Biosensor based toxicity dissection of tannery and associated environmental samples

Kenyan tannery and associated environmental samples were selected for ecotoxicological assessment. A tool-kit of techniques was developed, including whole-cell biosensor and chemical assays. A luminescence based bacterial biosensor (Escherichia coli HB101 pUCD607) (via a multi-copy plasmid) was used for toxicity assessment. Samples were manipulated prior to biosensor interrogation to identify the nature of the toxic contaminants. Untreated samples (before any manipulations) showed a strong toxic effect at the discharge point in comparison to other sampling points. Sparging was used to identify toxicity associated with volatile organics. The toxicity of contaminants, removed by treatment with activated charcoal was identified for all the sampling points except for those upstream of effluent discharges. Filtration identified toxicity associated with suspended solids. Changes in availability of toxic contaminants due to pH adjustment of most samples from the tannery effluent treatment pits were also associated with the extreme pH values (4.0 and 8.0). The approach used has highlighted the complexicity of toxic pollutants in effluent from the tanning industry and the dissection of toxicity points to possible remediation strategies for effluents from the tanning industry.

Power Capping: What Works, What Does Not

Peak power consumption is the first order design constraint of data centers. Though peak power consumption is rarely, if ever, observed, the entire data center facility must prepare for it, leading to inefficient usage of its resources. The most prominent way for addressing this issue is to limit the power consumption of the data center IT facility far below its theoretical peak value. Many approaches have been proposed to achieve that, based on the same small set of enforcement mechanisms, but there has been no corresponding work on systematically examining the advantages and disadvantages of each such mechanism. In the absence of such a study,it is unclear what is the optimal mechanism for a given computing environment, which can lead to unnecessarily poor performance if an inappropriate scheme is used. This paper fills this gap by comparing for the first time five widely used power capping mechanisms under the same hardware/software setting. We also explore possible alternative power capping mechanisms beyond what has been previously proposed and evaluate them under the same setup. We systematically analyze the strengths and weaknesses of each mechanism, in terms of energy efficiency, overhead, and predictable behavior. We show how these mechanisms can be combined in order to implement an optimal power capping mechanism which reduces the slow down compared to the most widely used mechanism by up to 88%. Our results provide interesting insights regarding the different trade-offs of power capping techniques, which will be useful for designing and implementing highly efficient power capping in the future.