Small Set Of Components Research Articles

Primary and secondary pre-service teachers’ attitudes towards inclusive education

The practice of inclusive education in schools has led to changes in policy and pedagogy, hence teacher acceptance and attitude are important components of its success. The aim of this study is to identify the differences in attitudes of primary and secondary pre-service teachers on inclusion and the potential relationship between demographic variables such as definitions of inclusion, previous experience working in a school, completion of a module on inclusive schools, and other variables. The study included 548 Australian university students studying primary (n = 348) or secondary (n = 193) professional teaching. All participants completed the Teacher Attitudes to Inclusion Scale (TAISA). Principle components analysis was performed to transform the TAISA questionnaire into smaller set of components and two-way between-groups analysis of variance was used to analyse data. Results showed that primary pre-service teachers have more positive attitudes towards inclusion than secondary pre-service teachers. Primary pre-service teachers were also more responsive to training on inclusive education. Implications for practice and future research are discussed.

A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression

This article introduces a probabilistic machine learning framework for the uncertainty quantification (UQ) of electronic circuits based on the Gaussian process regression (GPR). As opposed to classical surrogate modeling techniques, GPR inherently provides information on the model uncertainty. The main contribution of this work is twofold. First, it describes how, in a UQ scenario, the model uncertainty can be combined with the uncertainty of the input design parameters to provide confidence bounds for the statistical estimates of the system outputs, such as moments and probability distributions. These confidence bounds allow assessing the accuracy of the predicted statistics. Second, in order to deal with dynamic multioutput systems, principal component analysis (PCA) is effectively employed to compress the time-dependent output variables into a smaller set of components, for which the training of individual GPR models becomes feasible. The uncertainty on the principal components is then propagated back to the original output variables. Several application examples, ranging from a trivial RLC circuit to real-life designs, are used to illustrate and validate the advocated approach.

Modeling of the fat base of cosmetic creams

The development of the formulation of cosmetic creams with a fat base, as a rule, is based on a rich practical experience, which has found expression in a number of generally accepted strategies. At the same time, the most difficult stage is the creation of a multicomponent mixture of oils with a balanced fatty acid composition, taking into account skin type, age-related changes, etc. An alternative to the empirical approach can be the creation of mathematical models of fat mixtures based on an unambiguous clear choice of criteria and restrictive conditions for the content of individual fatty acids. The relevance of such a task is due to the fact that the proposed models are quite complex and limited to a small set of components in the blend. The purpose of this study was to create a simplified model for calculating the fatty acid composition of multicomponent blends of vegetable oils using oil extracts of berries, fruits, essential oils and medicinal plants of the Crimean region enriched with BAS as a fat base. Oil extracts were obtained on the principle of two-phase extraction, which allows the extraction of biologically active substances of medium polarity. As a result of the research, a simplified strategy for mathematical modeling of the multicomponent composition of the fat base of oil creams is proposed, which allows to obtain the optimal ratio between base and active oils. At the same time, oils are selected both taking into account their physiological activity and fatty acid composition. A significant simplification is achieved through a preliminary analysis of the fatty acid composition of oils and the selection on its basis of such mixtures of oils, which later act as a single component. This allows the formation of multicomponent mixtures. The ratio of oleic and linoleic acids; the ratio of oleic to the sum of polyunsaturated acids (PUFA) were chosen as restrictive criteria. The results of this work can be useful when creating and testing mixtures using new unconventional oils.

MAGICPL: A Generic Process Description Language for Distributed Pseudonymization Scenarios.

Pseudonymization is an important aspect of projects dealing with sensitive patient data. Most projects build their own specialized, hard-coded, solutions. However, these overlap in many aspects of their functionality. As any re-implementation binds resources, we would like to propose a solution that facilitates and encourages the reuse of existing components. We analyzed already-established data protection concepts to gain an insight into their common features and the ways in which their components were linked together. We found that we could represent these pseudonymization processes with a simple descriptive language, which we have called MAGICPL, plus a relatively small set of components. We designed MAGICPL as an XML-based language, to make it human-readable and accessible to nonprogrammers. Additionally, a prototype implementation of the components was written in Java. MAGICPL makes it possible to reference the components using their class names, making it easy to extend or exchange the component set. Furthermore, there is a simple HTTP application programming interface (API) that runs the tasks and allows other systems to communicate with the pseudonymization process. MAGICPL has been used in at least three projects, including the re-implementation of the pseudonymization process of the German Cancer Consortium, clinical data flows in a large-scale translational research network (National Network Genomic Medicine), and for our own institute's pseudonymization service. Putting our solution into productive use at both our own institute and at our partner sites facilitated a reduction in the time and effort required to build pseudonymization pipelines in medical research.

Diversification of the type IV filament superfamily into machines for adhesion, protein secretion, DNA uptake, and motility.

Processes of molecular innovation require tinkering and shifting in the function of existing genes. How this occurs in terms of molecular evolution at long evolutionary scales remains poorly understood. Here, we analyse the natural history of a vast group of membrane-associated molecular systems in Bacteria and Archaea—the type IV filament (TFF) superfamily—that diversified in systems involved in flagellar or twitching motility, adhesion, protein secretion, and DNA uptake. The phylogeny of the thousands of detected systems suggests they may have been present in the last universal common ancestor. From there, two lineages—a bacterial and an archaeal—diversified by multiple gene duplications, gene fissions and deletions, and accretion of novel components. Surprisingly, we find that the ‘tight adherence’ (Tad) systems originated from the interkingdom transfer from Archaea to Bacteria of a system resembling the ‘EppA-dependent’ (Epd) pilus and were associated with the acquisition of a secretin. The phylogeny and content of ancestral systems suggest that initial bacterial pili were engaged in cell motility and/or DNA uptake. In contrast, specialised protein secretion systems arose several times independently and much later in natural history. The functional diversification of the TFF superfamily was accompanied by genetic rearrangements with implications for genetic regulation and horizontal gene transfer: systems encoded in fewer loci were more frequently exchanged between taxa. This may have contributed to their rapid evolution and spread across Bacteria and Archaea. Hence, the evolutionary history of the superfamily reveals an impressive catalogue of molecular evolution mechanisms that resulted in remarkable functional innovation and specialisation from a relatively small set of components.

Application of Factor Analysis to k-means Clustering Algorithm on Transportation Data

Factor Analysis is a very useful linear algebra technique used for dimensionality reduction. It is also used for data compression and visualization of high dimensional datasets. This technique tries to identify from among a large set of variables, a reduced set of components which summarizes the original data. This is done by identifying groups of variables which have a strong inter correlation. The original variables are transformed into a smaller set of components which have a strong linear correlation. Using several data analysis techniques like Principal Components Analysis (PCA), Factor Analysis, cluster analysis may give insight into the patterns present in the data but may also give different results. The aim of this work is to study the use of Factor Analysis (FA) in capturing the cluster structures from transportation (HIS) data. It is proposed to compare the clustering obtained from original data from that of factor scores. Steps involved in preprocessing the transportation data are also illustrated. General Terms Clustering, Preprocessing.

On the incremental information content of the components of the Globe and Mail Corporate Governance Index

This study seeks to assess the construct validity of the Globe and Mail Corporate Governance Index. A number of recent studies that have examined corporate governance practices in Canada have relied on annual data from this index. We find an important degree of redundancy on the construction of the index. Our results indicate that fewer index components and even fewer questions to compose each component produces similar results, even for firms that are large and cross-listed. The study suggests that a further review of the theoretical justification of the properties of the Globe and Mail Corporate Governance Index is in order to streamline the selection of its components, thereby improving its quality. From the perspective of practitioners and policy makers, an implication of the study is that if a particular application requires the use of governance indexes for firms not on the list, our results justify relying on the use of a smaller set of components, with even fewer questions, to compose an index of comparable quality.

Component Sharing Through Licensing

In this paper, we consider products that are composed of distinct components that can be shared with rival firms through licensing agreements. In contrast to the standard licensing settings in which firms make binary choices (whether to license or not), the innovator decides on the set of product components to be licensed, i.e., on how much to license. The product components that are licensed out determines the degree of commonality in the competing products, which in turn affects post-licensing competition through the degree of product differentiation. In a duopoly setting we show that licensing occurs more often than what a binary choice setting predicts, while it does not necessarily imply more component-sharing between the firms. We also show that in a dynamic setting where the timing of entry depends on the components that are acquired through the license, the innovator may strategically grant a license for a smaller set of components to delay competition unless entry expands the market. Finally, we study a more general licensing scheme and show that a larger set of components are licensed out under a two-part scheme with a per-unit royalty than with a fixed fee scheme.

Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data

The performance of spatial filters based on independent components analysis (ICA) was evaluated by employing principal component analysis (PCA) preprocessing for dimensional reduction. The PCA preprocessing was not found to be a suitable method that could retain motor imagery information in a smaller set of components. In contrast, 6 ICA components selected on the basis of visual inspection performed comparably (61.9%) to the full range of 22 components (63.9%). An automated selection of ICA components based on a variance criterion was also carried out. Only 8 components chosen this way performed better (63.1%) than visually selected components. A similar analysis on the reduced set of electrodes over mid-central and centro-parietal regions of the brain revealed that common spatial patterns (CSPs) and Infomax were able to detect motor imagery activity with a satisfactory accuracy.

A comparison of multivariate statistical analysis protocols for ToF‐SIMS spectral images

AbstractTime‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) instruments produce raw data sets with a tremendous quantity of data. Multivariate statistical analysis (MVA) tools are being used to reduce the massive amount of chemical information into a smaller set of components that are easier to interpret and understand as a result of species association. Standard principal component analysis (PCA) is the most heavily used MVA algorithm in the ToF‐SIMS community, and is frequently computed using the singular value decomposition (SVD). Other algorithms such as multivariate curve resolution (MCR) have also gained popularity over the past few years. In this work, we compare the as‐measured ToF‐SIMS spectrum and ion images with four MVA data analysis protocols: standard PCA, image‐rotated SVD, spectra‐rotated SVD, and a PCA‐based MCR procedure. Image‐rotated SVD and spectra‐rotated SVD are closely connected to PCA and involve abstract rotations of the singular vectors that naturally arise during computation of the principal components via SVD. These rotations are designed to enhance either spatial contrast or spectral contrast in the components, respectively. We will show that the four MVA protocols provide essentially the same information, but accentuate different aspects of the sample's composition and lateral distribution, and that taken together these methods provide a more complete understanding of the sample. We will demonstrate that the component spectra estimated by MVA protocols assist the analyst in discovering minor constituents and understanding species correlation that would have been difficult, if not impossible, using univariate analysis protocols. For the data set described here, MVA tools identified unexpected species, which were not obvious in the as‐measured data. Copyright © 2008 John Wiley & Sons, Ltd.

Performance problem prediction in transaction-based e-business systems

Key areas in managing e-commerce systems are problem prediction, root cause analysis, and automated problem remediation. Anticipating SLO violations by proactive problem determination (PD) is particularly important since it can significantly lower the business impact of application performance problems. The main contribution of this paper is to investigate proactive PD based on two important concepts: dependency graphs and dynamic runtime performance characteristics of resources that comprise an I/T environment. The authors show how one can calculate and use the contribution of all supporting resources for a transaction to the end-to-end SLO for that transaction. Higher order moments of these components' contributions are further tracked for proactive alerting. An important aspect of this process is the classification of user transactions based on the profile of their resource usage, enabling one to set appropriate thresholds for the different classes only. Combined with the complete or semi-complete dependency information, our approach confines the scope of potential root causes to a small set of components, thus enabling efficient performance problem anticipation and quick remediation.

DETERMINATION OF THE SENSORY ATTRIBUTES OF WHEAT SOURDOUGH BREAD 1

ABSTRACTA lexicon for describing the flavor of wheat sourdough bread (WSB) was developed. A highly trained, descriptive sensory panel identified, defined, and referenced 32 flavor attributes for WSB. To determine the flavor characteristics of WSB, 37 samples were selected or prepared to represent a wide range of both commercial and laboratory variations. Crust and crumb of sourdough bread had different flavor properties; thus, they were evaluated separately. Twenty six attributes described the characteristics of sourdough bread crumb, and 28 attributes described the characteristics of crust. Some attributes were found in most or all samples, but many attributes were found in only one or a few samples. All 37 samples had distinctly different flavor attributes and intensities in both crumb and crust samples. The principal components analysis for crumb and crust indicated that the number of attributes could not be reduced into a smaller set of components that completely described the wide range of breads used in this study. However, reducing the number of attributes measured might be possible when only a few WSB samples are evaluated.

Bayesian analysis for masked system failure data using non-identical Weibull models

In ideal circumstances, failure time data for a K component series system contain the time to failure along with information on the exact component responsible for the system failure. These data then can be used to estimate system and component reliabilities. In many cases, however, due to cost and time constraints, the exact component causing the system failure is not identified, but the cause of failure is only narrowed down to a subsystem or a smaller set of components. A Bayesian analysis is developed in this article for such masked data from a general K component system. The theoretical failure times for the K components are assumed to have independent Weibull distributions. These K Weibulls can have different scale and shape parameters, thus allowing wide flexibility into the model. Further flexibility is introduced in the choice of the prior. Three different prior models are proposed. They can model different prior beliefs and can further provide a vehicle to check for robustness with respect to the prior. A Gibbs sampling based method is described to perform the relevant Bayesian computations. The proposed model is applied to data on a system unit of a particular type of IBM PS/2 models.

System life data analysis with dependent partial knowledge on the exact cause of system failure

Because of cost and time factors the exact cause of system failure may be known only partially. For example, the cause is narrowed down to a component in a subsystem or a smaller set of components. This is called “masking” of the exact failure mode. Our paper focuses on reliability estimation when the masking probability is dependent on the particular cause of failure.