University of Alcalá

About: University of Alcalá is a education organization based out in Alcalá de Henares, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 10795 authors who have published 20718 publications receiving 410089 citations. The organization is also known as: University of Alcala & University of Alcala de Henares.

Zinc, Biochemical Markers of Nutrition, and Type I Osteoporosis

Abstract: Having observed previously that the reduction of levels of biological markers of nutrition in postmenopausal osteoporosis may be related to zinc deficiency, we measured plasma and urinary zinc concentrations in 30 women with postmenopausal osteoporosis and in 30 healthy postmenopausal women who served as controls. Plasma zinc levels did not differ between groups, but urinary zinc excretion was significantly higher in the women with postmenopausal osteoporosis (p = 0.002). The relation between total body bone mineral content corrected for body weight (TBBMC/W) and markers of nutrition was significant (multiple regression analysis: p < 0.0001) in the women with postmenopausal osteoporosis but not in the healthy postmenopausal controls. Likewise, the relation between TBBMC/W and plasma and urinary zinc levels also was significant in the women with postmenopausal osteoporosis but not in the controls (multiple regression analysis: p = 0.0022). Neither group showed any correlation between plasma or urinary zinc concentrations and levels of biological markers of nutrition. Urinary zinc concentration correlated significantly with serum tartrate-resistant acid phosphatase level (simple linear regression analysis: r = 0.583, p < 0.001) in the women with postmenopausal osteoporosis but not in controls. TBBMC correlated with urinary zinc concentration significantly in the women with postmenopausal osteoporosis (simple linear regression: r = 0.567, p = 0.0015), but the correlation was nonsignificant in healthy postmenopausal controls. These findings indicate that the elevation of urinary zinc elimination in osteoporosis is dependent on bone resorption.

Ontology-Based Competency Management: Infrastructures for the Knowledge Intensive Learning Organization

Abstract: Learning activities can be considered the final outcome of a complex process inside knowledge intensive organizations. This complex process encompasses a dynamic cycle, a loop in which business or organizational needs trigger the necessity of acquiring or enhancing human resource competencies that are essential to the fulfillment of the organizational objectives. This continuous evolution of organizational knowledge requires the management of records of available and required competencies, and the automation of such competency handling thus becomes a key issue for the effective functioning of knowledge management activities. This chapter describes the use of ontologies as the enabling semantic infrastructure of competency management, describing the main aspects and scenarios of the knowledge creation cycle from the perspective of its connection with competency definitions. Introduction and Background The “Semantic Web” vision described by Berners-Lee, Hendler, and Lassila (2003) has recently fostered research on the use of formal ontologies to support “intelligent” behaviors for a variety of Web applications. These applications include Web-based learning in a broad sense, which is commonly referred to as “e-learning” (Lytras, Tsilira, & Themistocleous, 2003). Nonetheless, the perspective of most of those current applications does not consider organizational needs as the essential driver for the elaboration and delivery of learning activities, but focuses on other aspects regarding technical, social, or usage issues from the perspective of the individual learner or informal communities of learners (Anderson & Whitelock, 2004). An organizational perspective to Semantic Web-enabled e-learning should focus on the role of learning activities in the broader framework of organizational learning (i.e., on providing a semantic account to existing learning processes). But in addition, the implications of the semantic approach to organizations should be explored as a source of new ideals and business designs for learning organizations (Ortenblad, 2001). According to this latter view, the Semantic Web can be considered as the enabler for a new model of a semantic learning organization (SLO) in which ontologies are the technological backbone for intelligent activities and semantics-enabled artifacts. A first step toward the definition of the concept of SLO is the analysis of the essential roles of ontologies in organizational learning. Since learning can be considered as an outcome of the need to acquire new competencies, it is worth first sketching the main components that surround such activities. Figure 1 provides an abstract, idealized view of such components. E-learning can be considered an important component of the knowledge management (KM) function, as described by Wild, Griggs, and Downing (2002). In fact, even some architectural guidelines for this integrated view have been described elsewhere (Metaxiotis, Psarras, & Papastefanatos, 2002), and the use of reusable learning objects in that context has also been analyzed recently (Lytras, Pouloudi, & Poulymenakou, 2002). This perspective puts an emphasis on Web technology-based learning activities inside the organization as enablers of knowledge acquisition activities. In consequence, e-learning becomes part of a more complex organizational conduct, in which lacks of required competencies trigger the search for appropriate contents or activities (i.e., learning objects), in an attempt to acquire knowledge and abilities that fulfill the contingent or strategic need. It should be noted that this approach does not preclude that other kinds of useful informal or incidental learning take place inside organizations (Matthews, 1999), but rather complement them with a more organizational goal-directed activity. In fact, recommender systems for exploiting employee interests like the one described by Lindgren, Stenmark, and Ljungberg (2003) could be built as a complement within the architecture described, also taking advantage of the richness of the underlying ontological structures. As illustrated in Figure 1, the process of acquisition (usually) starts from a business need emanated from the context of the organization, or eventually from strategic management (Rainer & Kazem, 1995). Such needs trigger the process of assessing if the organization is in place to deal with them. Such assessment is commonly referred to as knowledge gap analysis (Sunassee & Sewry, 2002) and essentially consists on matching the competencies required for the incoming needs with the available ones. Such competency management facilities are usually part of the human resources function (Soliman & Spooner, 2000), but this is not relevant for our present discussion. If the result is not satisfactory, the process of searching for available resources should start. This process may entail the selection of learning objects in external or internal repositories and the composition and delivery of the appropriate learning activities. After these activities take place, some kind of assessment would eventually end up with an update of the registry of available competencies. Finally, the newly acquired competencies could change the position of the organization to offer services or products, this way closing the “knowledge acquisition loop. ” The cycle depicted in Figure 1 can be expressed in terms of knowledge management (KM) activities and products. According to the recent Holsapple and Joshi (2004) ontology of KM, competences can be considered as capabilities attributable to processors of knowledge representations (KR), and the final learning activities can be considered as a specific type of knowledge manipulation activity (KMA), consisting on knowledge acquisition or eventually, transformation. Furthermore, processors are considered to Figure 1. The competency-guided organizational learning cycle have some capabilities, which are the focus of analysis in this chapter. This direct mapping of the essential concepts described in this chapter and H&J ontology of KM enables an effective integration of ontology-based KM and organizational e-learning, providing a concrete mean to the integration framework described by Sicilia and Garcia (2004). This will be the point of departure for the rest of the discussion provided in this chapter. In this chapter, an organizational view of learning processes enabled by Semantic Web technologies is provided, and the essential cornerstones for such semantic learning organization” are considered to be competencies and learning objects. The discussion focuses on competency management and its relationship to the description of learning concepts. Concretely, the second section provides an overview of existing work in ontologies and schemas for competence description. The third section deals with the use of ontological schemas to assess “knowledge gaps,” in terms of the “difference” between required and available competencies. Then, the connection of such knowledge gap with learning object metadata is described in the fourth section. Finally, some conclusions and a future outlook are provided in the fifth section. Existing Schemas and Ontologies for Competency Description Previous research and standardization activities have resulted in a number of data schemas aimed at describing competences. Among them, the competency format specified by the HrXML consortium (Allen, 2003) is of a special relevance for practical purposes, since it is the result of an industrial effort in the direction of interchanging data about competencies in a common format. Competencies in HrXML are defined through XML fragments like the following one, extracted from Allen (2003):

Forests, savannas, and grasslands: bridging the knowledge gap between ecology and Dynamic Global Vegetation Models

Abstract: . The forest, savanna, and grassland biomes, and the transitions between them, are expected to undergo major changes in the future due to global climate change. Dynamic global vegetation models (DGVMs) are very useful for understanding vegetation dynamics under the present climate, and for predicting its changes under future conditions. However, several DGVMs display high uncertainty in predicting vegetation in tropical areas. Here we perform a comparative analysis of three different DGVMs (JSBACH, LPJ-GUESS-SPITFIRE and aDGVM) with regard to their representation of the ecological mechanisms and feedbacks that determine the forest, savanna, and grassland biomes, in an attempt to bridge the knowledge gap between ecology and global modeling. The outcomes of the models, which include different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. By drawing on the large number of recent studies that have delivered new insights into the ecology of tropical ecosystems in general, and of savannas in particular, we identify two main mechanisms that need improved representation in the examined DGVMs. The first mechanism includes water limitation to tree growth, and tree–grass competition for water, which are key factors in determining savanna presence in arid and semi-arid areas. The second is a grass–fire feedback, which maintains both forest and savanna presence in mesic areas. Grasses constitute the majority of the fuel load, and at the same time benefit from the openness of the landscape after fires, since they recover faster than trees. Additionally, these two mechanisms are better represented when the models also include tree life stages (adults and seedlings), and distinguish between fire-prone and shade-tolerant forest trees, and fire-resistant and shade-intolerant savanna trees. Including these basic elements could improve the predictive ability of the DGVMs, not only under current climate conditions but also and especially under future scenarios.

Psychiatric Comorbidity at the Time of Diagnosis in Adults With ADHD: The CAT Study.

Abstract: Objective: The CAT (Comorbilidad en Adultos con TDAH) study aimed to quantify and characterize the psychiatric comorbidity at the time of diagnosis of ADHD in adult outpatients. Method: Cross-sectional, multicenter, observational register of adults with ADHD diagnosed for the first time. Results: In this large sample of adult ADHD (n = 367), psychiatric comorbidities were present in 66.2% of the sample, and were more prevalent in males and in the hyperactive-impulsive and combined subtypes. The most common comorbidities were substance use disorders (39.2%), anxiety disorders (23%), and mood disorders (18.1%). In all, 88.8% patients were prescribed pharmacological treatment for ADHD (in 93.4% of cases, modified release methylphenidate capsules 50:50). Conclusion: A high proportion of psychiatric comorbidity was observed when adult outpatients received a first-time diagnosis of ADHD. The systematic registering of patients and comorbidities in clinical practice may help to better understand and manage the pr...

An experiment for improving students performance in secondary and tertiary education by means of m-learning auto-assessment

Abstract: Mobile learning is considered an evolution of e-learning that embraces the ubiquitous nature of current computational systems in order to improve teaching and learning. Within this context it is possible to develop mobile applications oriented to learning, but it is also important to assess to what extent such applications actually work. In this paper we present a new tool designed to reinforce students' knowledge by means of self-assessment. Improvement in student achievement was evaluated and an attitudinal survey was also carried out to measure student attitudes towards this new tool. Three different experimental groups were selected for this research, with students aged from 14 to 21 years old, including high-school and university students. Results show that this kind of tool improves student achievement, especially amongst younger learners, with a relatively low impact on current teaching activities and methodology.