Taiwanese Learners’ Motivation and Attitude Towards Completing an Online Distance Education Course During a One Year Industrial Placement

This mixed methods study investigated language learning motivation in an one-year e-learning course for technological university students to bridge the geographical divide between students on industrial placements when studying graded readers using an e-learning course to improve their English competence and to pass the General English Proficiency Test. Data was collected through questionnaires and course feedback. The results of this study extend Gardner’s socio-educational model in an e-learning environment by adding the new category, Computer Attitudes, which was proven to be highly correlated with Motivation. Although the low proficiency English students had good computer skills, their habits of using the computer for entertainment and their lack of the skill of “technological communication efficacy” caused increased anxiety when using computers and thus provided them with a lower computer confidence over time. Consequently, it is recommended that sound e-learning training should be provided to all of the students prior to embarking on an e-leaning course so that these learners can benefit from online language learning in the future.

Layered RF Phantom Characterization for Wireless Medical Vital Sign Monitors

Wearable antenna performance measurements were used to characterize a synthetic variable layered phantom testbed, representative of human tissue for operation in the 868/915 MHz, and 2400 MHz industrial, scientific and medical frequency bands. Antenna radiation efficiency measurements on the phantom were compared with measurements on the thorax region of a human test subject. The results show that the phantom is representative of the human body for the application of wireless vital sign monitors, where conductive connections are made to the tissue.

Phosphorus Adsorption onto an Industrial Acidified Laterite By‒Product: Equilibrium and Thermodynamic Investigation

The present research investigates the uptake of phosphate ions from aqueous solutions using acidified laterite (ALS), a by-product from the production of ferric aluminium sulfate using laterite. Phosphate adsorption experiments were performed in batch systems to determine the amount of phosphate adsorbed as a function of solution pH, adsorbent dosage and thermodynamic parameters per fixed P concentration. Kinetic studies were also carried out to study the effect of adsorbent particle sizes. The maximum removal capacity of ALS observed at pH 5 was 3.68 mg P g^-1. It was found that as the adsorbent dosage increases, the equilibrium pH decreases, so an adsorbent dosage of 1.0 g L^-1 of ALS was selected. Adsorption capacity (q_m) calculated from the Langmuir isotherm was found to be 2.73 mg g^-1. Kinetic experimental data were mathematically well described using the pseudo first-order model over the full range of the adsorbent particle size. The adsorption reactions were endothermic, and the process of adsorption was favoured at high temperature; the ΔG and ΔH values implied that the main adsorption mechanism of P onto ALS is physisorption. The desorption studies indicated the need to consider a NaOH 0.1M solution as an optimal solution for practical regeneration applications.

Towards more flexible Architecture Description Languages for industrial applications

Architecture Description Languages (ADLs) have emerged in recent years as a tool for providing high-level descriptions of software systems in terms of their architectural elements and the relationships among them. Most of the current ADLs exhibit limitations which prevent their widespread use in industrial applications. In this paper, we discuss these limitations and introduce ALI, an ADL that has been developed to address such limitations. The ALI language provides a rich and flexible syntax for describing component interfaces, architectural patterns, and meta-information. Multiple graphical architectural views can then be derived from ALI's textual notation.

PRECYSE: Cyber-attack Detection and Response for Industrial Control Systems

In this short paper, we present an integrated approach to detecting and mitigating cyber-attacks to modern interconnected industrial control systems. One of the primary goals of this approach is that it is cost effective, and thus whenever possible it builds on open-source security technologies and open standards, which are complemented with novel security solutions that address the specific challenges of securing critical infrastructures.

Production of porous aluminium and iron sulphated oxyhydroxides using industrial grade coagulants for optimised arsenic removal from groundwater

The optimisation of Fe and Al oxyhydroxide materials produced using industrial grade coagulants is presented in this work. The effects of synthesis pH and post-synthesis washing procedure onto the arsenic adsorption capacity of the materials were investigated. It was shown that the materials produced at higher pH were more efficient in removing As(V), especially after cleaning procedure. The materials produced at lower pH were less efficient in removing As(V) but the higher presence of sulphate groups in the materials produced at lower pH enhanced As(III) adsorption. Most performing materials can remove up to 84.7 mg As(V) g^-1 or 77.9 mg As(III) g^-1.

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

Institutional maintenance work and power preservation in business exchanges: Insights from industrial supplier workshops

This paper aims to offer new theoretical and empirical insights into power dynamics in an industrial supplier workshop setting. Theoretically, it advances an institutional perspective on supplier workshops as an important venue in managing, preserving and instituting industrial market power. Based on a detailed ethnographic analysis of an industrial workshop setting, this article investigates the institutional maintenance work of Retail Co. in preserving the power dynamics of market dominance in business exchanges and market structures. Our findings revealed three previously unreported insights into the subtle, but nonetheless pervasive power from institutional maintenance work in an industrial workshop setting. First, the institutional workshop work comprised a cultural performance; constituting socialization practice through a performance game, the power of numbers in field comprehension and an award ceremony. Second, the institutional workshop work mobilized projective agency, stipulating, directing and appealing for the instituting of distinct market rules and collective identities. Finally, the institutional workshop work increases supplier docility and utility via the regulative technologies-of-the-self to enhance business planning, operations and market decision-making practice, without necessarily being seen to be disciplinarian.

An Adaptive Machine Learning Decision System for Flexible Predictive Maintenance

Process monitoring and Predictive Maintenance (PdM) are gaining increasing attention in most manufacturing environments as a means of reducing maintenance related costs and downtime. This is especially true in industries that are data intensive such as semiconductor manufacturing. In this paper an adaptive PdM based flexible maintenance scheduling decision support system, which pays particular attention to associated opportunity and risk costs, is presented. The proposed system, which employs Machine Learning and regularized regression methods, exploits new information as it becomes available from newly processed components to refine remaining useful life estimates and associated costs and risks. The system has been validated on a real industrial dataset related to an Ion Beam Etching process for semiconductor manufacturing.

Development of a slurry continuous flow reactor for photocatalytic treatment of industrial waste water

The water treatment capability of a novel photocatalytic slurry reactor was investigated using methylene blue (MB) as a model pollutant in an aqueous suspension. A pellet TiO ₂ catalyst was employed and this freed the system from the need of filtration of catalyst following photocatalysis. This configuration combines the high surface area contact of catalyst with pollutant of the slurry reactor and also offers a high illumination of catalyst by its unique array of weir-like baffles. In this work, the batch adsorption of MB from aqueous solution (10μM) onto the TiO ₂ catalyst was studied, adsorption isotherms and kinetics were determined from the experimental data. Complete degradation of MB was achieved within 60 min illumination with various loadings of catalyst (30-200 g L ^-1). A modest catalyst loading (30 g L ^-1) achieved 98% degradation within 60 min of irradiation. Experimental results indicate that this novel reactor configuration has a high effective mass transfer rate and UV light penetration characteristics. 

Dipping Wings:for flute (alto fl), clarinet (bass cot), tpt, vla, vcl, perc and live electronics

commissioned by Ballet Rambert for 60th Anniversary season, choreographer Mary Evelyn, designer Liz Emmanuel. World premiere: Theatre Royal York 03/06/86

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.