The application of electron microscopy and microanalysis in conjunction with organic petrology to further the understanding of organic-mineral association: examples from Mount Isa and McArthur Basins, Australia

Organic petrology supported by electron microscopical and micro-analytical techniques was applied to organic matter in Proterozoic sediments to better understand hydrothermal processes responsible for ore-grade mineralisation. It was shown that organic maturation was not only closely linked to the geological history of the sediments, but also highlighted heat transfer by convection as differentiated from conduction solely through sediment burial and step-wise subsidence. Water-rock ratios effect organic maturation in hydrothermal systems, and erratic reflectance profiles are indicators of convective heat transfer. Identification and characterisation of organic materials in terms of source rock and migrated hydrocarbons was shown to be a powerful tool in reconstructing the thermal history of sediments, identifying hydrothermal episodes, fluid pathways and heat source in the northern Australian Proterozoic basins. Higher reflectance of organic matter towards the central parts of the Mount Isa Basin and some of the most northerly parts point to proximity to higher heat flow at times, in contrast to relatively low temperatures prevailing in the western parts of the basin, next to the Murphy Inlier. A close correlation shown between peak organic reflectance values and super-sequence boundaries farther highlighted the valuable information to be gained from organic petrology, by allowing the separation of processes responsible for metal dissolution and transportation from those inducing precipitation. (C) 2001 Elsevier Science B.V All rights reserved.

Breeding for low input conditions and consequences for participatory plant breeding: Examples from tropical maize and wheat

Participatory plant breeding (PPB) has been suggested as an effective alternative to formal plant breeding (FPB) as a breeding strategy for achieving productivity gains under low input conditions. With genetic progress through PPB and FPB being determined by the same genetic variables, the likelihood of success of PPB approaches applied in low input target conditions was analyzed using two case studies from FPB that have resulted in significant productivity gains under low input conditions: (1) breeding tropical maize for low input conditions by CIMMYT, and (2) breeding of spring wheat for the highly variable low input rainfed farming systems in Australia. In both cases, genetic improvement was an outcome of long-term investment in a sustained research effort aimed at understanding the detail of the important environmental constraints to productivity and the plant requirements for improved adaptation to the identified constraints, followed up by the design and continued evaluation of efficient breeding strategies. The breeding strategies used differed between the two case studies but were consistent in their attention to the key determinants of response to selection: (1) ensuring adequate sources of genetic variation and high selection pressures for the important traits at all stages of the breeding program, (2) use of experimental procedures to achieve high levels of heritability in the breeding trials, and (3) testing strategies that achieved a high genetic correlation between performance of germplasm in the breeding trials and under on-farm conditions. The implications of the outcomes from these FPB case studies for realizing the positive motivations for adopting PPB strategies are discussed with particular reference for low input target environment conditions.

Engaging with theories of dialogue and voice: Using Bakhtin as a framework to understand teaching and learning Indigenous Australian women's performance

In this paper I explore the Indigenous Australian women's performance classroom (hereafter ANTH2120) as a dialectic and discursive space where the location of possibility is opened for female Indigenous performers to enter into a dialogue from and between both non-Indigenous and Indigenous voices. The work of Bakhtin on dialogue serves as a useful standpoint for understanding the multiple speaking positions and texts in the ANTH2120 context. Bakhtin emphasizes performance, history, actuality and the openness of dialogue to provide an important framework for analysing multiple speaking positions and ways of making meaning through dialogue between shifting and differing subjectivities. I begin by briefly critiquing Bakhtin's "dialogic imagination" and consider the application and usefulness of concepts such as dialogism, heteroglossia and the utterance to understanding the ANTH2120 classroom as a polyphonic and discursive space. I then turn to an analysis of dialogue in the ANTH2120 classroom and primarily situate my gaze on an examination of the interactions that took place between the voices of myself as family/teacher/student and senior Yanyuwa women from the r e m o t e N o r t h e r n T e r r i t o r y A b o r i g i n a l c o m m u n i t y o f B o r r o l o o l a as family/performers/teachers. The 2000 and 2001 Yanyuwa women's performance workshops will be used as examples of the way power is constantly shifting in this dialogue to allow particular voices to speak with authority, and for others to remain silent as roles and relationships between myself and the Yanyuwa women change. Conclusions will be drawn regarding how my subject positions and white race privilege affect who speaks, who listens and on whose terms, and further, the efficacy of this pedagogical platform for opening up the location of possibility for Indigenous Australian women to play a powerful part in the construction of knowledges about women's performance traditions.

Teaching and learning production and operations management: the journey from identity crisis to a cross, disciplinary approach

Este artigo tem suas raízes em algumas questões relacionadas à "forma" e ao "conteúdo" do que nós, professores, ensinamos na área de Administração da Produção e Operações. Inicialmente, descrevo a evolução histórica desse campo no Brasil. Em seguida, discuto a crise de identidade que o campo está sofrendo. Com o objetivo de apresentar respostas para essa situação, apresento seis propostas para o desenvolvimento e consolidação do campo. Finalmente, descrevo uma iniciativa prática, envolvendo uma disciplina específica da área, ensinada para alunos de pós-graduação. Essa iniciativa enfatiza a "dimensão do conteúdo" (de uma abordagem técnico-operacional para uma abordagem estratégico-gerencial) como também a "dimensão da forma" (do foco no ensino para o foco no aprendizado). O sucesso dessa experiência em curso confirma a coerência da agenda proposta e induz futuros aperfeiçoamentos.

Learning in higher education: strategies to overcome challenges faced by adult students - lessons drawn from two studies in portugal

Int’l J. of Information and Communication Technology Education, 3(2), 1-14, April-June 2007

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

From e-learning systems to specialised e-learning services

Learning systems are evolving from component based and centralized architectures towards service oriented and decentralized architectures. The standardization of e-learning content and interoperability is a powerful force in this evolution. In this chapter we put in perspective the evolution of e-learning systems and standards, and argue that specialized services will play an important role in future learning systems, especially in those targeted for competitive learning.

Curso E-learning de sistemas multicorpo (multibody systems) para engenheiros mecânicos

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Learning in higher education: strategies to overcome challenges faced by adult students – lessons drawn from two case studies in Portugal

This paper appears in International Journal of Information and Communication Technology Education edited by Lawrence A. Tomei (Ed.) Copyright 2007, IGI Global, www.igi-global.com. Posted by permission of the publisher. URL:http://www.idea-group.com/journals/details.asp?id=4287.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Inheritance of ductile and brittle structures in the development of large rock slope instabilities: Examples from western Norway

The high density of slope failures in western Norway is due to the steep relief and to the concentration of various structures that followed protracted ductile and brittle tectonics. On the 72 investigated rock slope instabilities, 13 were developed in soft weathered mafic and phyllitic allochthons. Only the intrinsic weakness of such rocks increases the susceptibility to gravitational deformation. In contrast, the gravitational structures in the hard gneisses reactivate prominent ductile or/and brittle fabrics. At 30 rockslides along cataclinal slopes, weak mafic layers of foliation are reactivated as basal planes. Slope-parallel steep foliation forms back-cracks of unstable columns. Folds are specifically present in the Storfjord area, together with a clustering of potential slope failures. Folding increases the probability of having favourably orientated planes with respect to the gravitational forces and the slope. High water pressure is believed to seasonally build up along the shallow-dipping Caledonian detachments and may contribute to destabilization of the rock slope upwards. Regional cataclastic faults localized the gravitational structures at 45 sites. The volume of the slope instabilities tends to increase with the amount of reactivated prominent structures and the spacing of the latter controls the size of instabilities.