GARRYA OVATA BENTH. SUBSP. GOLDMANII (WOOTON & STANDL.) DAHLING (GARRYACEAE) AND BESCHORNERIA CALCICOLA GARCÍA-MENDOZA (AGAVACEAE): TWO NEW REPORTS FROM VERACRUZ, MEXICO.

New reports of Garrya ovata ssp. goldmanii(Wooton & Standl.) Dahling and Beschorneriacalcicola García-Mendoza are presented forthe state of Veracruz, Mexico. Both taxa werefound on karstic hills in the arid zone ofVeracruz near Tenextepec, Perote. Garryaovata ssp. goldmanii is a small shrub measuringup to 1.5 m tall, which contrasts considerablywith G. laurifolia Benth. ssp. laurifolia and G.laurifolia Benth. ssp. macrophylla alreadyregistered for Veracruz. These are shrubs ortrees from 2 to 10 m tall. Beschorneriacalcicola is a small rosettiform plant measuringup to 60 cm tall which contrasts with B.yuccoides K. Koch ssp. dekosteriana (K.Koch) García-Mendoza of Veracruz, which is alarge plant from 1 to 2.5 m tall, with large wideleaves.Se encontraron como nuevos registros paraVeracruz Garrya ovata ssp. goldmanii(Wooton & Standl.) Dahling y Beschorneriacalcicola García-Mendoza, ambas seencontraron en los cerros calizos de la zonaárida veracruzana, en los alrededores deTenextepec, Perote. Garrya ovata ssp.goldmanii es un arbusto pequeño que midehasta 1.5 m de alto, contrastandoconsiderablemente con G. laurifolia Benth. ssp.laurifolia y G. laurifolia Benth. ssp.macrophylla ya registradas de Veracruz, lascuales son arbustos o árboles de 2 a 10 m dealto. De la misma forma Beschorneriacalcicola es una planta pequeña, rosetiformeque mide hasta 60 cm de alto, en contraste conB. yuccoides K. Koch ssp. dekosteriana(K.Koch) García-Mendoza conocida paraVeracruz, que es una planta grande de 1- 2.5 mde alto, con hojas grandes y anchas

Effect of the Target Size in the Calculation of the Energy Deposited Using PENELOPE Code

The specific and linear energy was calculated in target sizes of 10 μm, 5 μm, 1 μm, 60 nm, 40nm and 20 nm by taking into account the contribution of the primary photon beams and the electrons generated by them in LiF: Mg, Ti (TLD-100). The simulations were carried out by the code PENELOPE 2011. Using different histories of primary particles, for each energy beams the mean deposited energy is the same, but to achieve a statistical deviation lower than 1% the value of 108was fixed. We find that setting the values C1 = 0.1 C2 = 0.1 and Wcc = Wcr = 50 eV the time of simulation decreases around the 25%. The uncertainties (1 SD) in the specific energy increases with energy for all target sizes and decreases with target size, with values from 1.7 to 94% for 20 nm and between 0.1 and 0.8% for 10 μm. As expected, the specific and linear energies decrease with target size but not in a geometrical behavior

In the quest of specific-domain ontology components for the semantic web

This paper describes an approach we have been using to identify specific-domain ontology components by using Self-Organizing Maps. These components are clustered together in a natural way according to their similarity. The knowledge maps, as we call them, show colored regions containing knowledge components that may be used to populate an specific-domain ontology. Later, these ontology may be used by software agents to carry out basic reasoning task on our behalf. In particular, we deal with the issue of not constructing the ontology from scratch, our approach helps us to speed up the ontology creation process

Beetles (Coleoptera) of Peru: A Survey of the Families. Scarabaeoidea

The 1042 species of Scarabaeoidea known to occur in Peru are listed with their taxonomic placement in families, subfamilies, and tribes

Consultoría a una asociación civil enfocada en el tratamiento contra las adicciones en el municipio de Tlajomulco de Zúñiga, Jalisco

En el presente trabajo se presenta la serie de acciones realizadas para una labor de consultoría en una Mypes. El objetivo principal de este proyecto consiste en proporcionar a la MYPE un medio para contribuir a su consolidación, a través de un modelo de consultoría con intervención de estudiantes universitarios que da origen al nombre de “Consultorías Universitarias” y que es fomentado por el Centro Universidad Empresa del CUE. Primero se realizó un análisis contextual para entender el mercado y regulación de acción, posteriormente se realizó un diagnostico donde se encontró que la empresa estaba en un nivel confiable de maduración y que el problema principal había sido su crecimiento descontrolado basado en una deficiente planeación estratégica. Para favorecer al crecimiento controlado de la Mypes el equipo de trabajo se enfocó en realizar labores de estandarización a través de: diferentes manuales de procedimientos en las áreas de recursos humanos, mercadotecnia y administración, ayudar a entender la importancia de una planeación con objetivos operativos y estratégicos, además a lograr enfocar la acción social de la A.C. a mercados que podrían verse beneficiados en una relación ganar-ganar y a sus finanzas que deben tener un marco especifico de ganancia y reinversión. Los resultados de la implementación indican que la empresa logro adecuar su labor práctica a su área organizacional y sobre todo tener claro cómo, por qué, cuándo y dónde iban a enfocar sus actividades para el desarrollo.ITESO, A.C

In the quest of specific-domain ontology components for the semantic web

This paper describes an approach we have been using to identify specific-domain ontology components by using Self-Organizing Maps. These components are clustered together in a natural way according to their similarity. The knowledge maps, as we call them, show colored regions containing knowledge components that may be used to populate an specific-domain ontology. Later, these ontology may be used by software agents to carry out basic reasoning task on our behalf. In particular, we deal with the issue of not constructing the ontology from scratch, our approach helps us to speed up the ontology creation process

Alert classification for the ALeRCE broker system: The real-time stamp classifier

We present a real-time stamp classifier of astronomical events for the Automatic Learning for the Rapid Classification of Events broker, ALeRCE. The classifier is based on a convolutional neural network, trained on alerts ingested from the Zwicky Transient Facility (ZTF). Using only the science, reference, and difference images of the first detection as inputs, along with the metadata of the alert as features, the classifier is able to correctly classify alerts from active galactic nuclei, supernovae (SNe), variable stars, asteroids, and bogus classes, with high accuracy (~94%) in a balanced test set. In order to find and analyze SN candidates selected by our classifier from the ZTF alert stream, we designed and deployed a visualization tool called SN Hunter, where relevant information about each possible SN is displayed for the experts to choose among candidates to report to the Transient Name Server database. From 2019 June 26 to 2021 February 28, we have reported 6846 SN candidates to date (11.8 candidates per day on average), of which 971 have been confirmed spectroscopically. Our ability to report objects using only a single detection means that 70% of the reported SNe occurred within one day after the first detection. ALeRCE has only reported candidates not otherwise detected or selected by other groups, therefore adding new early transients to the bulk of objects available for early follow-up. Our work represents an important milestone toward rapid alert classifications with the next generation of large etendue telescopes, such as the Vera C. Rubin Observatory.The authors acknowledge support from the National Agency of Research and Development’s Millennium Science Initiative through grant IC12009, awarded to the Millennium Institute of Astrophysics (RC, ER, CV, FF, PE, GP, FEB, IR, PSS, GC, SE, Ja, EC, DR, DRM, MC) and from the National Agency for Research and Development (ANID) grants: BASAL Center of Mathematical Modelling AFB-170001 (CV, FF, IR, ECN, CS, ECI) and Centro de Astrofísica y Tecnologías Afines AFB170002 (FEB, PSS, MC); FONDECYT Regular #1171678 (PE), #1200710 (FF), #1190818(FEB), #1200495 (FEB), #1171273 (MC), #1201793(GP); FONDECYT Postdoctorado #3200250 (PSS); FONDECYT Iniciación #11191130 (CV); Magíster Nacional 2019 #22190947 (ER). This work was funded in part by project CORFO 10CEII-9157 Inria Chile (PS). The authors acknowledge financial support from the Spanish Ministry of Science, Innovation, and Universities (MICIU) under the 2019 Ramón y Cajal program RYC2019- 027683 (LG)

A meta-analysis of state-of-the-art electoral prediction from Twitter data

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.Comment: 19 pages, 3 table