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.

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.

Biosynthesis of the metallic nanoparticle is gaining importance because it’s a single-step process, nontoxic, environmentally acceptable, and easily scaled up. The present study investigated the biosynthesis of silver nanoparticles (AgNPs) via reacting olive wastewater filtrate (OWF) with silver nitrate, and the formation of AgNPs was confirmed by a color change of the reaction mixture and visible spectrophotometry. Additionally, the influence of pH, reaction time, AgNO₃ concentration, temperature, and OWF volume on the proposed method was investigated. It was found that with increasing the previously mentioned parameters, the formation of the AgNPs was increased under the experimental conditions. The results showed that OWF represented a promising material for the biosynthesis of AgNPs.      

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.

A convenient method of preparing ultrafine polystyrene latex nano-particles with chlorine groups on the surface is developed. Polystyrene latexes in the size range 50–400 nm were prepared via emulsion polymerization, using sodium dodecyl sulfate (SDS) as surfactant. The poly styrene with chlorine groups on the surface will be fine to use as organic filler to modify rubber. Transmission electron microscopy (TEM) was used to observe the morphology of silicon dioxide and functionalized polystyrene nanoparticles. The nature of bonding between the polymer and the reactive groups on the filler surfaces was analyzed using Fourier transform infrared spectroscopy (FTIR). Scanning electron microscopy (SEM) was employed to examine the filler surface.

Abstract: Sitagliptin is a drug used in treatment of type 2 diabetes mellitus. Incretin hormones, including glucagonlike peptide-1 analogue and dipeptidyl peptidase-4 inhibitor have recently been found to regulate glucose metabolism. Thus, the aim of this study was to evaluate the effect of sitagliptin on glycemic control and it is associated side effects in Libyan patients with type 2 diabetes, who had inadequate response to existing anti-diabetic drugs. Hundred patients with a known history of type 2 diabetes were recruited in this study during the period of 2019 and 2020. Sitagliptin (100 mg daily) was added on to the pre-existing therapy for type 2 diabetes and the main outcome measures were a change from the base line in glycated hemoglobin and fasting plasma sugar as well as the incidence of symptomatic hypoglycemia. All patients were engaged from outpatient clinic of National Diabetes and Endocrinology Center at Tripoli, Libya. The findings show that the actions of dipeptidyl peptidase-4 inhibitors improve glycaemic control in the patients, and glycated hemoglobin as well as fasting plasma sugar. Thus, sitagliptin is a profound drug for a comprehensive treatment of patients with type 2 diabetes with a low incidence of hypoglycemia.