A model-based assessment of the effects of projected climate change on the water resources of Jordan

This paper is concerned with the quantification of the likely effect of anthropogenic climate change on the water resources of Jordan by the end of the twenty-first century. Specifically, a suite of hydrological models are used in conjunction with modelled outcomes from a regional climate model, HadRM3, and a weather generator to determine how future flows in the upper River Jordan and in the Wadi Faynan may change. The results indicate that groundwater will play an important role in the water security of the country as irrigation demands increase. Given future projections of reduced winter rainfall and increased near-surface air temperatures, the already low groundwater recharge will decrease further. Interestingly, the modelled discharge at the Wadi Faynan indicates that extreme flood flows will increase in magnitude, despite a decrease in the mean annual rainfall. Simulations projected no increase in flood magnitude in the upper River Jordan. Discussion focuses on the utility of the modelling framework, the problems of making quantitative forecasts and the implications of reduced water availability in Jordan.

Haem peroxidase activity in Daphnia magna: A biomarker for sub-lethal toxicity assessments of kerosene-contaminated groundwater

A novel biomarker was developed in Daphnia magna to detect organic pollution in groundwater. The haem peroxidase assay, which is an indirect means of measuring oxidase activity, was particularly sensitive to kerosene contamination. Exposure to sub-lethal concentrations of kerosene-contaminated groundwater resulted in a haem peroxidase activity increase by dose with a two-fold activity peak at 25%. Reproduction in D. magna remained unimpaired when exposed to concentrations below 25% for 21 days, and a decline in fecundity was only observed at concentrations above the peak in enzyme activity. The measurement of haem peroxidase activity in D. magna detected sublethal effects of kerosene in just 24 h, whilst offering information on the health status of the organisms. The biomarker may be useful in determining concentrations above which detrimental effects would occur from long-term exposure for fuel hydrocarbons. Moreover, this novel assay detects exposure to chemicals in samples that would normally be classified as non-toxic by acute toxicity tests.

Photocatalytic degradation of humic acid in saline waters. Part 1. Artificial seawater: influence of TiO2, temperature, pH, and air-flow

We report the first systematic study on the photocatalytic oxidation of humic acid (HA) in artificial seawater (ASW). TiO2 (Degussa P25) dispersions were used as the catalyst with irradiation from a medium-pressure mercury lamp. The optimum quantity of catalyst was found to be between 2 and 2.5 g l(-1); whiled the decomposition was fastest at low pH values (pH 4.5 in the range examined), and the optimum air-flow, using an immersion well reactor with a capacity of 400 ml, was 850 ml min(-1). Reactivity increased with air-flow up to this figure, above which foaming prevented operation of the reactor. Using pure. oxygen, an optimal flow rate was observed at 300 nil min(-1), above which reactivity remains essentially constant. Following treatment for 1 h, low-salinity water (2700 mg l(-1)) was completely mineralised, whereas ASW (46000 mg l(-1)) had traces of HA remaining. These effects are interpreted and kinetic data presented. To avoid problems of precipitation due to change of ionic strength humic substances were prepared directly in ASW, and the effects of ASW on catalyst suspension and precipitation have been taken into account. The Langmuir-Hinshelwood kinetic model has been shown to be followed only approximately for the catalytic oxidation of HA in ASW. The activation energy for the reaction derived from an Arrhenius treatment was 17 ( +/-0.6) kJ mol(-1). (C) 2003 Elsevier Science Ltd. All rights reserved.

Carbohydrate-based anti-adhesive inhibition of Vibrio cholerae toxin binding to GM1-OS immobilized into artificial planar lipid membranes

We have studied 'food grade' sialyloligosaccharides (SOS) as anti-adhesive drugs or receptor analogues, since the terminal sialic acid residue has already been shown to contribute significantly to the adhesion and pathogenesis of the Vibrio cholerae toxin (Ctx). GM1-oligosaccharide (GM1-OS) was immobilized into a supporting POPC lipid bilayer onto a surface plasmon resonance (SPR) chip, and the interaction between uninhibited Ctx and GM1-OS-POPC was measured. SOS inhibited 94.7% of the Ctx binding to GM1-OS-POPC at 10 mg/mL. The SOS EC50 value of 5.521 mg/mL is high compared with 0.2811 mu g/mL (182.5 pM or 1.825 x 10(-10) M) for GM1-OS. The commercially available sialyloligosaccharide (SOS) mixture Sunsial E (R) is impure, containing one monosialylated and two disialylated oligosaccharides in the ratio 9.6%. 6.5% and 17.5%, respectively, and 66.4% protein. However, these inexpensive food-grade molecules are derived from egg yolk and could be used to fortify conventional food additives, by way of emulsifiers, sweeteners and/or preservatives. The work further supports our hypothesis that SOS could be a promising natural anti-adhesive glycomimetic against Ctx and prevent subsequent onset of disease. (C) 2009 Elsevier Ltd. All rights reserved

Effects of divided attention and speeded responding on implicit and explicit retrieval of artificial grammar knowledge

The artificial grammar (AG) learning literature (see, e.g., Mathews et al., 1989; Reber, 1967) has relied heavily on a single measure of implicitly acquired knowledge. Recent work comparing this measure (string classification) with a more indirect measure in which participants make liking ratings of novel stimuli (e.g., Manza & Bornstein, 1995; Newell & Bright, 2001) has shown that string classification (which we argue can be thought of as an explicit, rather than an implicit, measure of memory) gives rise to more explicit knowledge of the grammatical structure in learning strings and is more resilient to changes in surface features and processing between encoding and retrieval. We report data from two experiments that extend these findings. In Experiment 1, we showed that a divided attention manipulation (at retrieval) interfered with explicit retrieval of AG knowledge but did not interfere with implicit retrieval. In Experiment 2, we showed that forcing participants to respond within a very tight deadline resulted in the same asymmetric interference pattern between the tasks. In both experiments, we also showed that the type of information being retrieved influenced whether interference was observed. The results are discussed in terms of the relatively automatic nature of implicit retrieval and also with respect to the differences between analytic and nonanalytic processing (Whittlesea Price, 2001).

Prediction of Parkinson's disease tremor onset using artificial neural networks

In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.

Identification of Parkinson’s disease tremor onset using artificial neural networks

In this paper we consider the possibility of using an artificial neural network to accurately identify the onset of Parkinson’s Disease tremors in human subjects. Data for the network is obtained by means of deep brain implantation in the human brain. Results presented have been obtained from a practical study (i.e. real not simulated data) but should be regarded as initial trials to be discussed further. It can be seen that a tuned artificial neural network can act as an extremely effective predictor in these circumstances.

Using self-organising feature maps for the control of artificial organisms

Variations on the standard Kohonen feature map can enable an ordering of the map state space by using only a limited subset of the complete input vector. Also it is possible to employ merely a local adaptation procedure to order the map, rather than having to rely on global variables and objectives. Such variations have been included as part of a hybrid learning system (HLS) which has arisen out of a genetic-based classifier system. In the paper a description of the modified feature map is given, which constitutes the HLSs long term memory, and results in the control of a simple maze running task are presented, thereby demonstrating the value of goal related feedback within the overall network.

A model of UK domestic appliance-use using Artificial Neural Networks

The development of an Artificial Neural Network model of UK domestic appliance energy consumption is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 households during the summer of 2010. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with backpropagation training and has a12:10:24architecture.Model outputs include appliance load profiles which can be applied to the fields of energy planning (micro renewables and smart grids), building simulation tools and energy policy.