Edad al primer parto y productividad lechera del ganado bovino Holstein en la costa central del Perú

The aim of this study was to determine the effect of age at first calving (AFC) on the standardized production of milk (SMP) of the first and second lactation and interval between the first two calvings (CI) in Holstein cows of the Lima region, Peru. Records of 4215 cows from nine farms, registered in the Dairy Productivity Service of the National Agrarian University La Molina, between January 2003 and December 2012 were analysed. The information was analysed through descriptive statistics and by a mixed linear model, using SAS 9.4. The average AFC was 24.9 ± 2.3 months. The AEPP influenced on the SMP (p=0.0008), but not in the IEP (p=0.3969). Likewise, significant differences were found (p<0.01) between the two lactations, between the four seasons of the year and between the 10 years evaluated for the two variables under study. In conclusion, higher SMP and lower CI were observed at intermediate ages (22-30 months), so the optimal AFC would be around 22 months to achieve maximum productive performance.El objetivo del estudio fue determinar el efecto de la edad al primer parto (EPP) sobre la producción estandarizada de leche (PEL) de la primera y segunda lactación e intervalo entre los primeros dos partos (IEP) en vacas Holstein de la cuenca lechera de Lima, Perú. Se analizaron registros de 4215 vacas provenientes de nueve establos, registrados en el Servicio de Productividad Lechera de la Universidad Nacional Agraria La Molina, entre enero de 2003 y diciembre de 2012. La información se analizó mediante estadística descriptiva y un modelo lineal mixto, utilizando el paquete SAS 9.4. La EPP promedio fue de 24.9 ± 2.3 meses. La EPP influyó en la PEL (p=0.0008), pero no en el IEP (p=0.3969). Asimismo, se encontraron diferencias significativas (p<0.01) entre las dos lactaciones, entre las cuatro estaciones del año y entre los 10 años evaluados, para las dos variables en estudio. En conclusión, se observaron mayores PEL y menores IEP a edades intermedias (22-30 meses), por lo que la EPP óptima estaría alrededor de los 22 meses para lograr maximizar el rendimiento productivo

Cross-Language Plagiarism Detection

Cross-language plagiarism detection deals with the automatic identification and extraction of plagiarism in a multilingual setting. In this setting, a suspicious document is given, and the task is to retrieve all sections from the document that originate from a large, multilingual document collection. Our contributions in this field are as follows: (1) a comprehensive retrieval process for cross-language plagiarism detection is introduced, highlighting the differences to monolingual plagiarism detection, (2) state-of-the-art solutions for two important subtasks are reviewed, (3) retrieval models for the assessment of cross-language similarity are surveyed, and, (4) the three models CL-CNG, CL-ESA and CL-ASA are compared. Our evaluation is of realistic scale: it relies on 120,000 test documents which are selected from the corpora JRC-Acquis and Wikipedia, so that for each test document highly similar documents are available in all of the six languages English, German, Spanish, French, Dutch, and Polish. The models are employed in a series of ranking tasks, and more than 100 million similarities are computed with each model. The results of our evaluation indicate that CL-CNG, despite its simple approach, is the best choice to rank and compare texts across languages if they are syntactically related. CL-ESA almost matches the performance of CL-CNG, but on arbitrary pairs of languages. CL-ASA works best on "exact" translations but does not generalize well.This work was partially supported by the TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 project and the CONACyT-Mexico 192021 grant.Potthast, M.; Barrón Cedeño, LA.; Stein, B.; Rosso, P. (2011). Cross-Language Plagiarism Detection. Language Resources and Evaluation. 45(1):45-62. https://doi.org/10.1007/s10579-009-9114-zS4562451Ballesteros, L. A. (2001). Resolving ambiguity for cross-language information retrieval: A dictionary approach. PhD thesis, University of Massachusetts Amherst, USA, Bruce Croft.Barrón-Cedeño, A., Rosso, P., Pinto, D., & Juan A. (2008). On cross-lingual plagiarism analysis using a statistical model. In S. Benno, S. Efstathios, & K. Moshe (Eds.), ECAI 2008 workshop on uncovering plagiarism, authorship, and social software misuse (PAN 08) (pp. 9–13). Patras, Greece.Baum, L. E. (1972). An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process. Inequalities, 3, 1–8.Berger, A., & Lafferty, J. (1999). Information retrieval as statistical translation. In SIGIR’99: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (vol. 4629, pp. 222–229). Berkeley, California, United States: ACM.Brin, S., Davis, J., & Garcia-Molina, H. (1995). Copy detection mechanisms for digital documents. In SIGMOD ’95 (pp. 398–409). New York, NY, USA: ACM Press.Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., & Mercer R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2), 263–311.Ceska, Z., Toman, M., & Jezek, K. (2008). Multilingual plagiarism detection. In AIMSA’08: Proceedings of the 13th international conference on artificial intelligence (pp. 83–92). Berlin, Heidelberg: Springer.Clough, P. (2003). Old and new challenges in automatic plagiarism detection. National UK Plagiarism Advisory Service, http://www.ir.shef.ac.uk/cloughie/papers/pas_plagiarism.pdf .Dempster A. P., Laird N. M., Rubin D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.Dumais, S. T., Letsche, T. A., Littman, M. L., & Landauer, T. K. (1997). Automatic cross-language retrieval using latent semantic indexing. In D. Hull & D. Oard (Eds.), AAAI-97 spring symposium series: Cross-language text and speech retrieval (pp. 18–24). Stanford University, American Association for Artificial Intelligence.Gabrilovich, E., & Markovitch, S. (2007). Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In Proceedings of the 20th international joint conference for artificial intelligence, Hyderabad, India.Hoad T. C., & Zobel, J. (2003). Methods for identifying versioned and plagiarised documents. American Society for Information Science and Technology, 54(3), 203–215.Levow, G.-A., Oard, D. W., & Resnik, P. (2005). Dictionary-based techniques for cross-language information retrieval. Information Processing & Management, 41(3), 523–547.Littman, M., Dumais, S. T., & Landauer, T. K. (1998). Automatic cross-language information retrieval using latent semantic indexing. In Cross-language information retrieval, chap. 5 (pp. 51–62). Kluwer.Maurer, H., Kappe, F., & Zaka, B. (2006). Plagiarism—a survey. Journal of Universal Computer Science, 12(8), 1050–1084.McCabe, D. (2005). Research report of the Center for Academic Integrity. http://www.academicintegrity.org .Mcnamee, P., & Mayfield, J. (2004). Character N-gram tokenization for European language text retrieval. Information Retrieval, 7(1–2), 73–97.Meyer zu Eissen, S., & Stein, B. (2006). Intrinsic plagiarism detection. In M. Lalmas, A. MacFarlane, S. M. Rüger, A. Tombros, T. Tsikrika, & A. Yavlinsky (Eds.), Proceedings of the European conference on information retrieval (ECIR 2006), volume 3936 of Lecture Notes in Computer Science (pp. 565–569). Springer.Meyer zu Eissen, S., Stein, B., & Kulig, M. (2007). Plagiarism detection without reference collections. In R. Decker & H. J. Lenz (Eds.), Advances in data analysis (pp. 359–366), Springer.Och, F. J., & Ney, H. (2003). A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1), 19–51.Pinto, D., Juan, A., & Rosso, P. (2007). Using query-relevant documents pairs for cross-lingual information retrieval. In V. Matousek & P. Mautner (Eds.), Lecture Notes in Artificial Intelligence (pp. 630–637). Pilsen, Czech Republic.Pinto, D., Civera, J., Barrón-Cedeño, A., Juan, A., & Rosso, P. (2009). A statistical approach to cross-lingual natural language tasks. Journal of Algorithms, 64(1), 51–60.Potthast, M. (2007). Wikipedia in the pocket-indexing technology for near-duplicate detection and high similarity search. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 909–909). ACM.Potthast, M., Stein, B., & Anderka, M. (2008). A Wikipedia-based multilingual retrieval model. In C. Macdonald, I. Ounis, V. Plachouras, I. Ruthven, & R. W. White (Eds.), 30th European conference on IR research, ECIR 2008, Glasgow , volume 4956 LNCS of Lecture Notes in Computer Science (pp. 522–530). Berlin: Springer.Pouliquen, B., Steinberger, R., & Ignat, C. (2003a). Automatic annotation of multilingual text collections with a conceptual thesaurus. In Proceedings of the workshop ’ontologies and information extraction’ at the Summer School ’The Semantic Web and Language Technology—its potential and practicalities’ (EUROLAN’2003) (pp. 9–28), Bucharest, Romania.Pouliquen, B., Steinberger, R., & Ignat, C. (2003b). Automatic identification of document translations in large multilingual document collections. In Proceedings of the international conference recent advances in natural language processing (RANLP’2003) (pp. 401–408). Borovets, Bulgaria.Stein, B. (2007). Principles of hash-based text retrieval. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 527–534). ACM.Stein, B. (2005). Fuzzy-fingerprints for text-based information retrieval. In K. Tochtermann & H. Maurer (Eds.), Proceedings of the 5th international conference on knowledge management (I-KNOW 05), Graz, Journal of Universal Computer Science. (pp. 572–579). Know-Center.Stein, B., & Anderka, M. (2009). Collection-relative representations: A unifying view to retrieval models. In A. M. Tjoa & R. R. Wagner (Eds.), 20th International conference on database and expert systems applications (DEXA 09) (pp. 383–387). IEEE.Stein, B., & Meyer zu Eissen, S. (2007). Intrinsic plagiarism analysis with meta learning. In B. Stein, M. Koppel, & E. Stamatatos (Eds.), SIGIR workshop on plagiarism analysis, authorship identification, and near-duplicate detection (PAN 07) (pp. 45–50). CEUR-WS.org.Stein, B., & Potthast, M. (2007). Construction of compact retrieval models. In S. Dominich & F. Kiss (Eds.), Studies in theory of information retrieval (pp. 85–93). Foundation for Information Society.Stein, B., Meyer zu Eissen, S., & Potthast, M. (2007). Strategies for retrieving plagiarized documents. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 825–826). ACM.Steinberger, R., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., Tufis, D., & Varga, D. (2006). The JRC-Acquis: A multilingual aligned parallel corpus with 20+ languages. In Proceedings of the 5th international conference on language resources and evaluation (LREC’2006).Steinberger, R., Pouliquen, B., & Ignat, C. (2004). Exploiting multilingual nomenclatures and language-independent text features as an interlingua for cross-lingual text analysis applications. In Proceedings of the 4th Slovenian language technology conference. Information Society 2004 (IS’2004).Vinokourov, A., Shawe-Taylor, J., & Cristianini, N. (2003). Inferring a semantic representation of text via cross-language correlation analysis. In S. Becker, S. Thrun, & K. Obermayer (Eds.), NIPS-02: Advances in neural information processing systems (pp. 1473–1480). MIT Press.Yang, Y., Carbonell, J. G., Brown, R. D., & Frederking, R. E. (1998). Translingual information retrieval: Learning from bilingual corpora. Artificial Intelligence, 103(1–2), 323–345

Diseño para el desarrollo sustentable y la habitabilidad segura e incluyente

Este libro se divide en dos partes que permiten permear en el campo de la enseñanza del diseño; la primera se enfoca en temáticas que se desprenden del diseño en la educación para la sustentabilidad; en la segunda, se identifican las tendencias del diseño como un modo de verlo y sentirlo: va desde el diseño emocional hacia uno de conservación, reúso y reparación de objetos para reducir el consumo de recursos materiales

Gender differences and management of stroke risk of nonvalvular atrial fibrillation in an upper middle-income country: Insights from the CARMEN-AF registry

Background: Atrial Fibrillation (AF) is associated with an increased risk of stroke and systemic embolism. Several studies have suggested that female AF patients could have a greater risk for stroke. There is scarce information about clinical characteristics and use of antithrombotic therapies in Latin American patients with nonvalvular AF. Objective: To describe the gender differences in clinical characteristics, thromboembolic risk, and antithrombotic therapy of patients with nonvalvular AF recruited in Mexico, an upper middle-income country, into the prospective national CARMEN-AF Registry. Methods: A total of 1423 consecutive patients, with at least one thromboembolic risk factor were enrolled in CARMEN-AF Registry during a three-year period (2014–2017). They were categorized according to Gender. Results: Overall, 48.6% were women, mean age 70 ± 12 years. Diabetes, smoking, alcoholism, non-ischemic cardiomyopathy, coronary artery disease, and obstructive sleep apnea were higher in men. Most women were found with paroxysmal AF (40.6%), and most men with permanent AF (44.0%). No gender differences were found in the use of vitamin K antagonists (VKA) (30.5% in women vs. 28.0% in men). No gender differences were found in the use of direct oral anticoagulants (DOAC) (33.8% women vs 35.4% men). Conclusions: CARMEN-AF Registry demonstrates that in Mexico, regardless of gender, a large proportion of patients remain undertreated. No gender differences were found in the use of VKA or DOAC. Keywords: Atrial fibrillation, Gender, Thromboembolic risk, Antithrombotic therapy, Stroke, Mexic

Compilación de Proyectos de Investigacion de 1984-2002

Instituto Politecnico Nacional. UPIICS