Energy overview of Botswana: generation and consumption

Botswana has a basic need to explore its energy concept, this being its energy sources, generation and percentage of the population with access to electricity. At present, Botswana generates electricity from coal, which supplies about 29% (on average) of the country’s demand. The other 71% is imported mainly from South Africa (Eskom). Consequently, the dependence of Botswana on imports posses threats to the security of its energy supply. As a result, there is the need to understand the bases for a possible generation expansion that would substantiate existing documentation. In view of this need, this study investigates the existing energy sources as well as energy consumption and production levels in Botswana. The study would be further developed by making projections of the energy demand up until the year 2020. The key techniques that were used include; literature review, questionnaire survey and an empirical study. The results presented indicated that, current dependable operation capacity (i.e. 100MW) should be increased to 2,595 MW or more assuming 85% plant efficiency. This would then be able to meet the growing demand for energy use. In addition, the installed capacity would be able to support commercial and mining activities for the growth of the economy.

K-Surfer: a KNIME extension for the management and analysis of Human brain MRI FreeSurfer/FSL data

Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.

Roles of the non-medical prescribing leads within organisations across a Strategic Health Authority: perceived functions and factors supporting the role

Objectives Extending the roles of nurses, pharmacists and allied health professionals to include prescribing has been identified as one way of improving service provision. In the UK, over 50 000 non-medical healthcare professionals are now qualified to prescribe. Implementation of non-medical prescribing ( NMP) is crucial to realise the potential return on investment. The UK Department of Health recommends a NMP lead to be responsible for the implementation of NMP within organisations. The aim of this study was to explore the role of NMP leads in organisations across one Strategic Health Authority (SHA) and to inform future planning with regards to the criteria for those adopting this role, the scope of the role and factors enabling the successful execution of the role. Methods Thirty-nine NMP leads across one SHA were approached. Semi-structured telephone interviews were conducted. Issues explored included the perceived role of the NMP lead, safety and clinical governance procedures and facilitators to the role. Transcribed audiotapes were coded and analysed using thematic analytical techniques. Key findings In total, 27/39 (69.2%) NMP leads were interviewed. The findings highlight the key role that the NMP lead plays with regards to the support and development of NMP within National Health Service trusts. Processes used to appoint NMP leads lacked clarity and varied between trusts. Only two NMP leads had designated or protected time for their role. Strategic influence, operational management and clinical governance were identified as key functions. Factors that supported the role included organisational support, level of influence and dedicated time. Conclusion The NMP lead plays a significant role in the development and implementation of NMP. Clear national guidance is needed with regards to the functions of this role, the necessary attributes for individuals recruited into this post and the time that should be designated to it. This is important as prescribing is extended to include other groups of non-medical healthcare professionals.

Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

Decadal predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy and society. The present study examines the decadal predictability of regional wind speed and wind energy potentials in three generations of the MiKlip (‘Mittelfristige Klimaprognosen’) decadal prediction system. The system is based on the global Max-Planck-Institute Earth System Model (MPI-ESM), and the three generations differ primarily in the ocean initialisation. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess the forecast skill for 10 m wind speeds and wind energy output (Eout) over Central Europe with lead times from one year to one decade. With this aim, a statistical-dynamical downscaling (SDD) approach is used for the regionalisation. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD-simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. This forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skills of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer and persist longest in autumn. A large-scale westerly weather type with strong pressure gradients over Central Europe is identified as potential source of the skill for wind energy potentials, showing a similar forecast skill and a high correlation with Eout anomalies. These results are promising towards the establishment of a decadal prediction system for wind energy applications over Central Europe.

Evidence of heavy metal movement down the profile of a heavily-sludged soil

Two sites in central England where sewage sludge has been deposited for decades were studied to measure the heavy metal distribution in the soil profiles. The first site (S 1) was a field receiving heavy loads sludge from a nearby wastewater treatment plant, and the second (S2) was a farm applying 'normal' sludge rates of 8 t ha(-1) y(-1) of the same sludge. Soil samples were also taken by a near-by untreated control site. In S I the movement of heavy metals was significant even down to 80 cm depth compared to the control. In S2, the concentrations of lead (Pb) and zinc (Zn) and the organic matter content were higher than the control down to 20 cm, while nickel (Ni) moved significantly down to 80 cm. This underlies. the possibility that the metals bound onto organic surfaces moved along with organic matter down to that depth. The movement of metals in S2 points out the potential risks of applying sewage sludge for a long time.

Geochemical precursors to volcanic activity at Mount St. Helens, USA

The importance of the interplay between degassing and crystallization before and after the eruption of Mount St. Helens (Washington, USA) in 1980 is well established. Here, we show that degassing occurred over a period of decades to days before eruptions and that the manner of degassing, as deduced from geochemicai signatures within the magma, was characteristic of the eruptive style. Trace element (lithium) and short-lived radioactive isotope (lead-210 and radium-226) data show that ascending magma stalled within the conduit, leading to the accumulation of volatiles and the formation of lead-210 excesses, which signals the presence of degassing magma at depth.

Life cycle analysis of UK coal fired power plants

This paper examines the life cycle GHG emissions from existing UK pulverized coal power plants. The life cycle of the electricity Generation plant includes construction, operation and decommissioning. The operation phase is extended to upstream and downstream processes. Upstream processes include the mining and transport of coal including methane leakage and the production and transport of limestone and ammonia, which are necessary for flue gas clean up. Downstream processes, on the other hand, include waste disposal and the recovery of land used for surface mining. The methodology used is material based process analysis that allows calculation of the total emissions for each process involved. A simple model for predicting the energy and material requirements of the power plant is developed. Preliminary calculations reveal that for a typical UK coal fired plant, the life cycle emissions amount to 990 g CO2-e/kWh of electricity generated, which compares well with previous UK studies. The majority of these emissions result from direct fuel combustion (882 g/kWh 89%) with methane leakage from mining operations accounting for 60% of indirect emissions. In total, mining operations (including methane leakage) account for 67.4% of indirect emissions, while limestone and other material production and transport account for 31.5%. The methodology developed is also applied to a typical IGCC power plant. It is found that IGCC life cycle emissions are 15% less than those from PC power plants. Furthermore, upon investigating the influence of power plant parameters on life cycle emissions, it is determined that, while the effect of changing the load factor is negligible, increasing efficiency from 35% to 38% can reduce emissions by 7.6%. The current study is funded by the UK National Environment Research Council (NERC) and is undertaken as part of the UK Carbon Capture and Storage Consortium (UKCCSC). Future work will investigate the life cycle emissions from other power generation technologies with and without carbon capture and storage. The current paper reveals that it might be possible that, when CCS is employed. the emissions during generation decrease to a level where the emissions from upstream processes (i.e. coal production and transport) become dominant, and so, the life cycle efficiency of the CCS system can be significantly reduced. The location of coal, coal composition and mining method are important in determining the overall impacts. In addition to studying the net emissions from CCS systems, future work will also investigate the feasibility and technoeconomics of these systems as a means of carbon abatement.

Improvements in or relating to the manufacture of multilayer interference filters

This invention relates to the manufacture of multi-layer interference filters for use with infra-red radiation, especially at wavelengths beyond 3.8 microns. A method of manufacturing a multi-layer interference filter comprising the steps of forming on a substrate successive layers of lead telluride and another material in alternation, under conditions in which sufficient oxygen is included in the lead telluride layers to reduce the apparent free charge carrier concentration therein, so that the resulting filter exhibits enhanced transparency to radiation of wavelengths greater than 3.8 microns and enhanced natural absorption to radiation of wavelength less than 3.8 microns.

Evaluating the potential for statistical decadal predictions of sea surface temperatures with a perfect model approach

We explore the potential for making statistical decadal predictions of sea surface temperatures (SSTs) in a perfect model analysis, with a focus on the Atlantic basin. Various statistical methods (Lagged correlations, Linear Inverse Modelling and Constructed Analogue) are found to have significant skill in predicting the internal variability of Atlantic SSTs for up to a decade ahead in control integrations of two different global climate models (GCMs), namely HadCM3 and HadGEM1. Statistical methods which consider non-local information tend to perform best, but which is the most successful statistical method depends on the region considered, GCM data used and prediction lead time. However, the Constructed Analogue method tends to have the highest skill at longer lead times. Importantly, the regions of greatest prediction skill can be very different to regions identified as potentially predictable from variance explained arguments. This finding suggests that significant local decadal variability is not necessarily a prerequisite for skillful decadal predictions, and that the statistical methods are capturing some of the dynamics of low-frequency SST evolution. In particular, using data from HadGEM1, significant skill at lead times of 6–10 years is found in the tropical North Atlantic, a region with relatively little decadal variability compared to interannual variability. This skill appears to come from reconstructing the SSTs in the far north Atlantic, suggesting that the more northern latitudes are optimal for SST observations to improve predictions. We additionally explore whether adding sub-surface temperature data improves these decadal statistical predictions, and find that, again, it depends on the region, prediction lead time and GCM data used. Overall, we argue that the estimated prediction skill motivates the further development of statistical decadal predictions of SSTs as a benchmark for current and future GCM-based decadal climate predictions.

In planta conditions induce genomic changes in Pseudomonas syringae pv. phaseolicola

The co-evolution of bacterial plant pathogens and their hosts is a complex and dynamic process. Plant resistance can impose stress on invading pathogens that can lead to, and select for, beneficial changes in the bacterial genome. The Pseudomonas syringae pv. phaseolicola (Pph) genomic island PPHGI-1 carries an effector gene, avrPphB (hopAR1), which triggers the hypersensitive reaction in bean plants carrying the R3 resistance gene. Interaction between avrPphB and R3 generates an antimicrobial environment within the plant, resulting in the excision of PPHGI-1 and its loss from the genome. The loss of PPHGI-1 leads to the generation of a Pph strain able to cause disease in the plant. In this study, we observed that lower bacterial densities inoculated into resistant bean (Phaseolus vulgaris) plants resulted in quicker PPHGI-1 loss from the population, and that loss of the island was strongly influenced by the type of plant resistance encountered by the bacteria. In addition, we found that a number of changes occurred in the bacterial genome during growth in the plant, whether or not PPHGI-1 was lost. We also present evidence that the circular PPHGI-1 episome is able to replicate autonomously when excised from the genome. These results shed more light onto the plasticity of the bacterial genome as it is influenced by in planta conditions.

Fully automated software solution for protein quantitation by global metabolic labeling with stable isotopes

Metabolic stable isotope labeling is increasingly employed for accurate protein (and metabolite) quantitation using mass spectrometry (MS). It provides sample-specific isotopologues that can be used to facilitate comparative analysis of two or more samples. Stable Isotope Labeling by Amino acids in Cell culture (SILAC) has been used for almost a decade in proteomic research and analytical software solutions have been established that provide an easy and integrated workflow for elucidating sample abundance ratios for most MS data formats. While SILAC is a discrete labeling method using specific amino acids, global metabolic stable isotope labeling using isotopes such as (15)N labels the entire element content of the sample, i.e. for (15)N the entire peptide backbone in addition to all nitrogen-containing side chains. Although global metabolic labeling can deliver advantages with regard to isotope incorporation and costs, the requirements for data analysis are more demanding because, for instance for polypeptides, the mass difference introduced by the label depends on the amino acid composition. Consequently, there has been less progress on the automation of the data processing and mining steps for this type of protein quantitation. Here, we present a new integrated software solution for the quantitative analysis of protein expression in differential samples and show the benefits of high-resolution MS data in quantitative proteomic analyses.

Addressing uncertainty in adaptation planning for agriculture

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop–climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.

Anisotropic model for granulated sea ice dynamics

A continuum model describing sea ice as a layer of granulated thick ice, consisting of many rigid, brittle floes, intersected by long and narrow regions of thinner ice, known as leads, is developed. We consider the evolution of mesoscale leads, formed under extension, whose lengths span many floes, so that the surrounding ice is treated as a granular plastic. The leads are sufficiently small with respect to basin scales of sea ice deformation that they may be modelled using a continuum approach. The model includes evolution equations for the orientational distribution of leads, their thickness and width expressed through second-rank tensors and terms requiring closures. The closing assumptions are constructed for the case of negligibly small lead ice thickness and the canonical deformation types of pure and simple shear, pure divergence and pure convergence. We present a new continuum-scale sea ice rheology that depends upon the isotropic, material rheology of sea ice, the orientational distribution of lead properties and the thick ice thickness. A new model of lead and thick ice interaction is presented that successfully describes a number of effects: (i) because of its brittle nature, thick ice does not thin under extension and (ii) the consideration of the thick sea ice as a granular material determines finite lead opening under pure shear, when granular dilation is unimportant.

A continuum anisotropic model of sea-ice dynamics

We develop the essential ingredients of a new, continuum and anisotropic model of sea-ice dynamics designed for eventual use in climate simulation. These ingredients are a constitutive law for sea-ice stress, relating stress to the material properties of sea ice and to internal variables describing the sea-ice state, and equations describing the evolution of these variables. The sea-ice cover is treated as a densely flawed two-dimensional continuum consisting of a uniform field of thick ice that is uniformly permeated with narrow linear regions of thinner ice called leads. Lead orientation, thickness and width distributions are described by second-rank tensor internal variables: the structure, thickness and width tensors, whose dynamics are governed by corresponding evolution equations accounting for processes such as new lead generation and rotation as the ice cover deforms. These evolution equations contain contractions of higher-order tensor expressions that require closures. We develop a sea-ice stress constitutive law that relates sea-ice stress to the structure tensor, thickness tensor and strain rate. For the special case of empty leads (containing no ice), linear closures are adopted and we present calculations for simple shear, convergence and divergence.