Does training policy help to attract, retain and develop valuable human resources? Analysis from the Mondragon case

This is an electronic version of the accepted paper in the journal:Advances in the Economic Analysis of Participatory and Labor-Managed Firms. Volumen. 12

Causes of fish depletion: a factor analysis approach

A study was conducted in 54 wetlands of 13 districts of Assam, India to evaluate the causes of fish depletion. Twenty-two variables were considered for the study. Seven factors were extracted through factor analysis (Principal Component Analysis) based on Eigen Value Criteria of more than one. These seven factors together accounted for 69.3% of the total variance. Based on the characteristics of the variables, all the factors were given descriptive names. These variables can be used to measure the extent of management deficiency of the causes of fish depletion in the wetlands. The factors are management deficiency, organic load interference, catchment condition, extrinsic influence, fishermen’s ignorance, external environment and aquaculture program. Management deficiency accounted for a substantial portion of the total variance.

A multivariate statistical study with a factor analysis of recent planktonic foraminiferal distribution in the Coromandel Coast of India

A study of planktonic foraminiferal assemblages from 19 stations in the neritic and oceanic regions off the Coromandel Coast, Bay of Bengal has been made using a multivariate statistical method termed as factor analysis. On the basis of abundance, 17 foraminiferal species, species were clustered into 5 groups with row normalisation and varimax rotation for Q-mode factor analysis. The 19 stations were also grouped into 5 groups with only 2 groups statistically significant using column normalisation and varimax rotation for R-mode analysis. This assemblage grouping method is suitable because groups of species/stations can explain the maximum amount of variation in them in relation to prevailing environmental conditions in the area of study.

Factor analysis based VTS and JUD noise estimation and compensation

Model based compensation schemes are a powerful approach for noise robust speech recognition. Recently there have been a number of investigations into adaptive training, and estimating the noise models used for model adaptation. This paper examines the use of EM-based schemes for both canonical models and noise estimation, including discriminative adaptive training. One issue that arises when estimating the noise model is a mismatch between the noise estimation approximation and final model compensation scheme. This paper proposes FA-style compensation where this mismatch is eliminated, though at the expense of a sensitivity to the initial noise estimates. EM-based discriminative adaptive training is evaluated on in-car and Aurora4 tasks. FA-style compensation is then evaluated in an incremental mode on the in-car task. © 2011 IEEE.

Factor analysis based VTS discriminative adaptive training

Vector Taylor Series (VTS) model based compensation is a powerful approach for noise robust speech recognition. An important extension to this approach is VTS adaptive training (VAT), which allows canonical models to be estimated on diverse noise-degraded training data. These canonical model can be estimated using EM-based approaches, allowing simple extensions to discriminative VAT (DVAT). However to ensure a diagonal corrupted speech covariance matrix the Jacobian (loading matrix) relating the noise and clean speech is diagonalised. In this work an approach for yielding optimal diagonal loading matrices based on minimising the expected KL-divergence between the diagonal loading matrix and "correct" distributions is proposed. The performance of DVAT using the standard and optimal diagonalisation was evaluated on both in-car collected data and the Aurora4 task. © 2012 IEEE.

Healthcare human reliability analysis - By heart

As part of the investigations into a surgical incident involving the accidental retention inside a patient's venous system of a guide wire for central venous catheterisation (CVC), the Human Error Assessment and Reduction Technique (HEART) was used to examine the potential for further occurrences. It was found to be time-efficient and to yield plausible probabilities of human error, although its use in healthcare has challenges, suggesting adaptation would be beneficial.

Healthcare human reliability analysis – by HEART

This book presents the proceedings of the international conference on Contemporary Ergonomics and Human Factors 2013.

Excretion pattern investigation of urinary normal and modified nucleosides of breast cancer patients by RP-HPLC and factor analysis method

Modified nucleosides, formed post-transcriptionally in RNA by a number of modification enzymes, are excreted in abnormal levels in the urine of patients with malignant tumors. To test their usefulness as tumor markers, and to compare them with the conventional tumor markers, a reversed-phase high-performance liquid chromatographic (RP-HPLC) method and a factor analysis method have been used to study the excretion pattern of nucleosides of breast cancer patients. A clear cut differentiation of the breast cancer group and the healthy individuals in two clusters without overlapping was obtained. Copyright (C) 2000 John Wiley & Sons, Ltd.

Factor analysis used to peak position optimization and spectral interference correction in inductively coupled plasma atomic emission spectrometry

Target transformation factor analysis was used to correct spectral interference in inductively coupled plasma atomic emission spectrometry (ICP-BES) for the determination of rare earth impurities in high purity thulium oxide. Data matrix was constructed with pure and mixture vectors and background vector. A method based on an error evaluation function was proposed to optimize the peak position, so the influence of the peak position shift in spectral scans on the determination was eliminated or reduced. Satisfactory results were obtained using factor analysis and the proposed peak position optimization method.

APPLICATION OF FACTOR-ANALYSIS TO CORRECTION OF SPECTRAL OVERLAP INTERFERENCE IN INDUCTIVELY COUPLED PLASMA ATOMIC EMISSION-SPECTROMETRY

Correction of spectral overlap interference in inductively coupled plasma atomic emission spectrometry by factor analysis is attempted. For the spectral overlap of two known lines, a data matrix can be composed from one or two pure spectra and a spectrum of the mixture. The data matrix is decomposed into a spectra matrix and a concentration matrix by target transformation factor analysis. The component concentration of interest in a binary mixture is obtained from the concentration matrix and interference from the other component is eliminated. This method is applied to correcting spectral interference of yttrium on the determination of copper and aluminium: satisfactory results are obtained. This method may also be applied to correcting spectral overlap interference for more than two lines. Like other methods of correcting spectral interferences, factor analysis can only be used for additive spectral overlap. Results obtained from measurements on copper/yttrium mixtures with different white noise added show that random errors in measurement data do not significantly affect the results of the correction method.