A multi vector energy analysis for interconnected power and gas systems

This paper presents the first multi vector energy analysis for the interconnected energy systems of Great Britain (GB) and Ireland. Both systems share a common high penetration of wind power, but significantly different security of supply outlooks. Ireland is heavily dependent on gas imports from GB, giving significance to the interconnected aspect of the methodology in addition to the gas and power interactions analysed. A fully realistic unit commitment and economic dispatch model coupled to an energy flow model of the gas supply network is developed. Extreme weather events driving increased domestic gas demand and low wind power output were utilised to increase gas supply network stress. Decreased wind profiles had a larger impact on system security than high domestic gas demand. However, the GB energy system was resilient during high demand periods but gas network stress limited the ramping capability of localised generating units. Additionally, gas system entry node congestion in the Irish system was shown to deliver a 40% increase in short run costs for generators. Gas storage was shown to reduce the impact of high demand driven congestion delivering a reduction in total generation costs of 14% in the period studied and reducing electricity imports from GB, significantly contributing to security of supply.

Pleiotropic Analysis of Lung Cancer and Blood Triglycerides

Epidemiologically related traits may share genetic risk factors, and pleiotropic analysis could identify individual loci associated with these traits. Because of their shared epidemiological associations, we conducted pleiotropic analysis of genome-wide association studies of lung cancer (12 160 lung cancer case patients and 16 838 control subjects) and cardiovascular disease risk factors (blood lipids from 188 577 subjects, type 2 diabetes from 148 821 subjects, body mass index from 123 865 subjects, and smoking phenotypes from 74 053 subjects). We found that 6p22.1 (rs6904596, ZNF184) was associated with both lung cancer (P = 5.50x10(-6)) and blood triglycerides (P = 1.39x10(-5)). We replicated the association in 6097 lung cancer case patients and 204 657 control subjects (P = 2.40 × 10(-4)) and in 71 113 subjects with triglycerides data (P = .01). rs6904596 reached genome-wide significance in lung cancer meta-analysis (odds ratio = 1.15, 95% confidence interval = 1.10 to 1.21 ,: Pcombined = 5.20x10(-9)). The large sample size provided by the lipid GWAS data and the shared genetic risk factors between the two traits contributed to the uncovering of a hitherto unidentified genetic locus for lung cancer.

Joint Analysis of Multiple Algorithms and Performance Measures

There has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and time complexity). Once one has developed an approach to a problem of interest, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Standard tests used for this purpose are able to consider jointly neither performance measures nor multiple competitors at once. The aim of this paper is to resolve these issues by developing statistical procedures that are able to account for multiple competing measures at the same time and to compare multiple algorithms altogether. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameters of such models, as usually the number of studied cases is very reduced in such comparisons. Data from a comparison among general purpose classifiers is used to show a practical application of our tests.

An analysis of wind curtailment and constraint at a nodal level

Many countries have set challenging wind power targets to achieve by 2020. This paper implements a realistic analysis of curtailment and constraint of wind energy at a nodal level using a unit commitment and economic dispatch model of the Irish Single Electricity Market in 2020. The key findings show that significant reduction in curtailment can be achieved when the system non-synchronous penetration limit increases from 65% to 75%. For the period analyzed, this results in a decreased total generation cost and a reduction in the dispatch-down of wind. However, some nodes experience significant dispatch-down of wind, which can be in the order of 40%. This work illustrates the importance of implementing analysis at a nodal level for the purpose of power system planning.

Fast and sensitive aflatoxin B1 and total aflatoxins ELISAs for analysis of peanuts, maize and feed ingredients

Aflatoxins are a group of carcinogenic compounds produced by Aspergillus fungi that can grow on different agricultural crops. Both acute and chronic exposure to these mycotoxins can cause serious illness. Due to the high occurrence of aflatoxins in crops worldwide fast and cost-effective analytical methods are required for the identification of contaminated agricultural commodities before they are processed into final products and placed on the market. In order to provide new tools for aflatoxin screening two prototype fast ELISA methods: one for the detection of aflatoxin B₁ and the other for total aflatoxins were developed. Seven monoclonal antibodies with unique high sensitivity and at the same time good cross-reactivity profiles were produced. The monoclonal antibodies were characterized and two antibodies showing IC₅₀ of 0.037 ng/mL and 0.031 ng/mL for aflatoxin B₁ were applied in simple and fast direct competitive ELISA tests. The methods were validated for peanut matrix as this crop is one of the most affected by aflatoxin contamination. The detection capabilities of aflatoxin B₁ and total aflatoxins ELISAs were 0.4 μg/kg and 0.3 μg/kg for aflatoxin B₁, respectively, which are one of the lowest reported values. Total aflatoxins ELISA was also validated for the detection of aflatoxins B₂, G₁ and G₂. The application of the developed tests was demonstrated by screening 32 peanut samples collected from the UK retailers. Total aflatoxins ELISA was further applied to analyse naturally contaminated maize porridge and distiller's dried grain with solubles samples and the results were correlated with these obtained by UHPLC-MS/MS method.