Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models

The state of Kuwait is facing a substantial challenge in responding to the spread of the novel coronavirus 2019 (COVID-19). The government’s decision to repatriate stranded citizens back to Kuwait from various COVID-19 epicenters has generated a great concern. It has heightened the need for prediction models to estimate the epidemic size. Mathematical modeling plays a pivotal role in predicting the spread of infectious diseases to enable policymakers to implement various health and safety measures to contain the spread. This research presents a forecast of the COVID-19 epidemic size in Kuwait based on the confirmed data. Deterministic and stochastic modeling approaches were used to estimate the size of COVID-19 spread in Kuwait and determine its ending phase. In addition, various simulation scenarios were conducted to demonstrate the effectiveness of nonpharmaceutical intervention measures, particularly with time-varying infection rates and individual contact numbers. Results indicate that, with data until 19 April 2020 and before the repatriation plan, the estimated reproduction number in Kuwait is 2.2. It also confirms the efficiency of the containment measures of the state of Kuwait to control the spread even after the repatriation plan. The results show that a high contact rate among the population implies that the epidemic peak value is yet to be reached and that more strict intervention measures must be incorporate

Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait

This study presents the first accuracy assessment of a low cost small unmanned aerial vehicle (sUAV) in reconstructing three dimensional (3D) models of traffic accidents at extreme operating environments. To date, previous studies have focused on the feasibility of adopting sUAVs in traffic accidents photogrammetry applications as well as the accuracy at normal operating conditions. In this study, 3D models of simulated accident scenes were reconstructed using a low-cost sUAV and cloud-based photogrammetry platform. Several experiments were carried out to evaluate the measurements accuracy at different flight altitudes during high temperature, low light, scattered rain and dusty high wind environments. Quantitative analyses are presented to highlight the precision range of the reconstructed traffic accident 3D model. Reported results range from highly accurate to fairly accurate represented by the root mean squared error (RMSE) range between 0.97 and 4.66 and a mean percentage absolute error (MAPE) between 1.03% and 20.2% at normal and extreme operating conditions, respectively. The findings offer an insight into the robustness and generalizability of UAV-based photogrammetry method for traffic accidents at extreme environments

A New Model for Estimation of Bubble Point Pressure Using a Bayesian Optimized Least Square Gradient Boosting Ensemble

Accurate estimation of crude oil Bubble Point Pressure (Pb) plays a vital rule in the development cycle of an oil field. Bubble point pressure is required in many petroleum engineering calculations such as reserves estimation, material balance, reservoir simulation, production equipment design, and optimization of well performance. Additionally, bubble point pressure is a key input parameter in most oil property correlations. Thus, an error in a bubble point pressure estimate will definitely propagate additional error in the prediction of other oil properties. Accordingly, many bubble point pressure correlations have been developed in the literature. However, they often lack accuracy, especially when applied for global crude oil data, due to the fact that they are either developed using a limited range of independent variables or developed for a specific geographic location (i.e., specific crude oil composition). This research presents a utilization of the state-of-the-art Bayesian optimized Least Square Gradient Boosting Ensemble (LS-Boost) to predict bubble pointpressure as a function of readily available field data. The proposed model was trained on a global crude oil database which contains (4800) experimentally measured, Pressure–Volume–Temperature (PVT) data sets of a diverse collection of crude oil mixtures from different oil fields in the NorthSea, Africa, Asia, Middle East, and South and North America. Furthermore, an independent (775) PVT data set, which was collected from open literature, was used to investigate the effectiveness of the proposed model to predict the bubble point pressure from data that were not used during the model development process. The accuracy of the proposed model was compared to several published correlations (13 in total for both parametric and non-parametric models) as well as two other machine learning techniques, Multi-Layer Perceptron Neural Networks (MPL-ANN) and Support Vector Machines (SVM). The proposed LS-Boost model showed superior performance andremarkably outperformed all bubble point pressure models considered in this study

Bridging Nanowires for Enhanced Gas Sensing Properties

It is crucial to develop new bottom-up fabrication methods with control over the physical properties of the active materials to produce high-performance devices. This article reports well-controlled, without seed layer and site-selective hydrothermal method to produce ZnO bridging nanowires sensors. By controlling the growth environment, the performance of the sensor became more efficient. The presented on-chip bridging nanowire sensor enhanced sensitivity toward acetone gas (200 ppm) around 63 and fast response time (420 ms) and recovery time (900 ms). The enhancement in the speed of response and recovery is ascribed to the exceptional NW-NW junction barrier that governs the sensor’s conductivity, and the excellent contact between ZnO nanowires and Au electrodes

A New Model for Estimation of Bubble Point Pressure Using a Bayesian Optimized Least Square Gradient Boosting Ensemble

Accurate estimation of crude oil Bubble Point Pressure (Pb) plays a vital rule in the development cycle of an oil field. Bubble point pressure is required in many petroleum engineering calculations such as reserves estimation, material balance, reservoir simulation, production equipment design, and optimization of well performance. Additionally, bubble point pressure is a key input parameter in most oil property correlations. Thus, an error in a bubble point pressure estimate will definitely propagate additional error in the prediction of other oil properties. Accordingly, many bubble point pressure correlations have been developed in the literature. However, they often lack accuracy, especially when applied for global crude oil data, due to the fact that they are either developed using a limited range of independent variables or developed for a specific geographic location (i.e., specific crude oil composition). This research presents a utilization of the state-of-the-art Bayesian optimized Least Square Gradient Boosting Ensemble (LS-Boost) to predict bubble pointpressure as a function of readily available field data. The proposed model was trained on a global crude oil database which contains (4800) experimentally measured, Pressure–Volume–Temperature (PVT) data sets of a diverse collection of crude oil mixtures from different oil fields in the NorthSea, Africa, Asia, Middle East, and South and North America. Furthermore, an independent (775) PVT data set, which was collected from open literature, was used to investigate the effectiveness of the proposed model to predict the bubble point pressure from data that were not used during the model development process. The accuracy of the proposed model was compared to several published correlations (13 in total for both parametric and non-parametric models) as well as two other machine learning techniques, Multi-Layer Perceptron Neural Networks (MPL-ANN) and Support Vector Machines (SVM). The proposed LS-Boost model showed superior performance andremarkably outperformed all bubble point pressure models considered in this study.</jats:p

Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process. In this study, a cutting force prediction model for turning AISI 4340 alloy steel was developed using Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN) methods. The GPR simulations demonstrated a reliable prediction of surface roughness for the dry turning method with R2 = 0.9843, MAPE = 5.12%, and RMSE = 1.86%. Performance comparisons between GPR, SVM, and ANN show that GPR is an effective method that can ensure high predictive accuracy of the cutting force in the turning of AISI 4340.</jats:p

ZnO nanoleaves with superior photodetection properties

Controlled multiphase hydrothermal synthesis technique was developed to design and grow hierarchical zinc oxide (ZnO) nanostructures with high surface-to-volume ratio. ZnO nanoleaves (ZNLs) and nanoflakes (ZNFs) assembled from initial monomorphological nanostructures, ZnO nanowires (ZNWs), and ZnO nanodiscs (ZNDs), respectively. These hierarchical nanostructures with 2D nanosheets building blocks were obtained by sequential nucleation and growth following a hydrothermal process. Zinc sulphate was the source of zinc ions in the second growth phase. In comparison to their monomorphological counterparts, the hierarchically designed ZnO nanostructures demonstrated superior ultraviolet detection properties, an improved photosensitivity (∼105), and fast response-time (5 s) and fast recovery-time (1 s). The enhancement in photosensitivity of the ZNLs photodetector was ascribed to the reduced dimensions and increased surface-to-volume ratio. This work is part of the efforts leading the way toward low cost, large scale, and low temperature fabrication of high performance nanostructured ZnO PDs on flexible and transparent substrates

Least Squares Boosting Ensemble and Quantum-Behaved Particle Swarm Optimization for Predicting the Surface Roughness in Face Milling Process of Aluminum Material

Surface roughness is a significant factor in determining the product quality and highly impacts the production price. The ability to predict the surface roughness before production would save the time and resources of the process. This research investigated the performance of state-of-the-art machine learning and quantum behaved evolutionary computation methods in predicting the surface roughness of aluminum material in a face-milling machine. Quantum-behaved particle swarm optimization (QPSO) and least squares gradient boosting ensemble (LSBoost) were utilized to simulate numerous face milling experiments and have predicted the surface roughness values with high extent of accuracy. The algorithms have shown a superior prediction performance over genetics optimization algorithm (GA) and the classical particle swarm optimization (PSO) in terms of statistical performance indicators. The QPSO outperformed all the simulated algorithms with a root mean square error of RMSE = 2.17% and a coefficient of determination R2 = 0.95 that closely matches the actual surface roughness experimental values

A Vision-Based Neural Network Controller for the Autonomous Landing of a Quadrotor on Moving Targets

Time constraints is the most critical factor that faces the first responders&rsquo; teams for search and rescue operations during the aftermath of natural disasters and hazardous areas. The utilization of robotic solutions to speed up search missions would help save the lives of humans who are in need of help as quickly as possible. With such a human-robot collaboration, by using autonomous robotic solutions, the first response team will be able to locate the causalities and possible victims in order to be able to drop emergency kits at their locations. This paper presents a design of vision-based neural network controller for the autonomous landing of a quadrotor on fixed and moving targets for Maritime Search and Rescue applications. The proposed controller does not require prior information about the target location and depends entirely on the vision system to estimate the target positions. Simulations of the proposed controller are presented using ROS Gazebo environment and are validated experimentally in the laboratory using a Parrot AR Drone system. The simulation and experimental results show the successful control of the quadrotor in autonomously landing on both fixed and moving landing platforms

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.</jats:p