MPLS Recovery Mechanisms for IP-over-WDM Networks

Due to the fast increase of Internet traffic and the enormous bandwidth potential of all-optical transport networks based on wavelength division multiplexing, an IP-over-WDM network scenario is likely to be widespread in future communication networks. At the same time, IP networks are becoming more and more mission-critical. Hence, it is of paramount importance for IP-over-WDM networks to be able to recover quickly from frequently occurring network failures. This paper explains how multi-protocol label switching (both electrical and optical) recovery mechanisms can be important to reach that goal. Moreover, a novel MPLS recovery mechanism called fast topology-driven constrained-based rerouting is presented. Different MPLS recovery mechanisms are compared to each other. Special attention hereby goes to the additional capacity that is required to recover from frequently occurring failures.     

Proteomic Study of Low-Birth-Weight Nephropathy in Rats

The hyperfiltration theory has been used to explain the mechanism of low birth weight (LBW)-related nephropathy. However, the molecular changes in the kidney proteome have not been defined in this disease, and early biomarkers are lacking. We investigated the molecular pathogenesis of LBW rats obtained by intraperitoneal injection of dexamethasone into pregnant animals. Normal-birth-weight (NBW) rats were used as controls. When the rats were four weeks old, the left kidneys were removed and used for comprehensive label-free proteomic studies. Following uninephrectomy, all rats were fed a high-salt diet until 9 weeks of age. Differences in the molecular composition of the kidney cortex were observed at the early step of LBW nephropathy pathogenesis. Untargeted quantitative proteomics showed that proteins involved in energy metabolism, such as oxidative phosphorylation (OXPHOS), the TCA cycle, and glycolysis, were specifically downregulated in the kidneys of LBW rats at four weeks. No pathological changes were detected at this early stage. Pathway analysis identified NEFL2 (NRF2) and RICTOR as potential upstream regulators. The search for biomarkers identified components of the mitochondrial respiratory chain, namely, ubiquinol-cytochrome c reductase complex subunits (UQCR7/11) and ATP5I/L, two components of mitochondrial F₁F_O-ATP synthase. These findings were further validated by immunohistology. At later stages of the disease process, the right kidneys revealed an increased frequency of focal segmental glomerulosclerosis lesions, interstitial fibrosis and tubular atrophy. Our findings revealed proteome changes in LBW rat kidneys and revealed a strong downregulation of specific mitochondrial respiratory chain proteins, such as UQCR7.     

C,N-chelated dicyclopentadienylzirconium complexes and their possible use as hydrogenation catalysts

In situ generated Cp₂Zr(n-Bu)Cl (6) reacts with {2-[(CH₃)₂NCH₂]C₆H₄}₂Pb to form exclusively {2-[(CH₃)₂NCH₂]C₆H₄}Cp₂ZrCl (7), [(CH₃)₂NCH₂]C₆H₅, butene and elemental lead. The further derivatization of chloride (7) to fluoride (8), hydride (9), methyl derivative (10), and a reduction of 7 are also described. The crystal structures of 7–10 were determined. The catalytic activity of 9 and 10 in hydrogenation of styrene was also preliminarily tested.     

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.     

DSC measurements and modelling of the kinetics of methane hydrate formation in water-in-oil emulsion

The kinetics of formation of clathrate hydrates of methane was investigated in a water-in-oil emulsion using high-pressure differential scanning calorimetry in the range 10-40 MPa, at various temperatures. At high driving force, the heat peak related to the formation of hydrates has a regular and symmetric shape, and its height and width depend on the gas pressure and sub cooling degree. At near equilibrium conditions, hydrate formation is delayed by more than 1 h, but is still clearly observable. A model based on crystal growth theory, coupled with a normal distribution of induction times to take into account the germination in a population of micro-sized droplets, is proposed to represent the hydrate formation rate versus time in the particular case of water-in-oil emulsions. It uses four parameters which appear strongly correlated to the experimental conditions: the growth rate constant, the over saturation of gas in the water phase, the average and standard deviation of the induction time distribution.     

A new quasi-steady method to measure gas permeability of weakly permeable porous media

A new quasi-steady method for the determination of the apparent gas permeability of porous materials is presented in this paper along with the corresponding interpretative physical model derived from the unsteady flow equations. This method is mainly dedicated to the measurement of very low permeability of thin porous media, although thicker but more permeable samples may also be analyzed. The method relies on quasi-steady flow resulting from a (quasi) constant pressure maintained at the inlet face of the sample. Gas flow-rate, as low as 3 × 10(-10) m(3)/s, is determined from the record of pressure increase in a reservoir connected to the outlet face of the sample. An estimate of the characteristic time, t(c), to reach quasi-steady flow after imposing a constant pressure at the inlet is derived. It is validated by direct numerical simulations of the complete unsteady flow, clearly defining the required experimental duration for the method to apply. Experimental results obtained on rather permeable and thick rock samples are reported showing an excellent agreement of the measured permeability with that determined independently on the same sample whereas the experimental value of t(c) is also in very good agreement with the predicted one. The method is further employed on a composite material sheet allowing the identification of an apparent gas permeability of about 10(-23) m(2).     

Fuzzy constraints in job-shop scheduling

This paper proposes an extension of the constraint-based approach to job-shop scheduling, that accounts for the flexibility of temporal constraints and the uncertainty of operation durations. The set of solutions to a problem is viewed as a fuzzy set whose membership function reflects preference. This membership function is obtained by an egalitarist aggregation of local constraint-satisfaction levels. Uncertainty is qualitatively described in terms of possibility distributions. The paper formulates a simple mathematical model of job-shop scheduling under preference and uncertainty, relating it to the formal framework of constraint-satisfaction problems in artificial intelligence. A combinatorial search method that solves the problem is outlined, including fuzzy extensions of well-known look-ahead schemes.This paper is partially based on the Ph.D. dissertation of the second author. Preliminary versions have been presented at the IJCAI'93 Workshop on Knowledge-Based Scheduling, Chambéry, France, September 1993, and at the EURO XIII/OR 36 Conference, Glasgow, UK, July 1994.     

Method for the Design of a Contact Dryer-Application to Sludge Treatment in Thin Film Boiling

A method, based on the analysis of heat transfer, is developed for the design of contact dryers in thin film boiling for sludge treatment. An experimental device was built in order to estimate the heat flux density exchanged between a hot metallic plate and the drying sample. This specific device is made of a thick copper plate heated at a temperature above 100°C, on which the sludge is coated. The contact temperature and the heat flux density are estimated using a temperature sensor inside the copper block, and an inverse conduction method (Beck's sequential function specification method). The corresponding drying curve is deduced from an energy balance. Sludge drying was studied on an electrically heated laboratory continuous drum dryer. A simple steady state model of the drum dryer was developed by taking into account the combined effects of internal and external transfers. This model is validated by comparison with drying curves obtained from sampling and simulation. In this case, the relative importance of internal and external heat transfers is highlighted.