Analysis Of Experimental Datasets Research Articles

Test of models for 235U(nth,f) charge distributions and their impacts on the covariance analysis

Fission yields are of major interest for the nuclear industry and the study of the fission process. Fission yields are determined by evaluation process based on the analysis of experimental datasets and completed by phenomenological models. The aim is to provide the best estimation of independent yields of 235U(nth,f) reaction. In this paper, we will focus on the methodology of assessment of the nuclear charge distribution per mass. In the JEFF-3.1.1, the Wahl’s parametrization of the Zp-model is used to complete the experimental knowledge. Nevertheless, the lack of this parametrization consists in the absence of uncertainty information. Consequently, it is impossible to generate the components of independent yield correlation matrix from the Zp-model. In this context, we propose to use the Zp model with a new parametrization to assess the charge distributions. Two different calculations have been developed: the first one is based on a Wahl-like linear approximation of the parameters as a function of mass. The second approach consists to apply a “direct-Zp model” without linear assumptions. This analysis is achieved using the Markov-Chain Monte-Carlo (MCMC) method to adjust the model parameters and then to deduce the covariance of charge distributions. Moreover, with our development, we have directly and consistently described the pre-neutron and post-neutron charge distributions.

Correlation analysis of the relationship between Arrhenius viscosity parameters in Binary Liquid Mixtures

Recent linear model for pure fluids was developed by our research team. Extending and validating this model for binary fluid mixtures is the aim of this work. To achieve this, a statistical analysis of experimental data sets of 300 observations for 16 binary liquid mixtures in accordance with the families of organic liquid solvents over different temperature ranges, at atmospheric pressure, and at different molar fraction. It was found that the extended model for binary liquid mixtures facilitates the calculation of viscous behavior and engineering manipulations. In the 16 subgroups, it is possible to estimate a missing Arrhenius viscosity parameter if the second one is known. Furthermore, using statistical comparison techniques, one can accurately determine this missing parameter in specific subgroups. Those objectives help engineers to gain an in-depth understanding of pure liquids or mixtures of binary liquids to address their challenges in fluidization technology.

Fractal Dimension as Quantifier of EEG Activity in Driving Simulation

Dynamical systems and fractal theory methodologies have been proved useful for the modeling and analysis of experimental datasets and, in particular, for electroencephalographic signals. The computation of the fractal dimension of approximation curves in the plane enables the assignment of numerical values to bioelectric recordings in order to discriminate between different states of the observed system. The procedure does not require the stationarity of the signals nor extremely long segments of data. In previous works, we checked that this parameter is a good index for brain activity. In this paper, we consider this measurement in order to quantify the geometric complexity of the brain waves in states of rest and during vehicle driving simulation in different scenarios. This work presents evidence that the fractal dimension allows the detection of the brain bioelectric changes produced in the areas that carry out the different driving simulation tasks, increasing with their complexity.

Data Mining Model Based on Improved Ant Colony Algorithm

Data mining technology is an important means and way of data analysis in the Internet era, and it is a process of extracting potentially useful information from large databases. From the perspective of theory and technology, data mining is a process of discovering the relationship between data and models or between data from a huge database. From the application level, data mining can provide the government and enterprises with valuable and different levels of knowledge, and provide strong technical support for social development. The existing data methods mainly include classification method, association analysis method, cluster analysis method and anomaly detection method, but these methods have some shortcomings. Aiming at the problems of big randomness, sensitivity to parameters and slow convergence speed of existing ant colony clustering algorithm, this paper proposes a data mining model based on improved ant colony algorithm, which will be applied to cluster analysis of experimental data sets. The experimental results show that the improved algorithm has higher accuracy and faster convergence speed, and can effectively realize data information mining.

Extraction and Integration of Genetic Networks from Short-Profile Omic Data Sets.

Mass spectrometry technologies are widely used in the fields of ionomics and metabolomics to simultaneously profile the intracellular concentrations of, e.g., amino acids or elements in genome-wide mutant libraries. These molecular or sub-molecular features are generally non-Gaussian and their covariance reveals patterns of correlations that reflect the system nature of the cell biochemistry and biology. Here, we introduce two similarity measures, the Mahalanobis cosine and the hybrid Mahalanobis cosine, that enforce information from the empirical covariance matrix of omics data from high-throughput screening and that can be used to quantify similarities between the profiled features of different mutants. We evaluate the performance of these similarity measures in the task of inferring and integrating genetic networks from short-profile ionomics/metabolomics data through an analysis of experimental data sets related to the ionome and the metabolome of the model organism S. cerevisiae. The study of the resulting ionome–metabolome Saccharomyces cerevisiae multilayer genetic network, which encodes multiple omic-specific levels of correlations between genes, shows that the proposed measures can provide an alternative description of relations between biological processes when compared to the commonly used Pearson’s correlation coefficient and have the potential to guide the construction of novel hypotheses on the function of uncharacterised genes.

Separating adverse-pressure-gradient and Reynolds-number effects in turbulent boundary layers

Wall turbulence is characterized by a near-wall cycle of streaks and quasistreamwise vortices apparent as an invariant inner peak in the premultiplied energy spectra. A second, outer peak is known to emerge in this spectral view and become energized with increasing Reynolds number (Re) as well as adverse pressure gradient (APG). An analysis of experimental data sets examines how this outer peak scales with Re and APG and whether their imprint on the near-wall small scales are different.

Practical fit functions for transport critical current versus field magnitude and angle data from (RE)BCO coated conductors at fixed low temperatures and in high magnetic fields

Applications of (RE = Y, Gd)BCO coated conductors for the generation of high magnetic fields are increasing sharply, this while (RE)BCO coated conductors themselves are evolving rapidly. This article describes and demonstrates recently developed and applied mathematical models that systematically and comprehensively characterize the transport critical current angular dependence of a batch of (RE)BCO coated conductor in high magnetic fields at fixed temperatures with an uncertainty of 10% or better. The model development was based on analysis of experimental data sets from various published sources and coated conductors with different microstructures. These derivations directly are applicable to the accurate prediction of the performance in high magnetic fields of coils wound with (RE)BCO coated conductors. In particular, a nonlinear fit is discussed in this article of transport critical current at T = 4.2 K versus field and angle data. This fit was used to estimate the hysteresis losses of (RE)BCO coated conductors in high magnetic fields, and to design the inserts wound with such conductors of the all-superconducting 32 T magnet being constructed at the NHMFL. A series of such fits, recently developed at several fixed temperatures, continues to be used to simulate the quench behavior of that magnet.

Analysis of experimental data sets for local scour depth around bridge abutments using artificial neural networks

The performance of soft computing techniques to analyse and interpret the experimental data of local scour depth around bridge abutment, measured at different laboratory conditions and environment, is presented. The scour around bridge piers and abutments is, in the majority of cases, the main reason for bridge failures. Therefore, many experimental and theoretical studies have been conducted on this topic. This study sought to answer the following questions: Firstly, can data collected by different researchers at different times be combined in one data set? Secondly, can we determine any unquantified effects such as data differences, laboratory conditions and measurement devices? Artificial neural networks (ANN) are used and a basic ANN model is selected to observe the application problems, in order to avoid any misleading conclusion arising due to the model parameters selected and the compilation of different subsets of experimental data into one set. At the first stage, seven experimental data sets are compiled to address the first question and an ANN model is used to discovery any existing discrepancies between available data groups. The importance of selected model parameters for the model’s performance was demonstrated by increasing the number of parameters. Then, each data subset was inspected to expose the importance of the homogeneity of data groups in order to obtain a best-fit ANN model. Finally, a sensitivity analysis was carried out to obtain the dominant parameters of the problem. It was concluded that the use of ‘soft’ computational techniques such as ANN can be beneficial, provided the user is aware of the heterogeneity of the data set and the physical context of the subject or problem being addressed. However, as with other data analysis techniques, elaborate inspection of data and results is required. Keywords: Scour prediction methods, bridge abutment scour, soft computing techniques, ANN

Scaling, growth and cyclicity in biology: a new computational approach

BackgroundThe Phenomenological Universalities approach has been developed by P.P. Delsanto and collaborators during the past 2–3 years. It represents a new tool for the analysis of experimental datasets and cross-fertilization among different fields, from physics/engineering to medicine and social sciences. In fact, it allows similarities to be detected among datasets in totally different fields and acts upon them as a magnifying glass, enabling all the available information to be extracted in a simple way. In nonlinear problems it allows the nonscaling invariance to be retrieved by means of suitable redefined fractal-dimensioned variables.ResultsThe main goal of the present contribution is to extend the applicability of the new approach to the study of problems of growth with cyclicity, which are of particular relevance in the fields of biology and medicine.ConclusionAs an example of its implementation, the method is applied to the analysis of human growth curves. The excellent quality of the results (R2 = 0.988) demonstrates the usefulness and reliability of the approach.

Integrated analysis of experimental data sets reveals many novel promoters in 1% of the human genome

The regulation of transcriptional initiation in the human genome is a critical component of global gene regulation, but a complete catalog of human promoters currently does not exist. In order to identify regulatory regions, we developed four computational methods to integrate 129 sets of ENCODE-wide chromatin immunoprecipitation data. They collectively predicted 1393 regions. Roughly 47% of the regions were unique to one method, as each method makes different assumptions about the data. Overall, predicted regions tend to localize to highly conserved, DNase I hypersensitive, and actively transcribed regions in the genome. Interestingly, a significant portion of the regions overlaps with annotated 3'-UTRs, suggesting that some of them might regulate anti-sense transcription. The majority of the predicted regions are >2 kb away from the 5'-ends of previously annotated human cDNAs and hence are novel. These novel regions may regulate unannotated transcripts or may represent new alternative transcription start sites of known genes. We tested 163 such regions for promoter activity in four cell lines using transient transfection assays, and 25% of them showed transcriptional activity above background in at least one cell line. We also performed 5'-RACE experiments on 62 novel regions, and 76% of the regions were associated with the 5'-ends of at least two RACE products. Our results suggest that there are at least 35% more functional promoters in the human genome than currently annotated.

Modelling requirements for development of an advanced Czochralski control system

A low order model is presented that captures important equipment and process characteristics for development of an advanced control system for Czochralski crystal growth. Analysis of experimental data sets reveals important control issues, leading to development of a new control structure. A model based performance comparison of the conventional and proposed control structure is presented.

Critical examination of the radial functions in the Hansen-Coppens multipole model through topological analysis of primary and refined theoretical densities.

A double-zeta (DZ) multipolar model has been applied to theoretical structure factors of four organic molecular crystals as a test of the ability of the multipole model to faithfully retrieve a theoretical charge density. The DZ model leads to significant improvement in the agreement with the theoretical charge density along the covalent bonds and its topological parameters, and eliminates some of the bias introduced by the limited flexibility of the radial functions when a theoretical density is projected into the conventional multipole formalism. The DZ model may be too detailed for analysis of experimental data sets of the accuracy and resolution typically achieved at present, but provides guidance for the type of algorithms to be adapted in future studies.