Effects of fertilisation on the allyl isothiocyanate profile of above‐ground tissues of New Zealand‐grown wasabi

Wasabi (Wasabia japonica (Miq) Matsum) is a developing crop in New Zealand and is valued for its spicy taste and pungent smell. It is popular as a condiment for traditional and modern Japanese foods. However, the limited area suitable for wasabi production in Japan has resulted in inability to meet increasing market demand. Isothiocyanates (ITCs) are sulphur compounds responsible for the unique flavour of wasabi, with allyl isothiocyanate (AITC) being the main compound responsible for the pungency. In this study, AITC tissue concentration and yield were measured in three above‐ground tissues of the wasabi plant to investigate the effects of different fertiliser, manure and lime treatments. The highest tissue concentration of AITC was found in the rhizomes, ranging from 1564 to 3366 mg kg^?1 (fresh weight basis), while the petioles and leaves contained 254–373 and 453–643 mg kg^?1 respectively. Fertilisation with ammonium sulphate produced the highest‐quality rhizomes (72% increase in AITC yield) and petioles (64% increase), but the best response in the leaves (51%) resulted from the manure treatment. Nitrogen fertiliser alone reduced the AITC yield by up to 15%. These results should help in formulating guidelines for production of high‐quality wasabi tops containing high levels of AITC. © 2002 Society of Chemical Industry     

Studies on ion—solvent interactions through viscosity measurements—potassium iodide in N,N-dimethylformamide—water mixtures

The viscosities of DMF—water mixtures and of KI solutions in these mixed solvents have been measured at 25°, 30°, 35° and 40° C, covering the whole range of solvent composition. The Jones—Dole B-Coefficient, after passing through a minimum in water-rich solvent composition region, increases with the increase in DMF content in the solvent mixture. The variation of viscosity of the solvent mixture and of B-coefficient with solvent composition and temperature have been discussed in terms of solvent—solvent and ion—solvent interactions.     

Amelioration of photofermentative hydrogen production from molasses dark fermenter effluent by zeolite-based removal of ammonium ion

One of the challenges in the development of integrated dark and photofermentative biological hydrogen production systems is the presence of ammonium ions in dark fermentation effluent (DFE). Ammonium strongly inhibits the sequential photofermentation process, and so its removal is required for successful process integration. In this study, the removal of ammonium ions from molasses DFE using a natural zeolite (clinoptilolite) was investigated. The samples were treated with batch suspensions of Na-form clinoptilolite. The ammonium ion concentration could be reduced from 7.60 mM to 1.60 mM and from 12.30 mM to 2.40 mM for two different samples. Photofermentative hydrogen production on treated and untreated molasses DFE samples were investigated in batch photobioreactors by an uptake hydrogenase deleted (hup⁻) mutant strain of Rhodobacter capsulatus. Maximum hydrogen productivities of 1.11 mmol H₂/L_c·h and 1.16 mmol H₂/L_c·h and molar yields of 79% and 90% were attained in the treated DFE samples, while the untreated samples resulted in no hydrogen production. The results showed that ammonium ions in molasses DFE could be effectively removed using clinoptilolite by applying a cost-effective, simple batch process.     

Kinetic parameter estimation for supported vanadium oxide catalysts for propane ODH reaction: Effect of loading and support

Kinetic parameters are estimated for a sequential Mars van Krevelan (MVK) reaction model occurring over several supported vanadium oxide (vanadia) catalysts involved in the propane oxidative dehydrogenation (ODH) reaction. The estimated kinetic parameters, pre-exponential factors and activation energies, are used to understand the effect of vanadia loading and oxide support. The pre-exponential factors and vanadia normalized pre-exponential factors vary with vanadia loading and oxide support. The monotonic increase in normalized pre-exponential factors with vanadia loading and the variation of pre-exponential factors with oxide support appears to be related to the change in acidity/basicity of the catalyst and the redox nature of the catalyst, respectively. The activation energy for propene degradation does not significantly change with catalyst; however, the activation energy for propane oxidation is different for the V₂O₅/Al₂O₃ catalyst. It appears that two important considerations are required for the development of an efficient propane ODH catalyst: a high rate constant associated with the propane oxidation reaction, and a high ratio of the rate constant for propene formation to degradation reaction. Based on the observations in the present study it is proposed that a higher TiO₂ support surface area will assist in increasing the propane oxidation activity and propene yield.     

TRAPPY: a truthfulness and reliability aware application placement policy in fog computing

The Journal of Supercomputing - Fog computing facilitates the satisfaction of Internet of things (IoT) users by running time-sensitive offloaded applications. Heterogeneity, dynamism, and the...     

The restricted likelihood ratio test for autoregressive processes

The restricted likelihood is known to produce estimates with significantly less bias in AR(p) models with intercept and/or trend. In AR(1) models, the corresponding restricted likelihood ratio test (RLRT), unlike the t‐statistic or the usual LRT, has been recently shown to be well approximated by the chi‐square distribution even close to the unit root, thus yielding confidence intervals with good coverage properties. In this article, we extend this result to AR(p) processes of arbitrary order p by obtaining the expansion of the RLRT distribution around that of the limiting chi‐squared and showing that the error is bounded even as the unit root is approached. Next, we investigate the correspondence between the AR coefficients and the partial autocorrelations, which is well known in the stationary region, and extend to the more general situation of potentially multiple unit roots. In the case of one positive unit root, which is of most practical interest, the resulting parameter space is shown to be the bounded p‐dimensional hypercube (?1, 1] × (?1, 1)^p?1. This simple parameter space, in addition with a stable algorithm that we provide for computing the restricted likelihood, allows its easy computation and optimization as well as construction of confidence intervals for the sum of the AR coefficients. In simulations, the RLRT intervals are shown to have not only near exact coverage in keeping with our theoretical results, but also shorter lengths and significantly higher power against stationary alternatives than other competing interval procedures. An application to the well‐known Nelson–Plosser data yields RLRT based intervals that can be markedly different from those in the literature.     

The Slow Convergence of Ordinary Least Squares Estimators of α, β and Portfolio Weights under Long‐Memory Stochastic Volatility

We consider inference for the market model coefficients based on simple linear regression under a long memory stochastic volatility generating mechanism for the returns. We obtain limit theorems for the ordinary least squares (OLS) estimators of α and β in this framework. These theorems imply that the convergence rate of the OLS estimators is typically slower than if both the regressor and the predictor have long memory in volatility, where T is the sample size. The traditional standard errors of the OLS‐estimated intercept () and slope (), which disregard long memory in volatility, are typically too optimistic, and therefore the traditional t‐statistic for testing, say, α = 0 or β = 1, will diverge under the null hypothesis. We also obtain limit theorems (which imply slow convergence) for the estimated weights of the minimum variance portfolio and the optimal portfolio in the same framework. In addition, we propose and study the performance of a subsampling‐based approach to hypothesis testing for α and β. We conclude by noting that analogous results hold under more general conditions on long‐memory volatility models and state these general conditions which cover certain fractionally integrated exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models.     

Role of Air Gap in Scrap Dissolution Process

The role of the air gap formed between scrap and a solidified metal shell at the beginning of the scrap dissolution process has been investigated. The effect of air gap has been investigated for different Biot numbers (10 to 100) and ratios of air gap to liquid melt heat transfer coefficient (0.1 to 0.9). The predictions of the mathematical model are compared with the experimental results reported elsewhere. The role of air gap is found to be significant only for low Biot numbers (<50) and for low ratios of air gap to liquid melt heat transfer coefficient (<0.25).     

QoE Aware IoT Application Placement in Fog Computing Using Modified-TOPSIS

Mobile Networks and Applications - Over the years, fog computing has emerged as a paradigm to complement the cloud computing in handling the delay sensitive IoT applications in a better manner....     

Parallel algorithms for terminal-pair reliability

The parallelization of the two best-known sequential algorithms, that of W.P. Dotson and J.O. Gobein (1979) and that of L.B. Page and J.E. Perry (PP-F2TDN) (1989) for computing the terminal-pair reliability in a network is discussed. Reduce and Partition (R and P), a novel sequential algorithm which combines the best efficient features of these two algorithms, is presented. It is shown that R and P runs almost twice as fast as the previously known fastest algorithm. A parallel version of R and P is also presented. The execution times of all three parallel algorithms with various numbers of processors for different networks on the BBN Butterfly parallel computer are provided. The parallel algorithms were implemented on a shared-memory parallel computer. In R and P, the greedy approach was used in selecting shortest paths in order to locally minimize the number of subproblems. This selection did not consider the effect of reductions on the subproblems to be generated