A comparison of the relative toxicity of bone meal and other P sources used as remedial treatments to the earthworm Eisenia fetida

It has been suggested that sources of P could be used to remediate metal-contaminated soil. The toxicity of four potential P sources, potassium hydrogen phosphate (PHP), triple superphosphate (TSP), rock phosphate (RP) and raw bone meal (RBM) to Eisenia fetida was determined. The concentration of P that is statistically likely to kill 50% of the population (LC50) for PHP, TSP and RBM was determined in OECD acute toxicity tests. 14 day LC50s expressed as bulk P concentration lay in the range 3319–4272 mg kg−1 for PHP, 3107–3590 mg kg−1 for TSP and 1782–2196 mg kg−1 for RBM (ranges present the 95% confidence intervals). For PHP and TSP mortality was significantly impacted by the electrical conductivity of the treated soils. No consistent relationship existed between mortality and electrical conductivity, soil pH and available (Olsen) P across the PHP, TSP and RBM amendment types. In RP toxicity tests mortality was low and it was not possible to determine a LC50 value. Incineration of bone meal at temperatures between 200 and 300 ◦C, pre-washing the bone meal, co-amendment with 5% green waste compost and delaying introduction of earthworms after bone meal amendments by 21 days or more led to significant reductions in the bone meal toxicity. These results are consistent with the toxicity being associated with the release and/or degradation of a soluble organic component present in raw bone meal. Bone meal can be used as an earthworm-friendly remedial amendment in metal-contaminated soils but initial additions may have a negative effect on any earthworms surviving in the contaminated soil before the organic component in the bone meal degrades in the soil.

Purified phenolics from hydrothermal treatments of biomass: ability to protect sunflower bulk oil and model food emulsions from oxidation

The phenolic fractions released during hydrothermal treatment of selected feedstocks (corn cobs, eucalypt wood chips, almond shells, chestnut burs, and white grape pomace) were selectively recovered by extraction with ethyl acetate and washed with ethanol/water solutions. The crude extracts were purified by a relatively simple adsorption technique using a commercial polymeric, nonionic resin. Utilization of 96% ethanol as eluting agent resulted in 47.0-72.6% phenolic desorption, yielding refined products containing 49-60% w/w phenolics (corresponding to 30-58% enrichment with respect to the crude extracts). The refined extracts produced from grape pomace and from chestnut burs were suitable for protecting bulk oil and oil-in-water and water-in-oil emulsions. A synergistic action with bovine serum albumin in the emulsions was observed.

Understanding the early stages of the innovation diffusion process: awareness, influence and communication networks

The themes of awareness and influence within the innovation diffusion process are addressed. The innovation diffusion process is typically represented as stages, yet awareness and influence are somewhat under-represented in the literature. Awareness and influence are situated within the contextual setting of individual actors but also within the broader institutional forces. Understanding how actors become aware of an innovation and then how their opinion is influenced is important for creating a more innovation-active UK construction sector. Social network analysis is proposed as one technique for mapping how awareness and influence occur and what they look like as a network. Empirical data are gathered using two modes of enquiry. This is done through a pilot study consisting of chartered professionals and then through a case study organization as it attempted to diffuse an innovation. The analysis demonstrates significant variations across actors’ awareness and influence networks. It is argued that social network analysis can complement other research methods in order to present a richer picture of how actors become aware of innovations and where they draw their influences regarding adopting innovations. In summarizing the findings, a framework for understanding awareness and influence associated with innovation within the UK construction sector is presented. Finally, with the UK construction sector continually being encouraged to be innovative, understanding and managing an actor’s awareness and influence network will be beneficial. The overarching conclusion thus describes the need not only to build research capacity in this area but also to push the boundaries related to the research methods employed.

Attention through self-synchronisation in the spiking neuron stochastic diffusion network

The paper discusses ensemble behaviour in the Spiking Neuron Stochastic Diffusion Network, SNSDN, a novel network exploring biologically plausible information processing based on higher order temporal coding. SNSDN was proposed as an alternative solution to the binding problem [1]. SNSDN operation resembles Stochastic Diffusin on Search, SDS, a non-deterministic search algorithm able to rapidly locate the best instantiation of a target pattern within a noisy search space ([3], [5]). In SNSDN, relevant information is encoded in the length of interspike intervals. Although every neuron operates in its own time, ‘attention’ to a pattern in the search space results in self-synchronised activity of a large population of neurons. When multiple patterns are present in the search space, ‘switching of at- tention’ results in a change of the synchronous activity. The qualitative effect of attention on the synchronicity of spiking behaviour in both time and frequency domain will be discussed.

Time complexity analysis of the stochastic diffusion search

The Stochastic Diffusion Search algorithm -an integral part of Stochastic Search Networks is investigated. Stochastic Diffusion Search is an alternative solution for invariant pattern recognition and focus of attention. It has been shown that the algorithm can be modelled as an ergodic, finite state Markov Chain under some non-restrictive assumptions. Sub-linear time complexity for some settings of parameters has been formulated and proved. Some properties of the algorithm are then characterised and numerical examples illustrating some features of the algorithm are presented.

Dynamic analysis of house price diffusion across Asian financial centres

The aim of this paper is to explore effects of macroeconomic variables on house prices and also, the lead-lag relationships of real estate markets to examine house price diffusion across Asian financial centres. The analysis is based on the Global Vector Auto-Regression (GVAR) model estimated using quarterly data for six Asian financial centres (Hong Kong, Tokyo, Seoul, Singapore, Taipei and Bangkok) from 1991Q1 to 2011Q2. The empirical results indicate that the global economic conditions play significant roles in shaping house price movements across Asian financial centres. In particular, a small open economy that heavily relies on international trade such as – Singapore and Tokyo - shows positive correlations between economy’s openness and house prices, consistent with the Balassa-Samuelson hypothesis in international trade. However, region-specific conditions do play important roles as determinants of house prices, partly due to restrictive housing policies and demand-supply imbalances, as found in Singapore and Bangkok.