Aims. To estimate the prevelance of Generalized anxiety disorder (GAD) in adult patients with diabetes mellitus (T1DM or T2DM) during COVID-19 pandemic. Method. Random sample of 115 Adult Libyan patients (≥18 years) were drawn from 1200 Medical records of diabetic patients previously diagnosed in a primary care clinic (Gharyan polyclinic,South of Tripoli,West of Libya). Patients were recruited and diagnostically interviewed through outpatient visits and through Phone calls. Anxiety was assessed using Generalized Anxiety Disorder 7-item instrument (GAD-7), personal information, Co morbidities and History of COVID-19 infection within period of 3 weeks. Result. The statistical analysis done by SPSS version 23, using ANOVA test. The GAD-7 scores ranged from 0 to 19 for the diabetic patient, 82 patients scores ranged from 0 to 4 with varying degrees of non-signifacant to subsyndromal symptopms of Generalized anxiety disorder, 24 patient with Mild GAD, 7 patients with moderate GAD and 2 patients with severe GAD. (P value = 0.000) Conclusion. GAD is present in 28% of the patients who participated in the study. Additional epidemiological studies are needed to determine the prevalence of anxiety in the broader population of persons with diabetes

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.

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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.