greatest in the centre, the south and the east of our domain. It is related to rainfall from January to April, which controls, beyond the intervening dry season, the interannual variations of NDVI (December to September) at the beginning of the next rainy season. Through these new findings, this article emphasizes again the high potential of remote-sensing techniques to monitor and understand the dynamics of semi-arid environments.     

In vitro interactions between DMSA-coated maghemite nanoparticles and human fibroblasts: A physicochemical and cyto-genotoxical study

Although the current production of oxide nanoparticles may be modest, the wide range of proposed applications and forecasted growth in production has raised questions about the potential impact of these nanoparticles on the environment and human health. Iron oxide nanoparticles have been proposed for an increasing number of biomedical applications although in vitro toxicity depending on the particles coating has been evidenced. The aim of this study was to examine the potential in vitro cyto- and genotoxicity on human dermal fibroblasts of DMSA-coated maghemite nanoparticles (NmDMSA) as a function of well-defined physicochemical states. Well-stabilized NmDMSA produced weak cytotoxic and no genotoxic effects. This is attributed in part to the DMSA coating, which serves as a barrier for a direct contact between nano-oxide and fibroblasts, inhibiting a potential toxic effect.     

Neutron Scattering from the Flux Line Lattice in the Cubic (K, Ba)BiO 3 Superconductor

Small Angle Neutron Scattering has been used to observe diffraction from the flux line lattice in the fully isotropic (K, Ba)BiO ₃ high T _c superconductor. We show that the flux lines are ordered into a hexagonal lattice at low field (H < H _e 0.6T H _g (0) where H _g is the second order vortex glass transition field). The diffracted intensity continuously drops to zero as the field is increased towards H _e . Those observations are in good agreement with recent theoritical works which suggested that the formation of dislocations becomes favorable above H _e leading to a glassy structure. The field H _e lies close to the onset of the second peak in magnetization measurements indicating that this peak is related to the change in the vortex structure.     

Investigation on Convective Drying of Mixtures of Sewage Sludge and Sawdust in a Fixed Bed

This work is part of a project aiming to develop a renewable fuel for gasification purposes, through convective drying of sludge/wood mixtures. The first step consists of characterizing the behavior of sawdust/sludge mixtures during the application of convective drying. The influence of the mixing step (no mixing versus 30 s at 40 rpm), as well as the sawdust : sludge ratio (1:9, 2:8, 3:7, and 4:6 on a dry basis) and the effect of drying temperature (50, 80, and 110°C) were investigated. In this study, X-ray tomography, a noninvasive imaging technique, is used to assess changes in the volume, void, and exchange surface at the beginning and the end of the drying process. Results first confirm the importance of the mixing step on the drying behavior: the drying rate of the mixed sludge is lower than that of the original sludge. Nevertheless, the addition of sawdust is shown to have a positive impact on the drying process from a mass ratio of 2:8, with drying rates higher than that of the original sludge. With increasing amounts of sawdust, the initial and final bed volumes, initial and final total exchange surfaces, and initial void fraction increase linearly, but the bed volume shrinkage and final void fraction decrease linearly.     

Profiling hospitals based on emergency readmission: a multilevel transition modelling approach

Emergency readmission is seen as an important part of the United Kingdom government policy to improve the quality of care that patients receive. In this context, patients and the public have the right to know how well different health organizations are performing. Most methods for profiling estimate the expected numbers of adverse outcomes (e.g. readmission, mortality) for each organization. A number of statistical concerns have been raised, such as the differences in hospital sizes and the unavailability of relevant data for risk adjustment. Having recognized these statistical concerns, a new framework known as the multilevel transition model is developed. Hospital specific propensities of the first, second and further readmissions are considered to be measures of performance, where these measures are used to define a new performance index. During the period 1997 and 2004, the national (English) hospital episodes statistics dataset comprise more than 5 million patient readmissions. Implementing a multilevel model using the complete population dataset could possibly take weeks to estimate the parameters. To resolve the problem, we extract 1000 random samples from the original data, where each random sample is likely to lead to differing hospital performance measures. For computational efficiency a Grid implementation of the model is developed. Analysing the output from the full 1000 sample, we noticed that 4 out of the 5 worst performing hospitals treating cancer patients were in London. These hospitals are known to be the leading NHS Trusts in England, providing diverse range of services to complex patients, and therefore it is inevitable to expect higher numbers of emergency readmissions.     

Modulation of the enzymatic activity of papain by interdomain residues remote from the active site

The two main catalytic residues Cys25 and Hisl59 of the monomericcysteine protease papain are located on different walls of acleft formed by two domains. This topology suggests a possiblerelationship between relative domain organization and catalyticmechanism. The effect on enzymatic parameters of structuralmodifications at various locations of the twodomain interfaceof papain was examined by individual or double replacementsby Ala of pairs of interacting residues. Most modificationshad no effect on enzyme activity. However, the enzyme's substrateturnover (k_cat) decreased following simultaneous alterationof the two most conserved residues, forming an apolar contactlocated 15 Å away from the active site. The pH activityprofile of the double mutant was unchanged, indicating a conservedionization state of the active site thiolate-imidazolium ionpair. This state is strongly dependent on the distance separatingthe two residues, thus suggesting that the active site geometryhas not been significantly altered. Efficient enzymatic activityin papain requires more than a correct active site geometryand is influenced by domain packing properties in a region remotefrom the active site.     

Effects of 1,4-Benzoxazin-3-One Derivatives from Maize on Survival and Fecundity of Metopolophium dirhodum (Walker) on Artificial Diet

Effects of 2--D-glucopyranosyloxy-4-hydroxy-7-methoxy-1,4benzoxazin-3-one (DIMBOA-Glc) and DIMBOA-Glc N-O-methylated (HDMBOA-Glc), two compounds present in high concentration in maize, were tested on the aphid Metopolophium dirhodum reared on artificial diet. HDMBOA-Glc and DIMBOA-Glc decrease survival of adults with an LD₅₀ of 1 mM and 5.6 mM, respectively, after 72 hr of feeding. These compounds also decrease the fecundity of the aphids at concentrations of 2 mM and 1 mM, respectively. At concentrations of 2 mM HDMBOA-Glc and 8 mM DIMBOA-Glc in the diet, the average lifetime fecundity of 10 females is near zero. Offspring mortality on diet with 2 mM DIMBOA-Glc is significantly higher than with the control diet. In contrast, HDMBOA-Glc has no effect on the survival of offspring. The possibility that these compounds protect Poaceae against aphids is discussed.