Inovação em recursos naturais: acumulação de capacidade em uma empresa de mineração no Brasil (1994-2008)

Ao longo dos últimos 40 anos tem havido uma profusão de estudos sobre acumulação de capacidades tecnológicas em empresas de economias emergentes. Porém, são escassos os estudos que examinem, de maneira conjunta e de uma perspectiva dinâmica, o relacionamento entre trajetórias de acumulação de capacidades tecnológicas e os mecanismos subjacentes de aprendizagem. São ainda mais escassos estudos sobre este relacionamento em firmas atuando na indústria de processamento de recursos naturais. O interesse neste último está em oferecer uma visão alternativa de alguns autores quando se referem a estas indústrias como 'maduras', de 'baixa tecnologia' ou meramente produtoras de 'commodities' e 'no fim da linha de inovação'. Logo, neste estudo, defende-se que as inovações tecnológicas estão bem presentes em empresas baseadas em processamento de recursos naturais, principalmente em empresas de mineração. Buscando preencher essas lacunas da literatura, examinam-se, nesta dissertação, essas questões à luz de modelos analíticos disponíveis na literatura internacional -, adaptados para o contexto desta dissertação. O modelo para examinar a acumulação de capacidades tecnológicas identifica as capacidades para as funções tecnológicas de processos e organização da produção. Para a análise das fontes de capacidades tecnológicas, utiliza-se, nesta dissertação, o modelo para examinar as estratégias intraorganizacionais que desmembram o processo de aprendizagem em aquisição de conhecimento externo e interno e os convertem do nível individual para o organizacional pela socialização e codificação, com base em suas característicaschave: variedade, intensidade e funcionamento. Esse conjunto de relacionamentos é examinado por meio de estudo de caso simples e de longo prazo (1994-2008) em uma empresa de processamento de recursos naturais (mineração de cobre) no Brasil. Tomando-se por base evidências empíricas qualitativas e quantitativas, colhidas em primeira mão, verificou-se o seguinte. 1. A empresa acumulou capacidade inovadora em processos e organização da produção em Nível Inovador Intermediário, ou seja, a empresa já promove a expansão sistemática da capacidade por meio da manipulação de parâmetros-chave de processo. Verificou-se também que a firma tem potencial para atingir o Nível Inovador Avançado em virtude dos avanços obtidos em seu projeto de biolixiviação de cobre sulfetedo. Este nível não foi atingido porque, ao final da pesquisa, a aplicação comercial bem-sucedida deste projeto ainda não tinha sido comprovada. 2. Os vários processos e mecanismos de aprendizagem tiveram um papel crucial na acumulação desse nível de capacidade inovadora. Especificamente a progressiva incidência e a maneira como os mecanismos de aprendizagem foram criados e geridos na empresa contribuíram decisivamente para criar uma base de conhecimento que pennitiu à empresa desenvolver capacidades tanto para atividades de produção como para atividades de inovação. Não obstante, as evidências também sugerem que estes mesmos tipos de mecanismos não foram suficientes para que a empresa acumulasse capacidades além do nível alcançado. Ou seja, o alcance de níveis mais sofisticados de inovação implica a adoção de mecanismos mais complexos de aprendizagem. Naturalmente, outros fatores, como o comportamento da liderança empresarial, também contribuíram para o acúmulo dessas capacidades, embora este ponto tenha sido examinado aqui de maneira superficial. Esses resultados fazem avançar nosso entendimento sobre as dificuldades e complexidades envolvidas no processo de acumulação de capacidades inovadoras em empresas de economias emergentes. O estudo contribui para mostrar que, se empresas dessa natureza objetivarem acumular níveis inovadores de capacidade tecnológica e, com isso, obter melhor performance competitiva, terão que desenhar estratégias robustas de aprendizagem. Finalmente, o estudo joga luz no entendimento sobre o processo de inovação em empresas em indústrias à base de processamento de recursos naturais, setores estes de grande importância para países ricos em recursos naturais como o Brasil.

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

HOW FAR do WE GET USING MACHINE LEARNING BLACK-BOXES?

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Estruturação do conhecimento e relações semânticas: uma ontologia para o domínio da naonociência e nanotecnologia

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Reconhecimento de padrões lexicais por meio de redes neurais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

As ambiguidades linguísticas no inglês e a tradução automática inglês-português: um estudo exploratório

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Delimitação e alinhamento de conceitos lexicalizados no inglês norte-americano e no português brasileiro

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Enfrentamento do problema das divergências de tradução por um sistema de tradução automática: um exercício exploratório

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Can neuroimaging be used as a support to diagnosis of borderline personality disorder? An approach based on computational neuroanatomy and machine learning

Several recent studies in literature have identified brain morphological alterations associated to Borderline Personality Disorder (BPD) patients. These findings are reported by studies based on voxel-based-morphometry analysis of structural MRI data, comparing mean gray-matter concentration between groups of BPD patients and healthy controls. On the other hand, mean differences between groups are not informative about the discriminative value of neuroimaging data to predict the group of individual subjects. In this paper, we go beyond mean differences analyses, and explore to what extent individual BPD patients can be differentiated from controls (25 subjects in each group), using a combination of automated-morphometric tools for regional cortical thickness/volumetric estimation and Support Vector Machine classifier. The approach included a feature selection step in order to identify the regions containing most discriminative information. The accuracy of this classifier was evaluated using the leave-one-subject-out procedure. The brain regions indicated as containing relevant information to discriminate groups were the orbitofrontal, rostral anterior cingulate, posterior cingulate, middle temporal cortices, among others. These areas, which are distinctively involved in emotional and affect regulation of BPD patients, were the most informative regions to achieve both sensitivity and specificity values of 80% in SVM classification. The findings suggest that this new methodology can add clinical and potential diagnostic value to neuroimaging of psychiatric disorders. (C) 2012 Elsevier Ltd. All rights reserved.

Preimplantation development regulatory pathway construction through a text-mining approach

Background: The integration of sequencing and gene interaction data and subsequent generation of pathways and networks contained in databases such as KEGG Pathway is essential for the comprehension of complex biological processes. We noticed the absence of a chart or pathway describing the well-studied preimplantation development stages; furthermore, not all genes involved in the process have entries in KEGG Orthology, important information for knowledge application with relation to other organisms. Results: In this work we sought to develop the regulatory pathway for the preimplantation development stage using text-mining tools such as Medline Ranker and PESCADOR to reveal biointeractions among the genes involved in this process. The genes present in the resulting pathway were also used as seeds for software developed by our group called SeedServer to create clusters of homologous genes. These homologues allowed the determination of the last common ancestor for each gene and revealed that the preimplantation development pathway consists of a conserved ancient core of genes with the addition of modern elements. Conclusions: The generation of regulatory pathways through text-mining tools allows the integration of data generated by several studies for a more complete visualization of complex biological processes. Using the genes in this pathway as “seeds” for the generation of clusters of homologues, the pathway can be visualized for other organisms. The clustering of homologous genes together with determination of the ancestry leads to a better understanding of the evolution of such process.