Extension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm. Four set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible are used from start of the outbreak until 23rd June 2020 to fit with SEIR-PAD model results. Results for trends of COVID-19 in GCC countries indicate that the disease may be terminated after 200 to 300 days from start of the outbreak depends on current measures and policies. SEIR-PAD model provides a robust and strong tool to predict trends of COVID-19 for better management and/or foreseeing effects of certain enforcing laws by governments, health organizations or policy makers.

The electricity consumption in residential/office buildings corresponded to 45% of the total annual electricity demand in hot-arid climates. This accounted for 27.2 TWh of electricity consumption with 14.2 MWh/capita/year in Kuwait. In this research, four offices in an educational building were equipped with a meteorological data logging system using temperature, humidity, and illuminance sensors. All four offices had double-glazed windows. Moreover, two offices were equipped with two types of commercially available window films. Two million data were stored in iCloud using Wi-Fi and an Internet of Things (IoT) system for the 3 months of June, July, and August 2019. Here, histograms and the kernel density estimation (KDE) of temperature/humidity were analyzed and compared for the two offices with/without 3M Neutral 20 window films. Two floors of the same building consisting of 31 offices were also modeled and simulated to study energy saving and CO2 footprint reduction using various window films. The results of simulations for the month of July 2019 using SOL 101 and SOL 102 window films, respectively, showed that about 250 kg and 255 kg of production of CO2 could be reduced and energy saving counted for 416 and 422 kWh. Measurements from offices with 3M Neutral 20% and 3M Neutral 70% window films for the month of July 2019 indicated that the carbon footprint could be reduced by about 82 kg and 0.43 kg and energy saving counted for 147.11 and 0.71 kWh, respectively. It was observed that an annual energy saving and CO2 footprint reduction of 2.76% could be achieved using window films in a hot-arid climate.

Wood-plastic composites (WPCs) are becoming one of the most attractive materials in building envelopes. In addition to WPCs' architectural and design attraction, they can enhance the thermal performance of buildings by acting as insulation materials. The thermal performance of building materials requires new experimental methods that can simulate true indoor/outdoor temperatures. In this study, a simple quasi-steady heating film (QSHF) method is devised to measure the thermal conductivity of WPC samples utilizing blocks of standard materials with known thermal conductivity. QSHF device uses a 10cm×10cm×0.5mm silicon heating film controlled by a temperature regulator and several transparent acrylic square blocks of the same size with 10mm thickness as the standard materials along with various specially designed WPC samples for Kuwait. The WPC samples' top surface is considered the cold side of the system, which is open to indoor temperatures of 22 to 23 oC. The bottom layer is maintained at fixed temperatures ranging from 25 to 55 oC to simulate the outdoor temperatures of a hot subtropical desert environment like Kuwait. The thermal conductivity of several WPCs type namely FB16, FB18W, CD, and TD were obtained as 0.0912, 0.1174, 0.3453, and 0.3078 W/m.K, respectively. Experimental results for DP45-1 were not consistent at different temperatures. hence Multiphysics CFD simulation was conducted for DP45 which shows strong 2D effects. A typical building sample was also modelled in TRNSYS to compare cooling loads with and without WPC. Also, the limitations and advantages of using QSHF method are discussed.

Power consumption of wellbore drilling in oil and gas exploitations count for 40% of total costs, hence power saving of WBM (water-based mud) by adding different concentrations of Al2O3, TiO2 and SiO2 nanoparticles is investigated here. A high-speed Taylor–Couette system (TCS) was devised to operate at speeds 0–1600 RPM to simulate power consumption of wellbore drilling using nanofluids in laminar to turbulent flow conditions. The TCS control unit uses several sensors to record current, voltage and rotational speed and Arduino microprocessors to process outputs including rheological properties and power consumption. Total power consumption of the TCS was correlated with a second-order polynomial function of rotational speed for different nanofluids, and the correlated parameters were found using an optimization technique. For the first time, energy saving of three nanofluids at four low volume concentrations 0.05, 0.1, 0.5 and 1% is investigated in the TCS simulating wellbore drilling operation. It is interesting to observe that the lower concentration nanofluids (0.05%) have better power savings. In average, for the lower concentration nanofluids (0.05%), power was saved by 39%, 30% and 26% for TiO2, Al2O3 and SiO2 WBM nanofluids, respectively. TiO2 nanofluids have better power saving at lower concentrations of 0.05 and 0.1%, while Al2O3 nanofluids have saved more power at higher concentrations of 0.5 and 1.0% compared with their counterpart nanofluids.

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Susceptible-infectious-recovered-deceased (SIRD) model is an essential model for outbreak prediction. This paper evaluates the performance of the SIRD model for the outbreak of COVID-19 in Kuwait, which initiated on 24 February 2020 by five patients in Kuwait. This paper investigates the sensitivity of the SIRD model for the development of COVID-19 in Kuwait based on the duration of the progressed days of data. For Kuwait, we have fitted the SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed the sensitivity of the model with the number of days of data. The parameters of the SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of the SIRD model indicate that after 40 days, the peak infectious day can be adequately predicted. Although error percentage from sensitivity analysis suggests that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19, and more precise methods are needed to tackle the correct trends of a pandemic.

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On 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study's main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases.In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II.