871 resultados para Optimal design of experiments


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Although modern control techniques such as eigenstructure assignment have been given extensive coverage in control literature there is a reluctance to use them in practice as they are often not believed to be as `visible' or as simple as classical methods. A simple aircraft example is used, and it is shown that eigenstructure assignment can be used easily to produce a more viable controller than with simple classical techniques.

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Limnologists had an early preoccupation with lake classification. It gave a necessary structure to the many chemical and biological observations that were beginning to form the basis of one of the earliest truly environmental sciences. August Thienemann was the doyen of such classifiers and his concept with Einar Naumann of oligotrophic and eutrophic lakes remains central to the world-view that limnologists still have. Classification fell into disrepute, however, as it became clear that there would always be lakes that deviated from the prescriptions that the classifiers made for them. Continua became the de rigeur concept and lakes were seen as varying along many chemical, biological and geographic axes. Modern limnologists are comfortable with this concept. That all lakes are different guarantees an indefinite future for limnological research. For those who manage lakes and the landscapes in which they are set, however, it is not very useful. There may be as many as 300000 standing water bodies in England and Wales alone and maybe as many again in Scotland. More than 80 000 are sizable (> 1 ha). Some classification scheme to cope with these numbers is needed and, as human impacts on them increase, a system of assessing and monitoring change must be built into such a scheme. Although ways of classifying and monitoring running waters are well developed in the UK, the same is not true of standing waters. Sufficient understanding of what determines the nature and functioning of lakes exists to create a system which has intellectual credibility as well as practical usefulness. This paper outlines the thinking behind a system which will be workable on a north European basis and presents some early results.

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Flavonoids are low-molecular weight, aromatic compounds derived from fruits, vegetables, and other plant components. The consumption of these phytochemicals has been reported to be associated with reduced cardiovascular disease (CVD) risk, attributed to their anti-inflammatory, anti-proliferative, and anti-thrombotic actions. Flavonoids exert these effects by a number of mechanisms which include attenuation of kinase activity mediated at the cell-receptor level and/or within cells, and are characterized as broad-spectrum kinase inhibitors. Therefore, flavonoid therapy for CVD is potentially complex; the use of these compounds as molecular templates for the design of selective and potent small-molecule inhibitors may be a simpler approach to treat this condition. Flavonoids as templates for drug design are, however, poorly exploited despite the development of analogues based on the flavonol, isoflavonone, and isoflavanone subgroups. Further exploitation of this family of compounds is warranted due to a structural diversity that presents great scope for creating novel kinase inhibitors. The use of computational methodologies to define the flavonoid pharmacophore together with biological investigations of their effects on kinase activity, in appropriate cellular systems, is the current approach to characterize key structural features that will inform drug design. This focussed review highlights the potential of flavonoids to guide the design of clinically safer, more selective, and potent small-molecule inhibitors of cell signalling, applicable to anti-platelet therapy.

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Purpose: This paper aims to design an evaluation method that enables an organization to assess its current IT landscape and provide readiness assessment prior to Software as a Service (SaaS) adoption. Design/methodology/approach: The research employs a mixed of quantitative and qualitative approaches for conducting an IT application assessment. Quantitative data such as end user’s feedback on the IT applications contribute to the technical impact on efficiency and productivity. Qualitative data such as business domain, business services and IT application cost drivers are used to determine the business value of the IT applications in an organization. Findings: The assessment of IT applications leads to decisions on suitability of each IT application that can be migrated to cloud environment. Research limitations/implications: The evaluation of how a particular IT application impacts on a business service is done based on the logical interpretation. Data mining method is suggested in order to derive the patterns of the IT application capabilities. Practical implications: This method has been applied in a local council in UK. This helps the council to decide the future status of the IT applications for cost saving purpose.

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Current methods and techniques used in designing organisational performance measurement systems do not consider the multiple aspects of business processes or the semantics of data generated during the lifecycle of a product. In this paper, we propose an organisational performance measurement systems design model that is based on the semantics of an organisation, business process and products lifecycle. Organisational performance measurement is examined from academic and practice disciplines. The multi-discipline approach is used as a research tool to explore the weaknesses of current models that are used to design organisational performance measurement systems. This helped in identifying the gaps in research and practice concerning the issues and challenges in designing information systems for measuring the performance of an organisation. The knowledge sources investigated include on-going and completed research project reports; scientific and management literature; and practitioners’ magazines.

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The concepts of on-line transactional processing (OLTP) and on-line analytical processing (OLAP) are often confused with the technologies or models that are used to design transactional and analytics based information systems. This in some way has contributed to existence of gaps between the semantics in information captured during transactional processing and information stored for analytical use. In this paper, we propose the use of a unified semantics design model, as a solution to help bridge the semantic gaps between data captured by OLTP systems and the information provided by OLAP systems. The central focus of this design approach is on enabling business intelligence using not just data, but data with context.

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Using a cross-layer approach, two enhancement techniques applied for adaptive modulation and coding (AMC) with truncated automatic repeat request (T-ARQ) are investigated, namely, aggressive AMC (A-AMC) and constellation rearrangement (CoRe). Aggressive AMC selects the appropriate modulation and coding schemes (MCS) to achieve higher spectral efficiency, profiting from the feasibility of using different MCSs for retransmitting a packet, whereas in the CoRe-based AMC, retransmissions of the same data packet are performed using different mappings so as to provide different degrees of protection to the bits involved, thus achieving mapping diversity gain. The performance of both schemes is evaluated in terms of average spectral efficiency and average packet loss rate, which are derived in closed-form considering transmission over Nakagami-m fading channels. Numerical results and comparisons are provided. In particular, it is shown that A-AMC combined with T-ARQ yields higher spectral efficiency than the AMC-based conventional scheme while keeping the achieved packet loss rate closer to the system's requirement, and that it can achieve larger spectral efficiency objectives than that of the scheme using AMC along with CoRe.

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Optimal estimation (OE) improves sea surface temperature (SST) estimated from satellite infrared imagery in the “split-window”, in comparison to SST retrieved using the usual multi-channel (MCSST) or non-linear (NLSST) estimators. This is demonstrated using three months of observations of the Advanced Very High Resolution Radiometer (AVHRR) on the first Meteorological Operational satellite (Metop-A), matched in time and space to drifter SSTs collected on the global telecommunications system. There are 32,175 matches. The prior for the OE is forecast atmospheric fields from the Météo-France global numerical weather prediction system (ARPEGE), the forward model is RTTOV8.7, and a reduced state vector comprising SST and total column water vapour (TCWV) is used. Operational NLSST coefficients give mean and standard deviation (SD) of the difference between satellite and drifter SSTs of 0.00 and 0.72 K. The “best possible” NLSST and MCSST coefficients, empirically regressed on the data themselves, give zero mean difference and SDs of 0.66 K and 0.73 K respectively. Significant contributions to the global SD arise from regional systematic errors (biases) of several tenths of kelvin in the NLSST. With no bias corrections to either prior fields or forward model, the SSTs retrieved by OE minus drifter SSTs have mean and SD of − 0.16 and 0.49 K respectively. The reduction in SD below the “best possible” regression results shows that OE deals with structural limitations of the NLSST and MCSST algorithms. Using simple empirical bias corrections to improve the OE, retrieved minus drifter SSTs are obtained with mean and SD of − 0.06 and 0.44 K respectively. Regional biases are greatly reduced, such that the absolute bias is less than 0.1 K in 61% of 10°-latitude by 30°-longitude cells. OE also allows a statistic of the agreement between modelled and measured brightness temperatures to be calculated. We show that this measure is more efficient than the current system of confidence levels at identifying reliable retrievals, and that the best 75% of satellite SSTs by this measure have negligible bias and retrieval error of order 0.25 K.

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Data from three cocoa (Theobroma cacao) clonal selection trials are used to investigate the genetic and environmental components of variation in yield and the percentage of total pods affected by black pod disease (Phytophtora pod rot). Simulations based on these estimated components of variation are then used to discuss the best choice in future of numbers of clones, replicates and years of harvest to maximise selection advances in the traits measured. The three main conclusions are the need to increase the number of clones at the expense of the number of replicates of each clone, the diminishing returns from additional years of harvesting and the importance of widening the genetic base of the clones chosen to be tested.