Pearl Cleage: Torn Between Hope and Despair

El presente artículo analiza el desarrollo que Pearl Cleage hace de la oposición binaria esperanza- desesperación en su obra teatral. Se describe brevemente la vida de esta dramaturga negra contemporánea y se estudian una a una sus cuatro obras teatrales más importantes: Chain, Blues for an Alabama Sky, Bourbon at the Border y Flyin’ West. Además, se mencionan varias de sus obras menores, como son Late Bus to Mecca, A Song for Coretta y The Nacirema Society Requests the Honor of Your Presence at a Celebration of Their First One Hundred Years. El estudio de la obra de Cleage se hace desde el marco teórico generado por el feminismo afroamericano contemporáneo y se contrasta su obra teatral con la de otras dramaturgas negras actuales.The current essay analyses the development of the binary opposition hope-despair that Pearl Cleage does in her dramatic texts. The life of this contemporary African American playwright is described briefly, and her four most important plays are analysed: Chain, Blues for an Alabama Sky, Bourbon at the Border, and Flyin’ West. Several of her other minor works are also mentioned: Late Bus to Mecca, A Song for Coretta, and The Nacirema Society Requests the Honor of Your Presence at a Celebration of Their First One Hundred Years. The current essay studies Cleage’s work using African American feminism as a theoretical framework, and it contrasts her theatrical work with that of other contemporary African American women playwrights

Pearl Cleage: entre la esperanza y la desesperación

El presente artículo analiza el desarrollo que Pearl Cleage hace de la oposición binaria esperanza-desesperación en su obra teatral. Se describe brevemente la vida de esta dramaturganegra contemporánea y se estudian una a una sus cuatro obras teatrales más importantes: Chain, Blues for an Alabama Sky, Bourbon at the Border y Flyin’ West. Además, se mencionan varias de sus obras menores, como son Late Bus to Mecca, A Song for Coretta y The Nacirema Society Requests the Honor of Your Presence at a Celebration of Their First One Hundred Years. El estudio de la obra de Cleage se hace desde el marco teórico generado por el feminismo afroamericano contemporáneo y se contrasta su obra teatral con la de otras dramaturgas negras actuales

Proteogenomic Study of the Effect of an Improved Mixed Diet of Live Preys on the Aquaculture of Octopus vulgaris Paralarvae

This work was part of the I+D+i grant AGL2017-89475-C2-1 -OCTOMICS, funded by MCIN/AEI/10.13039/501100011033. Funds will be also received for open access publication fees from a collaboration agreement between Innovation Galician Agency and CSIC (Program Agreement 2021-2022).The common octopus is the most demanded cephalopod species for human consumption. Despite important advances realized recently, the main bottleneck for commercial production of the common octopus, Octopus vulgaris, is the mass mortality of paralarvae in the first 15–20 days post-hatching (dph), with the main responsible factors still unknown. Thus, the identification of the limiting culture factors is, therefore, crucial for their aquaculture. This study investigates proteomic and transcriptomic responses of octopus paralarvae fed on an improved live preys-mixed diet (M) compared to an Artemia-based (A) reference diet. M diet resulted in the highest paralarvae specific growth rate obtained to date under culture conditions. This is supported by most of the proteins and genes over-expressed in the M group being linked to the cell cycle and replication, production of structural components, and development of the nervous system. Furthermore, the differential nutritional regulation of several genes and proteins seems to indicate that, instead of fatty acids, the preferred fuels for cephalopods would be proteins and carbohydrates. Also, M diet provides a better nutrient balance, which has allowed carrying out this comparative study in paralarvae under optimal conditions at a more advanced stage of growth (20 dph) than in previous studies. Moreover, the paralarvae culture extended up to 40 dph showed for the first time a proper pre-settlement behavior.MCIN/AEI AGL2017-89475-C2-1Innovation Galician AgencyCSI

TAPON: a two-phase machine learning approach for semantic labelling

Through semantic labelling we enrich structured information from sources such as HTML pages, tables, or JSON files, with labels to integrate it into a local ontology. This process involves measuring some features of the information and then nding the classes that best describe it. The problem with current techniques is that they do not model relationships between classes. Their features fall short when some classes have very similar structures or textual formats. In order to deal with this problem, we have devised TAPON: a new semantic labelling technique that computes novel features that take into account the relationships. TAPON computes these features by means of a two-phase approach. In the first phase, we compute simple features and obtain a preliminary set of labels (hints). In the second phase, we inject our novel features and obtain a refined set of labels. Our experimental results show that our technique, thanks to our rich feature catalogue and novel modelling, achieves higher accuracy than other state-of-the-art techniques.Ministerio de Economía y Competitividad TIN2016-75394-

A neural network for semantic labelling of structured information

Intelligent systems rely on rich sources of information to make informed decisions. Using information from external sources requires establishing correspondences between the information and known information classes. This can be achieved with semantic labelling, which assigns known labels to structured information by classifying it according to computed features. The existing proposals have explored different sets of features, without focusing on what classification techniques are used. In this paper we present three contributions: first, insights on architectural issues that arise when using neural networks for semantic labelling; second, a novel implementation of semantic labelling that uses a state-of-the-art neural network classifier which achieves significantly better results than other four traditional classifiers; third, a comparison of the results obtained by the former network when using different subsets of features, comparing textual features to structural ones, and domain-dependent features to domain-independent ones. The experiments were carried away with datasets from three real world sources. Our results show that there is a need to develop more semantic labelling proposals with sophisticated classification techniques and large features catalogues.Ministerio de Economía y Competitividad TIN2016-75394-

El Ministerio de Robin Hood: una experiencia de gamificación

Llevar al aula un contenido a través de la organización y las reglas de un juego, implica al alumnado en su aprendizaje, le proporciona un sentimiento de control y autonomía, aumenta su motivación y le ofrece una forma diferente de aprender. No es lo mismo plantear que durante las próximas clases se va a trabajar para intentar mejorar nuestro presupuesto doméstico, que comenzar diciéndole al alumnado que ha sido elegido para formar parte del Ministerio de Robin Hood y su misión será asesorar y ayudar a ahorrar a las familias que tengan dificultades para llegar a final de mes. Gamificar nos permitirá, partiendo del currículo, organizar la clase en torno a un juego, definir reglas y dinámicas que motiven al alumnado e introducirlo en el juego a través de una narrativa, una historia

Understanding CLIL from an ELF perspective: language in Taiwanese primary bilingual education

Recent global developments have intensified the use of English as a lingua franca (ELF), the principal means of communication employed among speakers of different linguistic backgrounds to interact worldwide. Consequently, there has been a growing interest in the pedagogical implications and applications of ELF in language teaching and learning. Few works, though, have investigated the influence of ELF in bilingual education such as in Content and Language Integrated Learning (CLIL). The current paper describes the design and implementation of a CLIL + ELF observation tool that was used to study a pilot CLIL program in Taiwan and to anticipate CLIL teachers’ training needs. The data collected from the rubric were contrasted with several unstructured interviews. The rubric contains 10 criteria developed from previous CLIL and ELF studies including: learners’ L1 and L2 proficiency; teachers’ L2 proficiency; teachers’ ability to reflect upon their practice; their familiarity with CLIL and ELF methodologies; and the school’s commitment to bilingual education and language policy considerations. Using these criteria, the researchers identified many positive results such as teachers’ growing familiarity with CLIL and their use of class management language, content-related language, and academic communication. The study also suggests areas for improvement such as the need for teacher training regarding ELF.Ministry of Education, Taiwa

LEAPME: learning-based property matching with embeddings

Data integration tasks such as the creation and extension of knowledge graphs involve the fusion of heterogeneous entities from many sources. Matching and fusion of such entities require to also match and combine their properties (attributes). However, previous schema matching approaches mostly focus on two sources only and often rely on simple similarity measurements. They thus face problems in challenging use cases such as the integration of heterogeneous product entities from many sources. We therefore present a new machine learning-based property matching approach called LEAPME (LEArning-based Property Matching with Embeddings) that utilizes numerous features of both property names and instance values. The approach heavily makes use of word embeddings to better utilize the domain-specific semantics of both property names and instance values. The use of supervised machine learning helps exploit the predictive power of word embeddings. Our comparative evaluation against five baselines for several multi-source datasets with real-world data shows the high effectiveness of LEAPME. We also show that our approach is even effective when training data from another domain (transfer learning) is used.Ministerio de Economía y Competitividad TIN2016-75394-RMinisterio de Ciencia e Innovación PID2019-105471RB-I00Junta de Andalucía P18-RT-106