An Investigation Into Identity, Power and Autonomous EFL Learning Among Indigenous and Minority Students In Post-secondary Education: A Mexican Case Study

This critical ethnographic case study draws on Indigenous and minority students\u27 process of learning English as a Foreign Language (EFL) in Mexico. The study specifically focuses on students who enrolled in a program called A wager with the Future. The aim of the study is to identify and understand contributing factors in these students’ struggles with the process of learning English by focusing on factors that influence their investment in EFL. The research is framed by (critical) applied linguistics and post-colonial theories that favour the integration of an understanding of these students’ socio historical context in their learning of English, and question (unequal) power relationships between languages and cultures in Mexico. The methodology was designed to ensure trustworthiness by adopting multiple data collection techniques, and to decolonize the research process by using participatory methods that featured researcher/participant coanalysis of the data. On a macro level, findings show that students enrolled in the program experience a relationship with English that is rooted in Mexico’s colonial legacies (as expressed through discrimination in the EFL classroom), which has an impact on their subjectivities; specifically, they feel afraid and inferior in the EFL classroom. On a micro level, the programming adopted in the university’s Language Department does not draw on diverse students’ multi-competences in other languages. Nonetheless, some Indigenous students manage to invest in EFL by creating imagined communities, and appropriating English through the creation of autonomous pluralistic language learning strategies

Transport hub flow modelling

Purpose: The purpose of this paper is to investigate the road freight haulage activity. Using the physical and data flow information from a freight forwarder, we intend to model the flow of inbound and outbound goods in a freight transport hub. Approach: This paper presents the operation of a road haulage group. To deliver goods within two days to any location in France, a haulage contractor needs to be part of a network. This network handles the processing of both physical goods and data. We will also explore the ways in which goods and data flows are connected. We then build a first model based on Ordinary Differential Equations which decrypt the flow of goods inside the hub and which is consistent with available data. This first model is designed to work at a fine-scale level. A second model which aggregates factors of the fine scale one is also built and a way to couple hub models to build a hub network is depicted. Tests are carried out to show the accuracy of the models. Finally, an explanation on how to use the models for industrial process optimizing is given

Critical Language Awareness

In the latter half of the 20th century, applied linguists, dissatisfied with the positioning of language teaching, called for a multidimensional curriculum to reframe teaching (about) languages, be they first or heritage languages (L1s or HLs); English as a second, foreign or international language (ESL, EFL and EIL); or other foreign languages (FLs). Their dissatisfaction stemmed from languages being viewed in isolation (like linguistic silos), an overemphasis on teaching the four skills in a discrete (unintegrated) manner, and decontextualized grammar and vocabulary teaching. Out of this discontent grew the notion of “language awareness,” with language awareness pedagogy implemented in the UK school system for the first time in 1974. The notion and pedagogical interventions emerged from the desire to bridge languages taught in isolation, and recognize the role language plays in all subject matter teaching (i.e., language-across-the- curriculum)

Exploratory analysis of excitation-emission matrix fluorescence spectra with self-organizing maps as a basis for determination of organic matter removal efficiency at water treatment works

In the paper, the self-organizing map (SOM) was employed for the exploratory analysis of fluorescence excitation-emission data characterizing organic matter removal efficiency at 16 water treatment works in the UK. Fluorescence spectroscopy was used to assess organic matter removal efficiency between raw and partially treated (clarified) water to provide an indication of the potential for disinfection by-products formation. Fluorescence spectroscopy was utilized to evaluate quantitative and qualitative properties of organic matter removal. However, the substantial amount of fluorescence data generated impeded the interpretation process. Therefore a robust SOM technique was used to examine the fluorescence data and to reveal patterns in data distribution and correlations between organic matter properties and fluorescence variables. It was found that the SOM provided a good discrimination between water treatment sites on the base of spectral properties of organic matter. The distances between the units of the SOM map were indicative of the similarity of the fluorescence samples and thus demonstrated the relative changes in organic matter content between raw and clarified water. The higher efficiency of organic matter removal was demonstrated for the larger distances between raw and clarified samples on the map. It was also shown that organic matter removal was highly dependent on the raw water fluorescence properties, with higher efficiencies for higher emission wavelengths in visible and UV humic-like fluorescence centers

Simultaneous voltammetric determination of heavy metals by use of crown ether-modified electrodes and chemometrics

A three-sensor array consisting of a graphite-epoxy composite electrode (GEC), 4-carboxybenzo-18-crown-6-GEC and 4- carboxybenzo-15-crown-5-GEC was employed for the simultaneous determination of Cd(II), Pb(II) and Hg(II) by differential pulse anodic stripping voltammetry (DPASV). Sensors were firstly studied for the determination of Hg(II); secondly, peak current responses confirmed that all sensors showed differentiated response for the three considered metals. A response model was developed to resolve mixtures of Cd(II), Pb(II) and Hg(II) at the μg L⁻¹ level; Discrete Wavelet Transform was selected as preprocessing tool and artificial neural network used for the modelling of the obtained responses

Comparison of multivariate calibration techniques applied to experimental NIR data sets

The present study compares the performance of different multivariate calibration techniques applied to four near-infrared data sets when test samples are well within the calibration domain. Three types of problems are discussed: the nonlinear calibration, the calibration using heterogeneous data sets, and the calibration in the presence of irrelevant information in the set of predictors. Recommendations are derived from the comparison, which should help to guide a nonchemometrician through the selection of an appropriate calibration method for a particular type of calibration data. A flexible methodology is proposed to allow selection of an appropriate calibration technique for a given calibration problem.54460862

Potentiometric Electronic Tongue to Resolve Mixtures of Sulfide and Perchlorate Anions

This work describes the use of an array of potentiometric sensors and an artificial neural network response model to determine perchlorate and sulfide ions in polluted waters, by what is known as an electronic tongue. Sensors used have been all-solid-state PVC membrane selective electrodes, where their ionophores were different metal-phtalocyanine complexes with specific and anion generic responses. The study case illustrates the potential use of electronic tongues in the quantification of mixtures when interfering effects need to be counterbalanced: relative errors in determination of individual ions can be decreased typically from 25% to less than 5%, if compared to the use of a single proposed ion-selective electrode

The impact of temperature variations on spectroscopic calibration modelling: a comparative study

Temperature fluctuations can have a significant impact on the repeatability of spectral measurements and as a consequence can adversely affect the resulting calibration model. More specifically, when test samples measured at temperatures unseen in the training dataset are presented to the model, degraded predictive performance can materialise. Current methods for addressing the temperature variations in a calibration model can be categorised into two classes—calibration model based approaches, and spectra standardisation methodologies. This paper presents a comparative study on a number of strategies reported in the literature including partial least squares (PLS), continuous piecewise direct standardisation (CPDS) and loading space standardisation (LSS), in terms of the practical applicability of the algorithms, their implementation complexity, and their predictive performance. It was observed from the study that the global modelling approach, where latent variables are initially extracted from the spectra using PLS, and then augmented with temperature as the independent variable, achieved the best predictive performance. In addition, the two spectra standardisation methods, CPDS and LSS, did not provide consistently enhanced performance over the conventional global modelling approach, despite the additional effort in terms of standardising the spectra across different temperatures. Considering the algorithmic complexity and resulting calibration accuracy, it is concluded that the global modelling (with temperature) approach should be first considered for the development of a calibration model where temperature variations are known to affect the fundamental data, prior to investigating the more powerful spectra standardisation approaches. Copyright © 2007 John Wiley & Sons, Ltd

Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC

Cholesteryl esters have antimicrobial activity and likely contribute to the innate immunity system. Improved separation techniques are needed to characterize these compounds. In this study, optimization of the reversed-phase high-performance liquid chromatography separation of six analyte standards (four cholesteryl esters plus cholesterol and tri-palmitin) was accomplished by modeling with an artificial neural network–genetic algorithm (ANN-GA) approach. A fractional factorial design was employed to examine the significance of four experimental factors: organic component in the mobile phase (ethanol and methanol), column temperature, and flow rate. Three separation parameters were then merged into geometric means using Derringer’s desirability function and used as input sources for model training and testing. The use of genetic operators proved valuable for the determination of an effective neural network structure. Implementation of the optimized method resulted in complete separation of all six analytes, including the resolution of two previously co-eluting peaks. Model validation was performed with experimental responses in good agreement with model-predicted responses. Improved separation was also realized in a complex biological fluid, human milk. Thus, the first known use of ANN-GA modeling for improving the chromatographic separation of cholesteryl esters in biological fluids is presented and will likely prove valuable for future investigators involved in studying complex biological samples