Structural and electrical anisotropy of (001)-, (116)-, and (103)-oriented epitaxial SrBi2Ta2O9 thin films on SrTiO3 substrates grown by pulsed laser deposition

Epitaxial SrBi2Ta2O9 (SBT) thin films with well-defined (001), (116), and (103) orientations have been grown by pulsed laser deposition on (001)-, (011)-, and (111)-oriented Nb-doped SrTiO3 substrates. X-ray diffraction pole figure and phi -scan measurements revealed that the three-dimensional epitaxial orientation relation SBT(001)parallel to SrTiO3(001), and SBT[1(1) over bar 0]parallel to SrTiO3[100] is valid for all cases of SET thin films on SrTiO3 substrates, irrespective of their orientations. Atomic force microscopy images of the c-axis-oriented SET revealed polyhedron-shaped grains showing spiral growth around screw dislocations. The terrace steps of the c-axis-oriented SET films were integral multiples of a quarter of the lattice parameter c of SBT (similar to 0.6 nm). The grains of (103)-oriented SET films were arranged in a triple-domain configuration consistent with the symmetry of the SrTiO3(111) substrate. The measured remanent polarization (2P(r)) and coercive field (2E(c)) of (116)-oriented SBT films were 9.6 muC/cm(2) and 168 kV/cm, respectively, for a maximum applied electric field of 320 kV/cm. Higher remanent polarization (2P(r)=10.4 muC/cm(2)) and lower coercive field (2E(c)=104 kV/cm) than those of SBT(116) films were observed in (103)-oriented SET thin films, and (001)-oriented SET revealed no ferroelectricity along the [001] axis. The dielectric constants of (001)-, (116)-, and (103)-oriented SBT were 133, 155, and 189, respectively. (C) 2000 American Institute of Physics.

Microbial Ecology and Geo-electrical Responses across a Groundwater Plume

We have used geophysics, microbiology, and geochemistry to link large-scale (30+ m) geophysical self-potential (SP) responses at a groundwater contaminant plume with its chemistry and microbial ecology of groundwater and soil from in and around it. We have found that microbially mediated transformation of ammonia to nitrite, nitrate, and nitrogen gas was likely to have promoted a well-defined electrochemical gradient at the edge of the plume, which dominated the SP response. Phylogenetic analysis demonstrated that the plume fringe or anode of the geobattery was dominated by electrogens and biodegradative microorganisms including Proteobacteria alongside Geobacteraceae, Desulfobulbaceae, and Nitrosomonadaceae. The uncultivated candidate phylum OD1 dominated uncontaminated areas of the site. We defined the redox boundary at the plume edge using the calculated and observed electric SP geophysical measurements. Conductive soils and waste acted as an electronic conductor, which was dominated by abiotic iron cycling processes that sequester electrons generated at the plume fringe. We have suggested that such geoelectric phenomena can act as indicators of natural attenuation processes that control groundwater plumes. Further work is required to monitor electron transfer across the geoelectric dipole to fully define this phenomenon as a geobattery. This approach can be used as a novel way of monitoring microbial activity around the degradation of contaminated groundwater plumes or to monitor in situ bioelectric systems designed to manage groundwater plumes.

Residential electrical vehicle charging strategies: the good, the bad and the ugly

In recent years, a wide variety of centralised and decentralised algorithms have been proposed for residential charging of electric vehicles (EVs). In this paper, we present a mathematical framework which casts the EV charging scenarios addressed by these algorithms as optimisation problems having either temporal or instantaneous optimisation objectives with respect to the different actors in the power system. Using this framework and a realistic distribution network simulation testbed, we provide a comparative evaluation of a range of different residential EV charging strategies, highlighting in each case positive and negative characteristics.

Uncovering BRCA1-regulated signalling pathways by microarray-based expression profiling

The introduction of microarray technology to the scientific and medical communities has dramatically changed the way in which we now address basic biomedical questions. Expression profiling using microarrays facilitates an experimental approach where alterations in the transcript level of entire transcriptomes can be simultaneously assayed in response to defined stimuli. We have used microarray analysis to identify downstream transcriptional targets of the BRCA1 (Breast Cancer 1) tumour-suppressor gene as a means of defining its function. BRCA1 has been implicated in the predisposition to early onset breast and ovarian cancer and while its exact function remains to be defined, roles in DNA repair, cell-cycle control and transcriptional regulation have been implied. In the current study we have generated cell lines with tetracycline-regulated, inducible expression of BRCA1 as a tool to identify genes, which might represent important effectors of BRCA1 function. Oligonucleotide array-based expression profiling identified a number of genes that were upregulated at various times following inducible expression of BRCA1 including the DNA damage-responsive gene GADD45 (Growth Arrest after DNA Damage). Identified targets were confirmed by Northern blot analysis and their functional significance as BRCA1 targets examined.

Uncovering functionally relevant signaling pathways using microarray-based expression profiling

The introduction of microarray technology to the scientific and medical communities has fundamentally altered the way in which we now address basic biomedical questions. Microarrays technology facilitates a more complete and inclusive experimental approach where alterations in the transcript level of entire genomes can be simultaneously assayed in response to a variety of stimuli. Conceptually different approaches to the development of microarray technology have resulted in the generation of two different array formats: oligonucleotide arrays and cDNA arrays. The application of microarray and related technologies to identify specific targets of defined genes that have clearly been implicated in cancer progression requires a specific experimental approach. The objective of tiffs approach is to define changes in transcriptional profile that occur in response to modulating the expression level of the gene to be studied. The resulting altered expression profile can then be viewed as a blueprint by which that gene effects its cellular function. We have used oligonucleotide array-based expression profiling in collaboration with Affymetrix to identify downstream transcriptional targets of the BRCA1 tumor-suppressor gene as a means of defining its function. BRCA1 has been implicated in at least three functional pathways, namely, mediating the cellular response to DNA damage, as a cell cycle checkpoint protein and in the regulation of transcription. The physiological significance of these properties and their implications for the function of BRCA1 as a tumor-suppressor gene remain to be defined.

ALEA: Fine-Grain Energy Profiling with Basic Block Sampling

Energy efficiency is an essential requirement for all contemporary computing systems. We thus need tools to measure the energy consumption of computing systems and to understand how workloads affect it. Significant recent research effort has targeted direct power measurements on production computing systems using on-board sensors or external instruments. These direct methods have in turn guided studies of software techniques to reduce energy consumption via workload allocation and scaling. Unfortunately, direct energy measurements are hampered by the low power sampling frequency of power sensors. The coarse granularity of power sensing limits our understanding of how power is allocated in systems and our ability to optimize energy efficiency via workload allocation.
We present ALEA, a tool to measure power and energy consumption at the granularity of basic blocks, using a probabilistic approach. ALEA provides fine-grained energy profiling via sta- tistical sampling, which overcomes the limitations of power sens- ing instruments. Compared to state-of-the-art energy measurement tools, ALEA provides finer granularity without sacrificing accuracy. ALEA achieves low overhead energy measurements with mean error rates between 1.4% and 3.5% in 14 sequential and paral- lel benchmarks tested on both Intel and ARM platforms. The sampling method caps execution time overhead at approximately 1%. ALEA is thus suitable for online energy monitoring and optimization. Finally, ALEA is a user-space tool with a portable, machine-independent sampling method. We demonstrate two use cases of ALEA, where we reduce the energy consumption of a k-means computational kernel by 37% and an ocean modelling code by 33%, compared to high-performance execution baselines, by varying the power optimization strategy between basic blocks.