Regionalization of droughts in Portugal

Comunicação apresentada na "6th International Conference on River Basin Management", Riverside, California, 2011Droughts are complex natural hazards that distress large worldwide areas every year with serious impacts on society, environment and economy. Despite their importance they are still among the least understood extreme weather events. This paper is focused on the identification of regional patterns of droughts in Mainland Portugal based on monthly precipitation data, from September 1910 to October 2004, in 144 rain gages distributed uniformly over the country. The drought events were described by means of the Standardized Precipitation Index (SPI) applied to different time scales. To assess the spatial and temporal patterns of droughts, a principal component analysis (PCA) and K-means clustering method (KMC) were applied to the SPI series. The study showed that, for the different times scales, both methods resulted in an equivalent areal zoning, with three regions with different behaviours: the north, the centre and the south of Portugal. These three regions are consistent with the precipitation spatial distribution in Portugal Mainland, which in general terms decrease from North to South, with the central mountainous region representing the transition between the wet north and the progressively dry south. As the mean annual precipitation decreases southwards the hydrological regime becomes more irregular and consequently more prone to droughts

Spring drought forecasting in mainland Portugal based on large-scale climatic indices

[EN] The success of a strategy of mitigation of the effects of the droughts requires the implementation of an effective monitoring and forecasting system, able to identify drought events and follow their spatiotemporal evolution. This article demonstrates the capability of the artificial neural networks in predicting the spring standardized precipitation index, SPI, for Portugal. The validation of the models used the hindcasting, which is a technique by which a given model is tested through its application to historical data followed by the comparison of the results thus achieved with the data. The SPI index was calculated at the timescale of six months and the climate indices used as external predictors in the hindcasting were the North Atlantic Oscillation and temperatures of the sea surface. The study showed the added value of the inclusion of previous predictors in the model. Maps of the probabilities of the drought occurrences which may be very important for integrated planning and management of water resources were also developed.[PT] O sucesso de uma estratégia de mitigação dos efeitos da seca passa pela implementação de um sistema de monitorização e previsão eficaz, capaz de identificar os eventos de seca e de seguir a sua evolução espácio-temporal. Neste artigo demonstrase a eficiência de redes neuronais artificiais na previsão, para Portugal, do índice de precipitação padronizada, SPI, relativo à primavera. A validação dos modelos recorreu ao hindcasting, designando-se, por tal, a técnica através da qual um dado modelo é testado mediante a sua aplicação a períodos temporais históricos, com comparação dos resultados obtidos com as respectivas observações. O índice SPI foi calculado à escala temporal de 6 meses tendo o hindcast utilizado como indicadores climáticos a oscilação do Atlântico Norte e temperaturas da superfície do mar. O estudo evidenciou a mais valia da inclusão dos anteriores predictores externos no modelo de previsão. Elaboraram-se, ainda, mapas de probabilidade de ocorrência de seca os quais constituem importantes ferramentas no planeamento integrado e na gestão de recursos hídricosSantos, J.; Portela, M.; Pulido-Calvo, I. (2015). Previsão de secas na primavera em Portugal Continental com base em indicadores climáticos de larga escala. Ingeniería del Agua. 19(4):211-227. doi:10.4995/ia.2015.4109.SWORD211227194Agnew, C.T. (2000). Using the SPI to identify drought. Drought Network News, 12, 6-12.ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). Artificial neural networks in hydrology. I. Preliminary concepts. Journal of Hydrologic Engineering, 5(2), 115-123. doi:10.1061/(ASCE)1084-0699(2000)5:2(115)ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). Artificial neural networks in hydrology. II. Hydrologic applications. Journal of Hydrologic Engineering, 5(2), 124-137. doi:10.1061/(ASCE)1084-0699(2000)5:2(124)Bordi, I., Fraedrich, K., Petitta, M., Sutera, A. (2005). Methods for predicting drought occurrences. In Proceedings of the 6th International Conference of the European Water Resources Association, Menton, France.Bowden, G.J., Dandy, G.C., Maier, H.R. (2005). Input determination for neural network models in water resources applications. Part 1-background and methodology. Journal of Hydrology, 301(1-4), 75-92. doi:10.1016/j.jhydrol.2004.06.021Campolo, M., Andreusi, P., Soldati, A. (1999). River flood forecasting with a neural network model. Water Resources Research, 35(4), 1191-1197. doi:10.1029/1998WR900086Cancelliere, A., Di Mauro, G., Bonaccorso, B., Rossi, G. (2005). Stochastic forecasting of Standardized Precipitation Index. In Proceedings of XXXI IAHR Congress Water Engineering for the future: Choice and Challenges, Seoul, Korea, 3252-3260.Cancelliere, A., Di Mauro, G., Bonaccorso, B., Rossi, G. (2007). Drought forecasting using the Standardized Precipitation Index. Water Resources Management, 21(5), 801-819. doi:10.1007/s11269-006-9062-yCordery, I., McCall, M. (2000). A model for forecasting drought from teleconnections. Water Resources Research, 36(3), 763-768. doi:10.1029/1999WR900318Dastorani, M.T., Afkhami, H. (2011). Application of artificial neural networks on drought prediction in Yazd (Central Iran). Desert, 16, 39-48.Dawson, D.W., Wilby, R. (1998). An artificial neural network approach to precipitation-runoff modeling. Hydrological Sciences Journal, 43(1), 47-66. doi:10.1080/02626669809492102Demyanov, V., Kanevsky, M., Chernov, S., Savelieva, E., Timonin, V. (1998). Neural network residual kriging application for climatic data. Journal of Geographic Information and Decision Analysis, 2(2), 215-232.Di Mauro, G., Bonaccorso, G.B., Cancelliere, A., Rossi, G. (2008). Use of NAO index to improve drought forecasting in the Mediterranean area: Application to Sicily region. Options Méditerranéennes. Série A: Séminaires Méditerranéens, No. 80.Fernando, M.K.G., Maier, H.R., Dandy, G.C. (2009). Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach. Journal of Hydrology, 367(3-4), 165-176. doi:10.1016/j.jhydrol.2008.10.019Gámiz-Fortis, S., Esteban-Parra, M.J., Trigo, R.M., Castro-Díez, Y. (2010). Potential predictability of Iberian river flow based on its relationship with previous winter global SST. Journal of Hydrology, 385, 143-149. doi:10.1016/j.jhydrol.2010.02.010Gámiz-Fortis, S., Pozo-Vázquez, D., Trigo, R.M., Castro-Díez, Y. (2008a). Quantifying the predictability of winter river flow in Iberia. Part I: Interannual predictability. Journal of Climate, 21, 2484-2502. doi:10.1175/2007JCLI1774.1Gámiz-Fortis, S., Pozo-Vázquez, D., Trigo, R.M., Castro-Díez, Y. (2008b). Quantifying the predictability of winter river flow in Iberia. Part II: Seasonal predictability. Journal of Climate, 21, 2503-2518. doi:10.1175/2007JCLI1775.1Hoerling, M., Kumar, A. (2003). The perfect ocean for drought. Science, 299(5607), 691-694. Geophysical Research Abstracts, 12, EGU2010-8454, EGU General Assembly 2010, Viena, Austria. doi:10.1126/science.1079053Hurrell, J.W. (1995). Decadal trends in North Atlantic Oscillation: regional temperatures and precipitation. Science, 269(5224), 676-679. doi:10.1126/science.269.5224.676Hurrell, J.W., Kushnir, Y., Visbeck, M. (2001). The North Atlantic Oscillation. Science, 291(5504), 603-605. doi:10.1126/science.1058761Hurrell, J.W., Kushnir, Y., Ottersen, G., Visbeck, M. (2003). The North Atlantic Oscillation: climatic significance and environmental impact. Geophysical Monograph Series, 134, American Geophysical Union, Washington, DC, USA.Ionita, M., Lhomann, G., Rimbu, N. (2008). Prediction of spring Elbe discharge based on stable teleconnections with winter global temperature and precipitation. Journal of Climate, 21(23), 6215-6226. doi:10.1175/2008JCLI2248.1Ionita, M., Lohmann, G., Rimbu, N., Chelcea, S., Dima, M. (2012). Interannual to decadal summer drought variability over Europe and its relationship to global sea surface temperature. Climate Dynamics, 38(1), 363-377. doi:10.1007/s00382-011-1028-yIyer, M.S., Rhinehart, R.R. (1999). A method to determine the required number of neural-network training repetitions. IEEE Transactions on Neural Networks, 10(2), 427-432. doi:10.1109/72.750573Jain, A., Kumar, A.M. (2007). Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing, 7(2), 585-592. doi:10.1016/j.asoc.2006.03.002Jones, P.D., Jonsson, T., Wheeler, D. (1997). Extension to the North Atlantic Oscillation using early instrumental pressure observations from Gibraltar and South-West Iceland. International Journal of Climatology, 17(13), 1433-1450. doi:10.1002/(SICI)1097-0088(19971115)17:133.0.CO;2-PJones, P.D., Osborn, T.J., Briffa, K.R. (2003). Pressure-based measures of the North Atlantic oscillation (NAO): a comparison and an assessment of changes in the strength of the NAO and in its influence on surface climate parameters in The North Atlantic Oscillation: climate significance and environmental impact. Geophysics Monogram 134, 51-62, American Geophysical Union.Karunanithi, N., Grenney, W.J., Whitely, D., Bovee, K. (1994). Neural networks for river flow prediction. Journal of Computing Civil Engineering, 8(2), 201-219. doi:10.1061/(ASCE)0887-3801(1994)8:2(201)Kim T. e Juan B. Valdés, (2003). Nonlinear Model for Drought Forecasting Based on a Conjunction of Wavelet Transforms and Neural Networks. Journal of Hydrologic Engineering, 8(6), 319-328. doi:10.1061/(ASCE)1084-0699(2003)8:6(319)Kitanidis, P.K., Bras, R.L. (1980). Real time forecasting with a conceptual hydrological model. 2. Applications and results. Water Resources Research, 16(6), 1034-1044. doi:10.1029/WR016i006p01034Kurnik, B. (2009). DESERT Action JRC, Drought forecasting methods. Ljubljana on 24 September 2009 - 1st DMCSEE - JRC Workshop on Drought Monitoring.Legates, D.R., McCabe Jr., G.J. (1999). Evaluating the use of 'goodness-of-fit' measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233-241. doi:10.1029/1998WR900018Lloyd-Hughes, B. (2002). The long range predictability of European drought. PhD Thesis, Department of Space and Climate Physics, University of London, University College London, UK.López-Moreno, J.I., Vicente-Serrano, S.M. (2008). Extreme phases of the wintertime North Atlantic Oscillation and drought occurrence over Europe: a multi-temporal-scale approach. Journal of Climate, 21(6), 1220-1243. doi:10.1175/2007JCLI1739.1López-Moreno, J.I., Beguería, S., Vicente-Serrano, S.M., García-Ruiz, J.M. (2007). The influence of the NAO on water resources in central Iberia: precipitation, streamflow anomalies and reservoir management strategies. Water Resources Research, 43,W09411, doi:10.1029/2007WR005864Martín, M.L., Luna, M.Y., Morata, A., Valero, F. (2004). North Atlantic teleconnection patterns of low-frequency variability and their links with springtime precipitation in the western Mediterranean. International Journal of Climatology, 24(2), 213-230. doi:10.1002/joc.993Martín-Vide, J., Fernández, D. (2001). El índice NAO y la precipitación mensual en la España peninsular. Investigaciones Geográficas, 26, 41-58. doi:10.14198/INGEO2001.26.07May, R.J., Maier, H.R., Dandy, G.C., Fernando, T.M.K.G. (2008). Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modelling and Software, 23(10-11), 1312-1326. doi:10.1016/j.envsoft.2008.03.007McKee, T.B., Doesken, N.J., Kleist, J. (1993).The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society, Boston, USA, 179-184.Mishra, A.K., Desai, V.R. (2006). Drought forecasting using feed-forward recursive neural network. Ecological Modelling, 198(1-2), 127-138. doi:10.1016/j.ecolmodel.2006.04.017Mo, K.C., Jae-Kyung, E., Schemm, E., Yoo, S.-H. (2009). Influence of ENSO and the Atlantic multi-decadal Oscillation on drought over the United States. Journal of Climate, 22, 5962-5982. doi:10.1175/2009JCLI2966.1Mutlu, E., Chaubey, I., Hexmoor, H., Bajwa, S.G. (2008). Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrological Processes, 22(26), 5097-5106. doi:10.1002/hyp.7136Michie, D., Spiegelhalter, D.J., Taylor, C.C. (1994). Machine learning, neural and statistical classification. Project StatLog, Department of Statistics, University of Leeds, UK.Ochoa-Rivera, J.C., García-Bartual, R., Andreu, J. (2002). Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks. Journal of Hydrology and Earth System Sciences, 6(4), 641-654. doi:10.5194/hess-6-641-2002Ochoa-Rivera, J.C., García-Bartual, R., Andreu, J. (2007). Influence of Inflows Modeling on Management Simulation of Water Resources System. Journal of Water Resources Planning and Management, ASCE, 133(2), 106-116. doi:10.1061/(ASCE)0733-9496(2007)133:2(106)Portela, M.M., Quintela, A.C. (2006). Estimação em Portugal Continental de escoamento e de capacidades úteis de albufeiras de regularização na ausência de informação. Recursos Hídricos, 27(2), 7-18.Pulido-Calvo, I., Portela, M.M. (2007). Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. Journal of Hydrology, 332(1-2), 1-15. doi:10.1016/j.jhydrol.2006.06.015Pulido-Calvo, I., Gutiérrez-Estrada, J.C., Savic, D. (2012). Heuristic modelling of the water resources management in the Guadalquivir River Basin, Southern Spain. Water Resources Management, 26(1), 185-209. doi:10.1007/s11269-011-9912-0Qian, B., Corte-Real, J.M., Xu, H. (2000a). Is the North Atlantic Oscillation the most important atmospheric pattern for precipitation in Europe? Journal of Geophysical Research, 105(D9), 901-910. doi:10.1029/2000JD900102Qian, B., Xu, H., Corte-Real, J.M. (2000b). Spatial-temporal structures of the quasi-periodic oscillations in precipitation over Europe. International Journal of Climatology, 20(13), 1583-1598. doi:10.1002/1097-0088(20001115)20:133.0.CO;2-YRodwell, M.J. (2003). On the predictability of the North Atlantic climate. The North Atlantic Oscillation: climate significance and environmental impact, Geophysical Monograph, 134, 173-192, Amer. Geophys. Union. doi:10.1029/134GM08Rossi, G. (2003). Requisites for a drought watch system. In: G. Rossi et al. (eds), Tools for Drought Mitigation in Mediterranean Regions, pp. 147-157. Kluwer Academic Publishing: Dordrecht. doi:10.1007/978-94-010-0129-8_9Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536. doi:10.1038/323533a0Santos, J.A., Corte-Real, J., Leite, S.M. (2005). Weather regimes and their connection to the winter precipitation in Portugal. International Journal of Climatology, 25(1), 33-50. doi:10.1002/joc.1101Santos, J.F., Portela, M.M., Pulido-Calvo, I. (2011). Regional frequency analysis of droughts in Portugal. Water Resources Management, 25(14), 3537-3558. doi:10.1007/s11269-011-9869-zSantos, J.F., Portela, M.M., Pulido-Calvo, I. (2013). Dimensionality reduction in drought modelling. Hydrological Processes, 27(10), 1399-1410. doi:10.1002/hyp.9300Santos, J.F., Portela, M.M., Pulido-Calvo, I., (2014). Spring drought prediction based on winter NAO and global SST in Portugal, Hydrological Processes, 28(3), 1009-1024. doi:10.1002/hyp.9641Santos, J.F., Pulido-Calvo, I., Portela, M.M. (2010). Spatial and temporal variability of droughts in Portugal. Water Resources Research, 46(3). DOI: 10.1029/2009WR008071. doi:10.1029/2009WR008071Senthil-Kumar, A.R., Sudheer, K.P., Jain, S.K., Agarwal, P.K. (2005). Rainfall-runoff modelling using artificial neural networks: comparison of network types. Hydrological Processes, 19(6), 1277-1291. doi:10.1002/hyp.5581Silva, A.T., Portela, M.M., Naghettini, M. (2012), Nonstationarities in the occurrence rates of flood events in Portuguese watersheds. Journal of Hydrology and Earth System Sciences, 16, 241-254. doi:10.5194/hess-16-241-2012Smith, T.M., Reynolds, R.W., Peterson, T.C. Lawrimore, J. (2008). Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis (1880-2006). Journal of Climate, 21, 2283-2296. doi:10.1175/2007JCLI2100.1Snedecor, G.W., Cochran, W.G. (1989). Statistical methods, Ames, Iowa State University Press (8th edition), Iowa, USA.Trigo, R.M., Osborn, T.J., Corte-Real, J.M. (2002). The North Atlantic Oscillation influence on Europe. Climate impacts and associated physical mechanisms. Climate Research, 20, 9-17. doi:10.3354/cr020009Trigo, R.M., Pozo-Vázquez, D., Osborn, T.J., Castro-Díez, Y., Gámiz-Fortis, S., Esteban-Parra, M.J. (2004). North Atlantic Oscillation influence on precipitation, river flow and water resources in the Iberian Peninsula. International Journal of Climatology, 24(8), 925-944. doi:10.1002/joc.1048Trigo, R., Xoplaki, E., Zorita, E., Luterbacher, J., Krichak, S.O., Alpert, P., Jacobeit, J., Sáenz, J., Fernández, J., González-Rouco, F., García-Herrera, R., Rodo, X., Brunetti, M., Nanni, T., Maugeri, M., Trkes, M., Gimeno, L., Ribera, P., Brunet, M., Trigo, I.F., Crepon, M., Mariotti, A. (2006). Relations between Variability in the Mediterranean region and mid-latitude variability. In: Mediterranean Climate Variability, edited by: Lionello P., Malanotte-Rizzoli P., e R. Boscolo. Amsterdam, Elsevier, 179-226. doi:10.1016/s1571-9197(06)80006-6Vicente-Serrano, S.M., López-Moreno, J.I., Lorenzo-Lacruz, J., El Kenawy, A., Azorin-Molina, C., Morán-Tejeda, E., Pasho, E., Zabalza, J., Beguería, S., Angulo-Martínez, M. (2011). The NAO impact on droughts in the Mediterranean region. In: VicenteSerrano S.M. e Trigo R. (Eds.), Hydrological, socioeconomic and ecological impacts of the North Atlantic Oscillation in the Mediterranean region. Advances in Global Research (AGLO) series, Springer-Verlag. doi:10.1007/978-94-007-1372-7_3Vinther, B.M., Andersen, K.K., Hansen, A.W., Schmith, T., Jones, P.D. (2003). Improving the Gibraltar/Reykjavik NAO Index. Geophysical Research Letters, 30(23), 2222. doi:10.1029/2003GL018220Xoplaki E., González-Rouco J.F., Luterbacher J. e H. Wanner, (2004). Wet season Mediterranean precipitation variability: influence of large-scale dynamics and predictability. Climate Dynamiques 23, 63-78.Xue, Y., Smith, T.M., Reynolds, R.W. (2003). Interdecadal changes of 30-yr SST normals during 1871-2000. Journal of Climate, 16, 1601-1612. doi:10.1175/1520-0442-16.10.1601Yevjevich, V. (1972). Stochastic Processes in Hydrology. Water Resources Publications, Fort Collins, Co

Ovine HSP90AA1 Expression Rate Is Affected by Several SNPs at the Promoter under Both Basal and Heat Stress Conditions

The aim of this work was to investigate the association between polymorphisms located at the HSP90AA1 ovine gene promoter and gene expression rate under different environmental conditions, using a mixed model approach. Blood samples from 120 unrelated rams of the Manchega sheep breed were collected at three time points differing in environmental conditions. Rams were selected on the basis of their genotype for the transversion G/C located 660 base pairs upstream the gene transcription initiation site. Animals were also genotyped for another set of 6 SNPs located at the gene promoter. Two SNPs, G/C-660 and A/G-444, were associated with gene overexpression resulting from heat stress. The composed genotype CC-660-AG-444 was the genotype having the highest expression rates with fold changes ranging from 2.2 to 3.0. The genotype AG-522 showed the highest expression levels under control conditions with a fold change of 1.4. Under these conditions, the composed genotype CC-601-TT-524-AG-522-TT-468 is expected to be correlated with higher basal expression of the gene according to genotype frequencies and linkage disequilibrium values. Some putative transcription factors were predicted for binding sites where the SNPs considered are located. Since the expression rate of the gene under alternative environmental conditions seems to depend on the composed genotype of several SNPs located at its promoter, a cooperative regulation of the transcription of the HSP90AA1 gene could be hypothesized. Nevertheless epigenetic regulation mechanisms cannot be discarded. © 2013 Salces-Ortiz et al

Combining support vector machines and segmentation algorithms for efficient anomaly detection: a petroleum industry application

Proceedings of: International Joint Conference SOCO’14-CISIS’14-ICEUTE’14, Bilbao, Spain, June 25th–27th, 2014, ProceedingsAnomaly detection is the problem of finding patterns in data that do not conform to expected behavior. Similarly, when patterns are numerically distant from the rest of sample, anomalies are indicated as outliers. Anomaly detection had recently attracted the attention of the research community for real-world applications. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct, or react to the situations associated with them. In that sense, heavy extraction machines for pumping and generation operations like turbomachines are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. For dealing with this and with the lack of labeled data, in this paper we propose a combination of a fast and high quality segmentation algorithm with a one-class support vector machine approach for efficient anomaly detection in turbomachines. As result we perform empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.This work was partially funded by CNPq BJT Project 407851/2012-7 and CNPq PVE Project 314017/2013-

Protection from Staphylococcus aureus mastitis associated with poly-N-acetyl beta-1,6 glucosamine specific antibody production using biofilm-embedded bacteria

Staphylococcus aureus vaccines based on bacterins surrounded by slime, surface polysaccharides coupled to protein carriers and polysaccharides embedded in liposomes administered together with non-biofilm bacterins confer protection against mastitis. However, it remains unknown whether protective antibodies are directed to slime-associated known exopolysaccharides and could be produced in the absence of bacterin immunizations. Here, a sheep mastitis vaccination study was carried out using bacterins, crude bacterial extracts or a purified exopolysaccharide from biofil

Is It Rational to Assume that Infants Imitate Rationally? A Theoretical Analysis and Critique

It has been suggested that preverbal infants evaluate the efficiency of others' actions (by applying a principle of rational action) and that they imitate others' actions rationally. The present contribution presents a conceptual analysis of the claim that preverbal infants imitate rationally. It shows that this ability rests on at least three assumptions: that infants are able to perceive others' action capabilities, that infants reason about and conceptually represent their own bodies, and that infants are able to think counterfactually. It is argued that none of these three abilities is in place during infancy. Furthermore, it is shown that the idea of a principle of rational action suffers from two fallacies. As a consequence, is it suggested that it is not rational to assume that infants imitate rationally. Copyright (C) 2012 S. Karger AG, Base

Populism and feminist politics: The cases of Finland and Spain

While populism has been subject to growing scholarly interest, its relationship to feminist politics has remained conspicuously understudied. This article investigates this relationship by analysing two cases of European populism: left populism in Spain (Podemos), and right populism in Finland (the Finns Party). The questions asked, and the challenges posed to feminist politics from populist political forces are intriguing: How is feminist politics articulated in both left and right populism? What differences can be discerned between left and right populism for feminist politics? To explore this, the article analyses three core dimensions: (1) political representation: descriptive representation (numbers of women, men and minority positions) and substantive representation (policy content in relation to gender equality); (2) populist parties’ formal and informal gender institutions such as internal quotas, gender equality plans and institutional culture; and (3) dedicated spaces for feminist politics such as women’s sections or feminist groups. It is argued that political ideology matters for feminist politics, and while left parties are more responsive to feminist concerns and populism poses specific problems for feminist politics, it is the gendered culture of political parties that ensures both left and right parties are problematic for feminist politics

Measurement of the Bs0→J/ψKS0B_s^0\to J/\psi K_S^0 branching fraction

The $B_s^0\to J/\psi K_S^0$ branching fraction is measured in a data sample corresponding to 0.41$fb^{-1}$ of integrated luminosity collected with the LHCb detector at the LHC. This channel is sensitive to the penguin contributions affecting the sin2$\beta$ measurement from $B^0\to J/\psi K_S^0$ The time-integrated branching fraction is measured to be $BF(B_s^0\to J/\psi K_S^0)=(1.83\pm0.28)\times10^{-5}$. This is the most precise measurement to date

Model-independent search for CP violation in D0→K−K+π−π+ and D0→π−π+π+π− decays

A search for CP violation in the phase-space structures of D0 and View the MathML source decays to the final states K−K+π−π+ and π−π+π+π− is presented. The search is carried out with a data set corresponding to an integrated luminosity of 1.0 fb−1 collected in 2011 by the LHCb experiment in pp collisions at a centre-of-mass energy of 7 TeV. For the K−K+π−π+ final state, the four-body phase space is divided into 32 bins, each bin with approximately 1800 decays. The p-value under the hypothesis of no CP violation is 9.1%, and in no bin is a CP asymmetry greater than 6.5% observed. The phase space of the π−π+π+π− final state is partitioned into 128 bins, each bin with approximately 2500 decays. The p-value under the hypothesis of no CP violation is 41%, and in no bin is a CP asymmetry greater than 5.5% observed. All results are consistent with the hypothesis of no CP violation at the current sensitivity

Measurement of the CP-violating phase \phi s in Bs->J/\psi\pi+\pi- decays

Measurement of the mixing-induced CP-violating phase phi_s in Bs decays is of prime importance in probing new physics. Here 7421 +/- 105 signal events from the dominantly CP-odd final state J/\psi pi+ pi- are selected in 1/fb of pp collision data collected at sqrt{s} = 7 TeV with the LHCb detector. A time-dependent fit to the data yields a value of phi_s=-0.019^{+0.173+0.004}_{-0.174-0.003} rad, consistent with the Standard Model expectation. No evidence of direct CP violation is found.Comment: 15 pages, 10 figures; minor revisions on May 23, 201