306,573 research outputs found
Development of a framework for obsolescence resolution cost estimation
No full textCurrently, manufacturing organisations worldwide are shifting their business
models towards Product-Service Systems (PSS), which implies the
development of new support agreements such as availability-based contracts.
This transition is shifting the responsibilities for managing and resolving
obsolescence issues from the customer to the prime contractor and industry
work share partners. This new scenario has triggered a new need to estimate
the Non-Recurring Engineering (NRE) cost of resolving obsolescence issues at
the bidding stage, so it can be included in the support contract. Hence, the aim
of this research is to develop an understanding about all types of obsolescence
and develop methodologies for the estimation of NRE costs of hardware
(electronic, electrical and electromechanical (EEE) components and materials)
obsolescence that can be used at the bidding stage for support contracts in the
defence and aerospace sectors.
For the accomplishment of this aim, an extensive literature review of the related
themes to the research area was carried out. It was found that there is a lack of
methodologies for the cost estimation of obsolescence, and also a lack of
understanding on the different types of obsolescence such as materials and
software obsolescence. A systematic industrial investigation corroborated these
findings and revealed the current practice in the UK defence sector for cost
estimation at the bidding stage, obsolescence management and obsolescence
cost estimation. It facilitated the development of an understanding about
obsolescence in hardware and software. Further collaboration with experts from
more than 14 organisations enabled the iterative development of the EEEFORCE
and M-FORCE frameworks, which can be used at the bidding stage of
support contracts to estimate the NRE costs incurred during the contracted
period in resolving obsolescence issues in EEE components and materials,
respectively. These frameworks were implemented within a prototype software
platform that was applied to 13 case studies for expert validation
Methods in Chemical and Mineral Microscopy par Essam E. El-Hinnawi, 1966
No full textGabis Victor. Methods in Chemical and Mineral Microscopy par Essam E. El-Hinnawi, 1966. In: Bulletin de la Société française de Minéralogie et de Cristallographie, volume 90, 1, 1967. pp. 125-126
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Determination of nitrofurantoin drug in pharmaceutical formulation and biological fluids by square-wave cathodic adsorptive stripping voltammetry
No full textNitrofurnation is an antibacterial drug. It is used in the treatment of initial or recurrent urinary tract infections caused by susceptible organisms. The cyclic voltammogram of the drug in Britton-Robinson buffers (pH 2-11) exhibited a single well-defined cathodic peak at the hanging mercury drop electrode, that due to the reduction of its nitro group to the amine stage. A fully validated, sensitive, and reproducible developed procedure was described for determination of the drug in bulk form, pharmaceutical formulation, human serum and human urine using, square-wave cathodic adsorptive stripping voltammetry. The optimal experimental parameters for the drug assay were: accumulation POTENTIAL=?0.4 V (vs. Ag/AgCl/ KCls), accumulation TIME=40 s, FREQUENCY=120 Hz, pulse AMPLITUDE=50 mV and scan INCREMENT=10 mV in Britton–Robinson buffer (pH 10). A mean percentage recovery of 100.68 +/- 0.17 (n = 5) and a detection limit of 1.32 x 10(-10) M of bulk drug were achieved. Applicability to assay of the drug in pharmaceutical formulation, human serum and human urine was studied and illustrated. The mean percentage recoveries were found as: 101.49 +/- 0.65, 103.94 +/- 0.73 and 101.98 +/- 0.52 (n = 5) in pharmaceutical formulation, human serum and human urine, respectively. Detection limits of 2.86 x 10(-10) M and 5.77 x 10(-10) M nitrofurantoin were achieved in human serum and urine, respectively
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Clinical application of maximum entropy image processing in planar radionuclide imaging
No full textThe aim of the research was to develop the technique of maximum entropy for clinical application and to evaluate its effectiveness in improving image quality.Maximum entropy (ME) requires definition of various parameters for its operation. Studies were carried out to investigate the way in which the values of these parameters affected image quality. This allows methods for deriving optimal values in any situation to be devised.A study was carried out to investigate the way in which the figure of merit (FOM) method of image quality assessment depended on definition of regions defining the object and surroundings. This allowed description of a method for defining the regions which provided robust values for FOM.A comparative evaluation of maximum entropy processing with simple image smoothing (i.e. conventional smoothing (SM)) and Wiener filtering (WF) was carried out in simulated images of a planar object. Image quality was evaluated using the FOM and using receiver operating characteristic (ROC) analysis with two different observers. The FOM analysis showed that all image processing technique produced significant improvement over the raw data and that ME was the best of the methods. These findings were generally supported by the ROC analysis although, the conclusions were not so clearly defined. There was significant correlation between FOM and detectability for individual observers interpreting images from a single processing technique. Correlation was poorer when data from all the methods were combined.A further comparative evaluation of the processing techniques in simulated lung images was performed using ROC analysis. The analysis failed to show significant improvements in detectability using conventional smoothing or Wiener filtering.</p
Outlier detection and subspace learning via structured low rank approximation with applications to omic data
No full textDimensionality reduction is crucial when dealing with data with very high dimensionality and low number of samples. This is the case with genomic data where sequencing many genes is much easier than gathering many different samples. The main problem with high-dimensional data is that statistical inference and traditional pattern recognition techniques would break down or give misleading results. Therefore, we need to reduce the dimensionality of the data before extracting any useful information from it. A widely used dimensionality reduction technique is Principal Component Analysis (PCA). However, it is known from the literature that this method breaks down in the presence of even a small number of outliers in the data. We have reason to believe that outliers are present in genomic data due to shortcomings from the used experimental equipments, sensor malfunctions, and mistakes in the sample gathering processes. Moreover, outliers could be samples that are of interest in the problem that is being investigated, and need to be retained for further investigation.In this work we will investigate low rank approximation methods that are robust to outliers, much of which have been already introduced in the machine learning community, and they are formulated as convex optimization problems. The main advantage of the convexity of this problems, is that it can be solved iteratively in an efficient way using first order optimization algorithms. However, outlier robust low rank approximation models, such as Outlier Pursuit (OP), that is optimal for high-dimensional genomic datasets, assume that the data lies approximately along a low-dimensional linear subspace; which is a strong assumption when dealing with gene expression or any biological dataset. Inspired by previous work in the computer vision community, we exploit the usefulness of adding a graph regularization term to OP, by building a graph between the data points to model the local geometry structure of the input data. This algorithm is called Graph regularized Outlier Pursuit (GOP), and it has the beneficial advantage of being a convex optimization problem. We will show the effectiveness in outlier detection and low-dimensional visualization of both techniques on high-dimensional genomic datasets. Furthermore, we show here that GOP and OP give better outlier detection results than traditional density based methods used for anomaly detection. Moreover, we will show the enhanced visualization capability of GOP when compared to OP, PCA, and t-distributed Stochastic Neighbour embedding (t-SNE).Stemming from GOP, this work also proposes as novel method for multi-view clustering based on subspace learning, dubbed Convex Graph regularized Robust Multi-view Subspace Learning (CGRMSL). CGRMSL is robust to outliers and incorporates the non-linearities present in the different views. Moreover, the proposed multi-view method is also based on a convex objective function which guarantees a global optimal solution. We will investigate the power of this novel method on cancer multi-omic datasets for applications such as: cancer subtype clustering and cancer subtype discovery
Author, publisher and bookseller : a tripartite synergy in Nigerian book industry
No full textThis work is about the roles of Author, Publisher and Bookseller in Book development in
Nigeria. The paper started by delving into the history of Book Publishing in Nigeria after
which it proceeded by defining who an author, a publisher, and a bookseller is and
expatiated on the indispensable roles of these key actors in Nigerian Book Industry and in
the emerging Information Society. Furthermore, the various constraints to book
development were identified while the paper advised on how the Book Industry can be
further promoted in Nigeria. However, the paper concluded and made recommendations
on how the Book sector can help in enhancing scholarship in the country
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