Linalol, principal componente químico dos óleos essenciais da folha da sacaca (Croton cajucara Benth) e da madeira do pau-rosa (Aniba duckei Kostermans).

Verificação, de forma exploratória, dos efeitos de nutrientes químicos e orgânicos no rendimento em óleo e no teor de linalol de sacaca cultivada na área experimental da Embrapa Amazônia Ocidental (Manaus-AM).

A view of the history, literature, and religion of the Hindoos : including a minute description of their manners and customs, and translations from their principal works / by Rev. W. Ward.

http://www.archive.org/details/wardshidoos00sethuoft

India and India missions : including sketches of the gigantic system of hinduism, both in theory and practive : also notices of some of the principal agencies employed in conducting the process of Indian evangalization, etc. etc.

http://books.google.com/books?vid=OCLC05766993

Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Introduction of a Nonlinearity Measure for Principal Component Models

This is the first paper that introduces a nonlinearity test for principal component models. The methodology involves the division of the data space into disjunct regions that are analysed using principal component analysis using the cross-validation principle. Several toy examples have been successfully analysed and the nonlinearity test has subsequently been applied to data from an internal combustion engine.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

An exploratory study of Principal Investigator roles in UK University Proof-of-Concept processes: an Absorptive capacity perspective

The increasing emphasis on academic entrepreneurship, technology transfer and research commercialisation within UK universities is predicated on basic research being developed by academics into commercial entities such as university spin-off companies or licensing arrangements. However, this process is fraught with challenges and risks, given the degree of uncertainty regarding future returns. In an attempt to minimise such risks, the Proof-of-Concept (PoC) process has been developed within University Science Park Incubators (USIs) to test the technological, business and market potential of embryonic technology. The key or the pivotal stakeholder within the PoC is the Principal Investigator (PI), who is usually the lead academic responsible for the embryonic technology. Within the current literature, there appears to be a lack of research pertaining to the role of the PI in the PoC process. Moreover, Absorptive Capacity (ACAP) has emerged within the literature as a theoretical framework or lens for exploring the development and application of new knowledge and technology, where the USI is the organisation considered in the current study. Therefore, the aim of this paper is to explore the role and influence of the PI in the PoC process within a USI setting using an ACAP perspective. The research involved a multiple case analysis of PoC applications within a UK university USI. The results demonstrate the role of the PI in developing practices and routines within the PoC process. These practices and processes were initially tacit and informal in nature but became more explicit and formal over time so that knowledge was retained within the USI after the PIs had completed the PoC process. © 2010 The Authors. R&D Management © 2010 Blackwell Publishing Ltd.