Meeting the standards in secondary science : a guide to the ITT NC.

Todos los estudiantes en período de formación docente deben demostrar ciertas aptitudes y conocimientos antes de alcanzar la categoría de profesor cualificado. Aquí se abordan las normas específicas que rigen estas competencias relacionadas con la enseñanza de las ciencias en la etapa de secundaria. A lo largo de sus capítulos se recogen temas como el aprendizaje y la progresión de los alumnos en la educación científica, el desarrollo de habilidades científicas fundamentales, la ciencia y las tecnologías de la información y la comunicación (TIC) y, por último, el desarrollo profesional de los docentes.

GCSE in applied science. Teacher's file.

Este recurso para el profesor se apoya en el libro del alumno que sirve para preparar el General Certificate Secondary Education (GCSE) en ciencias. Proporciona al docente: tareas para cumplir con los criterios de evaluación, actividades prácticas, fichas para el desarrollo de destrezas y plantillas. Se completa con un doble CD-ROM.

BTEC First in applied science. 1.

Este recurso ha sido escrito para ayudar a los alumnos a formar su cartera de pruebas para el nivel 2 del curso Edexcel BTEC en ciencia aplicada y también a cómo obtener una calificación de aprobado, notable o distinción. El programa de estudios BTEC ofrece una cualificación profesional o laboral. El contenido se divide en tres grandes unidades: química y nuestra Tierra; energía y nuestro universo; biología y nuestro entorno.

Estudio y Análisis del Curso Europeo de Informática ECS (European Computer Science).

Resumen tomado de la publicación

Molecular quantum similarity in QSAR: applications in computer-aided molecular design

La present tesi està centrada en l'ús de la Teoria de Semblança Quàntica per a calcular descriptors moleculars. Aquests descriptors s'utilitzen com a paràmetres estructurals per a derivar correlacions entre l'estructura i la funció o activitat experimental per a un conjunt de compostos. Els estudis de Relacions Quantitatives Estructura-Activitat són d'especial interès per al disseny racional de molècules assistit per ordinador i, en particular, per al disseny de fàrmacs. Aquesta memòria consta de quatre parts diferenciades. En els dos primers blocs es revisen els fonaments de la teoria de semblança quàntica, així com l'aproximació topològica basada en la teoria de grafs. Ambdues teories es fan servir per a calcular els descriptors moleculars. En el segon bloc, s'ha de remarcar la programació i implementació de programari per a calcular els anomenats índexs topològics de semblança quàntica. La tercera secció detalla les bases de les Relacions Quantitatives Estructura-Activitat i, finalment, el darrer apartat recull els resultats d'aplicació obtinguts per a diferents sistemes biològics.

View-based approaches to spatial representation in human vision

In an immersive virtual environment, observers fail to notice the expansion of a room around them and consequently make gross errors when comparing the size of objects. This result is difficult to explain if the visual system continuously generates a 3-D model of the scene based on known baseline information from interocular separation or proprioception as the observer walks. An alternative is that observers use view-based methods to guide their actions and to represent the spatial layout of the scene. In this case, they may have an expectation of the images they will receive but be insensitive to the rate at which images arrive as they walk. We describe the way in which the eye movement strategy of animals simplifies motion processing if their goal is to move towards a desired image and discuss dorsal and ventral stream processing of moving images in that context. Although many questions about view-based approaches to scene representation remain unanswered, the solutions are likely to be highly relevant to understanding biological 3-D vision.

Data assurance in opaque computations

The chess endgame is increasingly being seen through the lens of, and therefore effectively defined by, a data ‘model’ of itself. It is vital that such models are clearly faithful to the reality they purport to represent. This paper examines that issue and systems engineering responses to it, using the chess endgame as the exemplar scenario. A structured survey has been carried out of the intrinsic challenges and complexity of creating endgame data by reviewing the past pattern of errors during work in progress, surfacing in publications and occurring after the data was generated. Specific measures are proposed to counter observed classes of error-risk, including a preliminary survey of techniques for using state-of-the-art verification tools to generate EGTs that are correct by construction. The approach may be applied generically beyond the game domain.

Performance and prediction: Bayesian modelling of fallible choice in chess

Evaluating agents in decision-making applications requires assessing their skill and predicting their behaviour. Both are well developed in Poker-like situations, but less so in more complex game and model domains. This paper addresses both tasks by using Bayesian inference in a benchmark space of reference agents. The concepts are explained and demonstrated using the game of chess but the model applies generically to any domain with quantifiable options and fallible choice. Demonstration applications address questions frequently asked by the chess community regarding the stability of the rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The last include alleged under-performance, fabrication of tournament results, and clandestine use of computer advice during competition. Beyond the model world of games, the aim is to improve fallible human performance in complex, high-value tasks.

Strengthening argumentation in medical explanations by text planning revision

Explanations are an important by-product of medical decisionsupport activities, as they have proved to favour compliance and correct treatment performance. To achieve this purpose, these texts should have a strong argumentation content and should adapt to emotional, as well as to rational attitudes of the Addressee. This paper describes how Rhetorical Sentence Planning can contribute to this aim: the rulebased plan discourse revision is introduced between Text Planning and Linguistic Realization, and exploits knowledge about the user personality and emotions and about the potential impact of domain items on user compliance and memory recall. The proposed approach originates from analytical and empirical evaluation studies of computer generated explanation texts in the domain of drug prescription. This work was partially supported by a British-Italian Collaboration in Research and Higher Education Project, which involved the Universities of Reading and of Bari, in 1996.

A new linear initialization in SOM for biomolecular data

In the past decade, the amount of data in biological field has become larger and larger; Bio-techniques for analysis of biological data have been developed and new tools have been introduced. Several computational methods are based on unsupervised neural network algorithms that are widely used for multiple purposes including clustering and visualization, i.e. the Self Organizing Maps (SOM). Unfortunately, even though this method is unsupervised, the performances in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. In this paper we present a new initialization technique based on a totally connected undirected graph, that report relations among some intersting features of data input. Result of experimental tests, where the proposed algorithm is compared to the original initialization techniques, shows that our technique assures faster learning and better performance in terms of quantization error.

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.