Electronic screening for mental illness in patients with psoriasis

In this cross sectional study from a large UK centre, screening for mental illness in individuals with psoriasis has demonstrated a high burden of depression and anxiety. 85% of the cohort report that their psoriasis had affected their quality of life. Quality of life scores correlate with depression scores, emphasing the importance of managing individual's mental health alongside their psoriasis to improve overall quality of life

Electronic screening for mental illness in patients with psoriasis

In this cross sectional study from a large UK centre, screening for mental illness in individuals with psoriasis has demonstrated a high burden of depression and anxiety. 85% of the cohort report that their psoriasis had affected their quality of life. Quality of life scores correlate with depression scores, emphasing the importance of managing individual's mental health alongside their psoriasis to improve overall quality of life

Influence of crop residue ration supplementation on the attainment of puberty and postpartum reproductive activities of Red Sokoto goats

The general objective of this study was to come up with an appropriate, affordable and locally available crop residue supplementation package that would enhance reproductive performance in small ruminants. Specifically, 28 Red Sokoto weaner does between 3 and 4 months of age weighing between 2 and 3 kg were used in the first experiment to determine the influence of crop residue supplementation on age and weight at puberty as determined by blood progesterone levels. In the second experiment, another 28 adult does (equal to or greater than 2 years old) of the same breed in the same flock with lactation numbers between 1 and 3 were used to determine the length of postpartum acyclic period. In both experiments, a 3 x 2 factorial experimental design comprising three dietary supplements (A, B, C) at two feeding levels (1% and 2% of body weight) fed in addition to a basal diet of Digitaria smutsii hay and natural pasture ad libitum with an unsupplemented negative control group (D) and four goats per treatment was utilized. In ration A, a conventional concentrate supplement consisting of maize, wheat offal, cottonseed cake and bonemeal was utilized; in rations B and C, the supplement consisted of guinea-corn bran, cowpea husk and groundnut haulms; and maize offal, groundnut shells and groundnut haulms respectively. Unsupplemented (ration D) weaner does reached puberty at a later age and had lighter body weights than all the others. Weaner does on ration 2A (concentrate fed at 2% of body weight) attained puberty at the earliest age and heaviest body weight, although the age at puberty was not significantly different from those on rations 1A (concentrate fed at 1% body weight), 1C and 2C. Blood progesterone profiles before and after puberty ranged from 0.05 to 9.0 ng/ml, respectively, and was highest in does fed rations A and C and least in the unsupplemented does. The mean interval between kidding and initiation of ovarian activity was 54.28 plus or minus 17.61 days and the mean interval between kidding and conception was 63.04 plus or minus 25.34 days. Only 25% of the unsupplemented does conceived again during the period under study compared with 100% in rations 1A, 2A, 1C and 2C; 75% in ration 2B and 50% in ration 1B. It was concluded that implementation of supplementary feeding in the dry season improves reproductive performance in the Red Sokoto doe. Furthermore, ration C, a crop residue-based ration, was a suitable dry season supplementation alternative to the expensive conventional concentrate ration for the smallholder goat farmer in the subhumid tropics of Nigeria

Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG) is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF) in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.Our gratitude to CEDER-CIEMAT for providing the data to the presented work. In the same way, we want to convey our gratitude to the project partners MIRED-CON (IPT-2012-0611-120000), funded by the INNPACTO agreement of the Ministry of Economy and Competitiveness of the Government of Spain. Finally, a special mention to the help of the students Fatih Selim Bayraktar and Guniz Betul Yasar of Gazi University (Turkey), and Cristina Gil Valverde of UNED (Spain).Hernandez, L.; Baladron, C.; Aguiar, JM.; Calavia, L.; Carro, B.; Sanchez-Esguevillas, A.; Perez, F.... (2014). Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems. Energies. 7(3):1576-1598. https://doi.org/10.3390/en7031576S1576159873Spencer, H. H., & Hazen, H. L. (1925). Artificial Representation of Power Systems. Transactions of the American Institute of Electrical Engineers, XLIV, 72-79. doi:10.1109/t-aiee.1925.5061095Hamilton, R. F. (1944). The Summation or Load Curves. Transactions of the American Institute of Electrical Engineers, 63(10), 729-735. doi:10.1109/t-aiee.1944.5058782Davies, M. (1959). The relationship between weather and electricity demand. Proceedings of the IEE Part C: Monographs, 106(9), 27. doi:10.1049/pi-c.1959.0007Matthewman, P. D., & Nicholson, H. (1968). Techniques for load prediction in the electricity-supply industry. Proceedings of the Institution of Electrical Engineers, 115(10), 1451. doi:10.1049/piee.1968.0258Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems, 16(1), 44-55. doi:10.1109/59.910780García-Ascanio, C., & Maté, C. (2010). Electric power demand forecasting using interval time series: A comparison between VAR and iMLP. Energy Policy, 38(2), 715-725. doi:10.1016/j.enpol.2009.10.007Marin, F. J., Garcia-Lagos, F., Joya, G., & Sandoval, F. (2002). Global model for short-term load forecasting using artificial neural networks. IEE Proceedings - Generation, Transmission and Distribution, 149(2), 121. doi:10.1049/ip-gtd:20020224Hernández, L., Baladrón, C., Aguiar, J., Carro, B., & Sánchez-Esguevillas, A. (2012). Classification and Clustering of Electricity Demand Patterns in Industrial Parks. Energies, 5(12), 5215-5228. doi:10.3390/en5125215Hernández, L., Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., … Gómez, J. (2012). A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework. Sensors, 12(9), 11571-11591. doi:10.3390/s120911571Hagan, M. T., & Behr, S. M. (1987). The Time Series Approach to Short Term Load Forecasting. IEEE Transactions on Power Systems, 2(3), 785-791. doi:10.1109/tpwrs.1987.4335210Hernandez, L., Baladrón, C., Aguiar, J., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies, 6(3), 1385-1408. doi:10.3390/en6031385Kim, H., & Thottan, M. (2011). A two-stage market model for microgrid power transactions via aggregators. Bell Labs Technical Journal, 16(3), 101-107. doi:10.1002/bltj.20524Zhou, L., Rodrigues, J., & Oliveira, L. (2012). QoE-driven power scheduling in smart grid: architecture, strategy, and methodology. IEEE Communications Magazine, 50(5), 136-141. doi:10.1109/mcom.2012.6194394Liang Zhou, & Rodrigues, J. J. P. C. (2013). Service-oriented middleware for smart grid: Principle, infrastructure, and application. IEEE Communications Magazine, 51(1), 84-89. doi:10.1109/mcom.2013.6400443Wille-Haussmann, B., Erge, T., & Wittwer, C. (2010). Decentralised optimisation of cogeneration in virtual power plants. Solar Energy, 84(4), 604-611. doi:10.1016/j.solener.2009.10.009Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A., Lloret, J., … Cook, D. (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Communications Magazine, 51(1), 106-113. doi:10.1109/mcom.2013.6400446Razavi, S., & Tolson, B. A. (2011). A New Formulation for Feedforward Neural Networks. IEEE Transactions on Neural Networks, 22(10), 1588-1598. doi:10.1109/tnn.2011.2163169Bishop, C. M. (1994). Neural networks and their applications. Review of Scientific Instruments, 65(6), 1803-1832. doi:10.1063/1.114483

The Cambridge Crystallography Subroutine Library Mark 4 users manual

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