Assessing methodologies for calculating road traffic noise levels in Ireland - Converting CRTN indicators to the EU indicators (Lden, Lnight)

To comply with the EU Noise Directive 2002/49/EC, Member States are required to produce strategic noise maps for designated areas, including mapping road traffic noise from major roads. These maps must be presented using the EU indicators L_den and L_night. However, the most common noise indicator used in Ireland at present is the L_A10,18h indicator arising from the use of the Calculation of Road Traffic Noise (CRTN) prediction method. Therefore, a relationship needs to be established between L_A10,18h and L_den and L_night, separately. In addition to noise mapping these indicators are used for noise abatement purposes, so the proposed relationship must be accurate and robust. In 2002, the UK’s Transport Research Laboratory (TRL) published a paper describing mathematical procedures that could be used to convert values of L_A10 to L_den and L_night. These procedures were then adopted for use in Ireland. This paper examines the suitability of the TRL conversion methods 1 and 3 for use under Irish road conditions. Method 2 was not considered in this study, as it was a methodology not applicable in an Irish scenario. Studies concluded that where hourly traffic data are available, the conversion methodology outlined in TRL Method 1 is robust and reproducible. However, in the absence of hourly traffic data where daily traffic counts are used, the relevant conversion procedures produce variable results for both L_den and L_night when applied to Irish road conditions. To reduce the variability, new conversion procedures were developed, specifically for Irish road conditions.     

A fuzzy expert system capable of computing LA,max for the Tehran-Karaj commuter train

This paper describes the methodology through which a fuzzy expert system capable of computing maximum A-weighed noise level (L_A,max) for the Tehran-Karaj commuter train is developed, tested, tuned and validated on the basis of the 50 measurements from five locations at distances of 25 m, 35 m, 45 m, 55 m, and 65 m from the centre of the track and at a height of 1.5 m. The expert system is initially developed, tested and tuned through the judgements made on the premise of the samples taken at the distances of 25, 45 and 65 m. The conducted hypothesis test, the non-parametric two-related samples Wilcoxon test, for this stage indicates satisfactory results. Eventually the developed expert system is validated for the measurements related to the distances of 35 and 55 m through carrying out the non-parametric two-independent samples Kolmogorov-Smirnov test.     

Mathematical, statistical and neural models capable of predicting LA,max for the Tehran-Karaj express train

This paper presents mathematical logarithmic, statistical linear regression, and neural models capable of predicting maximum A-weighed noise level (L_A,max) for the Tehran-Karaj express train. The models have been developed upon the basis of the measurements from sampling locations at distances of 25 m, 45 m, and 65 m from the centreline of the track and at a height of 1.5 m. In the next step, the predictive capability of the models have been tested on the data associated with the sampling locations, situated, respectively at distances of 35 and 55 m from the centreline of the track at a height of 1.5 m. The non-parametric tests i.e. two-related samples Wilcoxon, and two-independent samples Kolmogorov-Smirnov, carried out, respectively for training and testing steps, indicate satisfactory results. In the final step the non-parametric k-related samples Friedman test detects no significant differences amongst the absolute testing set error of the models.     

Thickness study of Al:ZnO film for application as a window layer in Cu(In1−xGax)Se2 thin film solar cell

Structural, electrical and optical properties of Al doped ZnO (Al:ZnO) thin film of various thicknesses, grown by radio-frequency magnetron sputtering system were studied in relation to the application as a window layer in Cu(In_1−xGa_x)Se₂ (CIGS) thin film solar cell. It was found that the electrical and structural properties of Al:ZnO film improved with increasing its thickness, however, the optical properties degraded. The short circuit current density, J_sc of the fabricated CIGS based solar cells was significantly influenced by the variation of the Al:ZnO window layer thickness. Best efficiency was obtained when CIGS solar cell was fabricated with electrically and optically optimized Al:ZnO window layer.     

Deduction of a three-phase model for the (√3 × √3)R30°-Cu2Si/Cu(1 1 1) surface alloy

The (√3 × √3)R30°-Cu₂Si/Cu(1 1 1) surface alloy that forms during high temperature dosing of silane (SiH₄) on Cu(1 1 1) has been investigated using LCAO-DFT. Simulated STM images have shown that experimental images may be interpreted as a mixed phase system consisting of Si ion cores bound in HCP, FCC and twofold bridge sites with a ratio of 25:25:50 rather than previously proposed models where the Si ion cores were bound in only FCC and HCP sites. The new model is shown to be consistent with previously published NIXSW studies.