Small amount of large surface area graphene (G) is expected to significantly alter functional properties of polymers. The property enhancement is a function of degree of exfoliation and dispersion of ...G as well as its compatibility with base polymer. However, nonpolar nature of polyolefins such as polypropylene (PP) restricts homogeneous dispersion of G, leading to significant agglomeration and properties reduction. In this work, two compatibilizers, poly (ethylene‐co‐butyl acrylate) (EBA) (new compatibilizer) and PP‐grafted‐maleic anhydride (MA‐PP) (conventional compatibilizer) were compared to enhance the dispersion efficacy of G in PP. The EBA‐compatibilized nanocomposites exhibited 44% increase in the Young's modulus compared to 32% increment in MA‐PP‐compatibilized nanocomposites. Higher elongation at break for EBA‐compatibilized nanocomposites is attributed to lower degree of crystallinity in these nanocomposites. On the other hand, EBA‐compatibilized nanocomposites showed significantly improved thermal stability compared to MA‐PP‐compatibilized nanocomposites. The results indicate that EBA may act as a potential compatibilizer for G/PP nanocomposites.
This paper investigates an optimal methodology for mitigating low-frequency oscillation concerns in power systems. The study explores the synergistic integration of a power system stabilizer (PSS) ...and a flexible alternating current transmission system (FACTS) to formulate an intelligent controller. A comprehensive analysis encompasses various PSS design strategies, including lead-lag (LL), proportional-derivative-integral (PID), and fractional-order proportional-integral-derivative (FOPID) controllers. The FACTS device selected for this investigation is a static VAR compensator (SVC), highlighting the exceptional efficacy of FOPID-based PSS over alternative strategies with a power oscillation damper. The study extends its scope to encompass a comparative assessment of two distinct optimization algorithms: the moth flame optimization (MFO) and the antlion optimization (ALO). The research is conducted using a single-machine infinite bus power system (SMIB) as the case study platform. A total of four diverse test scenarios are executed under varying operating conditions. The evaluation of the developed method employs six distinct performance indices to investigate the developed controller thoroughly. The outcomes reveal that the MFO-optimized FOPID-PSS and SVC controller outperforms other control schemes. This optimized configuration demonstrates substantial improvements across all performance indices. These findings underscore the superior capabilities of the proposed approach in enhancing power system stability and performance.
Corrosion of iron in sodium chloride (3.5% wt) solutions and its inhibition by ethanedihydrazide (EH) have been reported. Electrochemical impedance spectroscopy (EIS), cyclic potentiodynamic ...polarization (CPP), and change of current with time at −475 mV (Ag/AgCl) measurements were employed in this study. Scanning electron microscopy (SEM) and energy-dispersive X-ray (EDX) techniques were utilized to report surface morphology and elemental analysis, respectively. The presence of 5 × 10–5 M EH was found to inhibit the corrosion of iron, and the effect of inhibition profoundly increased with an increase in EH concentration up to 1 × 10–4 M and further to 5 × 10–4 M. The low values of corrosion current and high corrosion resistance, which were obtained from the EIS, CPP, and change of current with time experiments, affirmed the adequacy of EH as a corrosion inhibitor for iron. Surface investigations demonstrated that the chloride solution without EH molecules causes severe corrosion, while the coexistence of EH within the chloride solution greatly minimizes the acuteness of chloride, particularly pitting corrosion.
This paper investigates the trajectory tracking control of an autonomous tracked vehicle. First, the desired linear and angular velocities are evaluated based on vehicle’s kinematics. An optimized ...backstepping controller is proposed as the kinematic controller, whereas the controller gains are optimally obtained. Next, an integral sliding mode control (SMC) is exploited based on vehicle dynamics and slipping characteristics, to obtain the desired torques that drive the vehicle and converge its trajectory to the desired one. Moreover, stability analysis of the whole system is proven based on Lyapunov theory. Finally, simulations and real-time experiments based on robot operating system (ROS) implementation are conducted to validate the effectiveness of the proposed control algorithm and compared with a hybrid backstepping-modified PID dynamic controller.
The first COVID-19 cases in Qatar were reported on 29 February 2020. As the epidemic progresses, essential epidemiological information is needed to facilitate monitoring of COVID-19 in the population ...and plan the pandemic response in Qatar.
The primary aim of this cross-sectional study is to estimate the point prevalence of COVID-19 in Qatar's primary care registered population.
A cross-sectional study design will be utilised. One publicly funded health centre from each of three geographical regions in Qatar will be identified as a study location and set up to facilitate a drive-through for the study.
Primary Health Care Corporation (PHCC) is publicly funded and the largest primary care provider in Qatar. The study will include randomly selected individuals from the full list of PHCC's registered population on its electronic medical records system. The sample selection will be done using a proportional to size sampling technique stratified by age, sex, and nationality representative of the overall PHCC-registered population. Considering the total population registered in PHCC, a sample of 2080 is proposed. A questionnaire will be administered to collect sociodemographic information, and nasal and throat swab samples will be taken. Data will be analysed to report overall symptomatic and asymptomatic point prevalence of COVID-19.
This study, with the help of a randomly selected representative sample from Qatar's primary care registered population, will provide results that can be applied to the entire population. This study design will closely represent a real-world scenario of the outbreak and is likely to provide important data to guide COVID-19 pandemic planning and response in Qatar.
This paper presents an online path planning approach for an autonomous tracked vehicle in a cluttered environment based on teaching–learning-based optimization (TLBO), considering the path ...smoothness, and the potential collision with the surrounding obstacles. In order to plan an efficient path that allows the vehicle to be autonomously navigated in cluttered environments, the path planning problem is solved as a multi-objective optimization problem. First, the vehicle perception is fully achieved by means of inertial measurement unit (IMU), wheels odometry, and light detection and ranging (LiDAR). In order to compensate the sensors drift to achieve more reliable data and improve the localization estimation and corrections, data fusion between the outputs of wheels odometry, LiDAR, and IMU is made through extended Kalman filter (EKF). Then, TLBO is proposed and applied to determine the optimum online path, where the objectives are to find the shortest path to reach the target destination, and to maximize the path smoothness, while avoiding the surrounding obstacles, and taking into account the vehicle dynamic and algebraic constraints. To check the performance of the proposed TLBO algorithm, it is compared in simulation to genetic algorithm (GA), particle swarm optimization (PSO), and a hybrid GA–PSO algorithm. Finally, real-time experiments based on robot operating system (ROS) implementation are conducted to validate the effectiveness of the proposed path planning algorithm.
Automatic detection of small objects such as vehicles in satellite images is a very challenging task, due to the complexity of the background, vehicles colors, the large size of ground sample ...distance (GSD) for satellite images and jamming caused by buildings and trees. Many methods were proposed for this task by using handcrafted features (such as a Histogram of an Oriented Gradient, Local Binary Pattern, Scale-Invariant Feature Transform, etc.) along with support vector machine classifier, however, Convolutional Neural Networks (CNN) have proved to be potentially more effective. In this paper, we use two advanced deep learning frameworks, Faster Region CNN (Faster R-CNN) and Single Shot Multi-Box (SSD) based on (CNN) with Inception-V2 as a feature map generator instead of VGG-16, to detect vehicles through Transfer Learning, and making an experimental analysis comparison between the two models. Experimental results on the test dataset demonstrate the effectiveness and efficiency of the proposed methods.
Automated object detection in high-resolution remote sensing satellite images(HRRSSI) is a proper solution for this task rather than manual detection using professional specialists. However, it is ...more complex due to the varying size, type, orientation, and complex background of the objects to detect. Utilizing artificial intelligence using deep learning is the state of the art technique to achieve this task. The number of labeled satellite images is limited for training a deep neural network therefore; transfer learning techniques were adopted for this task. This paper proposes a framework for airplane detection based on Convolution Neural network (CNN). Faster Region Based CNN (Faster R-CNN) framework is used to perform automatic airplane detection through transfer learning. Inception v2 is added to the network for feature extraction to enhance detection accuracy. The problem of information reduction of the objects due to the resizing of large size satellite image in test phase has been solved by adding a split layer before the input layer, together with a mosaic layer after detection output layer. Dataset is used to build and test the model is collected from Google Earth. Experimental results prove that the proposed developed model is extremely accurate for satellite images object detection.
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