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.

Abstract: The most effective treatment for severe acne is isotretinoin. However, it carries major side effects as teratogenicity, dryness, lipid profile disturbance and constipation. Although it is commonly prescribed with and without prescription in Libya, little date are available regarding patients understanding and awareness of isotretinoin. Aims: This study was conducted to explore the awareness of acne patients about the use of isotretinoin. It aims to measure how much acne patients know about the side effects of the drug especially the teratogenic effects, how to deal with them and how the drug influence their daily activities. Plus, to identify how patients get their information about the drug and whether they consult professional or ordinary people. Methods: A verified and validated questionnaire addressed to acne patients (n = 50, Sept. - Dec., 2019) who were under and over 20 years of age in Tripoli city was used in this study. Results: Data shows that acne patients do not have enough information about the drug. They take their information from other patients and do not usually consult doctors or pharmacists. The only side effects that the patients complained from are dryness. In addition, they are not aware that this drug can cause teratogenic effect. Conclusion: A good understanding and awareness the Libyan patients about isotretinoin help in increasing compliance and minimize adverse effects. 

Energy savings based upon use of smart WiFi thermostats ranging from 10 to 15% have been documented, as new features such as geofencing have been added. Here, a new benefit of smart WiFi thermostats is identified and investigated; namely, as a tool to improve the estimation accuracy of residential energy consumption and, as a result, estimation of energy savings from energy system upgrades, when only monthly energy consumption is metered. This is made possible from the higher sampling frequency of smart WiFi thermostats. In this study, collected smart WiFi data are combined with outdoor temperature data and known residential geometrical and energy characteristics. Most importantly, unique power spectra are developed for over 100 individual residences from the measured thermostat indoor temperature in each and used as a predictor in the training of a singular machine learning models to predict consumption in any residence. The best model yielded a percentage mean absolute error (MAE) for monthly gas consumption ±8.6%. Applied to two residences to which attic insulation was added, the resolvable energy savings percentage is shown to be approximately 5% for any residence, representing an improvement in the ASHRAE recommended approach for estimating savings from whole-building energy consumption that is deemed incapable at best of resolving savings less than 10% of total consumption. The approach posited thus offers value to utility-wide energy savings measurement and verification.

Predicting cooling load is essential for many applications such as diagnosing the health of existing chillers, providing better control functionality, and minimizing peak loads. In this study, short-term chiller and total building demand are acquired for five different commercial buildings in the Midwest USA. Four different machine learning models are then used to predict the chiller demand using the total building demand, outdoor weather data, and day/time information. Two data collection scenarios are considered. The first relies upon use of multiple weeks of data collection that includes very warm periods and season transitional periods where the outdoor temperature ranged from very warm to cool conditions in order to envelope all cooling season weather conditions. The second scenario employs use of contiguous data for a several weeks during only the warmest period of the year. The results show that using two or more separate time periods to envelope most of the weather data yields a much more accurate model in comparison to use of data for only one time period. These research findings have importance to energy service companies which often do short term audits (measurements) in order to estimate potential savings from chiller system upgrades (controls or otherwise).