Abstract

Although listening skill plays a significant role in daily communication and

education process in teaching English , listening subject has long been neglected

in second language teaching. This study aims to explore problems, difficulties

and barriers in teaching listening to Libyan students in our case study Alassaba

area, 3rd year secondary science school, Mosab Ben Omer Secondary School

and Omer Ben Alkhtab Secondary school. The participants of our study were 5

teachers. Classroom observation and teacher’s interview are used for data

collection. It is found that all teachers were using grammar translation method

in teaching listening. Most classes were uncomfortable learning atmosphere,

beside the absence of technology which used and facilitate teaching listening.

The findings based on data analysis and discussion may be of a great

significance to find solutions for these problems and improving the way of

teaching listening

Prosopis farcta (Banks & Sol.) J. F. Macbr. has been reported for the first time from Libya. This species is native in Asia. Flowering specimens of Prosopis farcta collected from Sokna Region about 650 Km south-east Tripoli. It is presented with updated nomenclature, taxonomic description, geographical distribution, place of occurrence in Libya, and field colored photographs are also provided. The voucher specimens were deposited in the Herbarium of Botany Department (ULT), University of , Tripoli, Libya.

Experimental and numerical investigations have been performed to study the natural convection heat transfer from a vertical rectangular fin arrays at different orientation angles.An experimental setup was constructed and calibrated to test different fin configurations. It basically consists of base plate, an array of parallel longitudinal fins, heating unit and layers of thermal insulation. Fin length (L) and fin thickness (t) were kept fixed at 187 and 6.5 mm respectively, while fin spacing (S) was varied from 3 to 16 mm and fin height (H) was varied from 15 to 45 mm. The orientation angle (β) was changed from 0° to 60°, and temperature difference between fin and surrounding (∆T) from 30 to 95 o C.Base-to-ambient temperature difference was also varied through a calibrated wattmeter ranging from 10 to 180W. To understand the general flow patterns dominating flows from the heat sink, the three-dimensionless elliptic governing equations were solved using finite volume computational fluid dynamics (CFD) code. A comparative study between the experimental and numerical results was performed to verify the numerical code. It was found for the configuration tested that the heat transfer rate per unit base area increases with the increase in the fin spacing and reaches a maximum value then decreases with farther increase in the fin spacing. The maximum heat dissipation occurs at optimal spacing S opt =7 mm. Empirical correlations between Nussult number, Rayleigh number, fin spacing, fin height, orientation angle, temperature difference between the fin and surroundings were derived. Finally the present work general empirical formula is given in the form =. .. .. Where , 15 mm ≤ H ≤ 45 mm, 3mm ≤ S ≤ 16 mm, °0 ≤ β ≤°60, t = 6.5 mm, L = 187 mm.

A novel non-blind audio watermarking algorithm, using chirp z-transform in combination with discrete wavelet transform and singular value decomposition, is presented in this paper. In the proposed method, a watermark audio signal (about 13 seconds in length) is hidden into an original audio signal (about 51 seconds in length) by applying discrete wavelet transform, chirp z-transform, and singular value decomposition. The effectiveness of the proposed algorithm has been tested using signal-to-noise ratio, objective difference grade (ODG), and normalised cross- correlation of watermarked audio signal under various common audio attacks. Experimental results demonstrate that the algorithm has a high capacity (352.8 kbps), without significant perceptual distortion (ODG > -0.29) and provides good robustness against common audio attacks, including: re-sampling, echo addition, MP3 compression, additive white Gaussian noise, re- quantisation, cutting, and low-pass filtering. This algorithm has a high capacity and excellent audio quality compared with other audio watermarking algorithms.

Cooling accounts for 12-38% of total energy consumption in schools in the US, depending on the region. In this study, stacking learning is utilized to predict chiller running capacity for four school buildings (regression) and to predict the chiller status for four another schools (classification) using a collection of interval chiller data and building demand. Singular and multiple measurement periods within one or more seasons are considered. A generalized methodology for modeling building energy systems is posited that informs selection of features, data balancing to attain the best model possible, ensemble-based stacked learning in order to prevent over-fitting, and final model development based upon the results from the stacked learning. The results show that ensemble-based stacked learning improves the model performance substantially; providing the most accurate results for both regression and classification. for both classification and regression. For, classification, the balanced accuracy is 99.79% while Kappa is 99.39%. For regression, the R-squared value, the mean absolute error (MAE) error, and the root mean squared error (RMSE) are 1.78 kW, 2.77 kW, and 0.983 respectively.

Traditional building energy audits are both expensive, in the range of USD $1.29/m 2-$5.37/m 2, and inconsistent in their prediction of potential energy savings. Automation to reduce costs of evaluating the energy effectiveness of buildings is strongly needed. A key element of such automation is a means to estimate the building envelope energy effectiveness. We present a method that addresses this need by using infrared thermography to characterize building wall envelope effectiveness. To date, thermal imaging approaches for estimating wall R-Values, based upon thermal-physical models of walls, require additional manual measurements and analysis which prohibit low-cost, large-scale implementation. To overcome this implementation challenge, a machine learning approach is used to predict wall R-Values for a set of residences with known thermal resistance by utilizing the measured wall imaging temperature, prior weather conditions, historical energy consumption data, and available building geometrical data. The developed model is shown to predict wall R-Values with a maximum test-set root mean squared error of 7% using as few as nine training houses. This result has significant implications for low-cost large-scale envelope energy effectiveness characterization.