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One of the most interesting new approaches in the transportation research field is the Naturalistic Driver Behavior which is intended to provide insight into driver behavior during everyday trips by recording details about the driver, the vehicle and the surroundings through an unobtrusive data gathering equipment and without experimental control. In this paper, an Internet of Things solution that collects and analyzes data based on Naturalistic Driver Behavior approach is proposed. The analyzed and collected data will be used as a comprehensive review, and analysis of the existing Qatar traffic system, including traffic data infrastructure, safety planning, engineering practices and standards. Moreover, data analytics for crash prediction and the use of these predictions for the purpose of systemic and systematic network hotspot analysis, risk-based characterization of roadways, intersections, and roundabouts are developed. Finally, an integrated safety risk solution was proposed. This latter, enables decision makers and stakeholders (road users, state agencies, and law enforcement) to identify both high-risk locations and behaviors by measuring a set of dynamic variables including event-based data, roadway conditions, and driving maneuvers. More specifically, the solution consists of a driver behaviors detector system that uses mobile technologies. The system can detect and analyze several behaviors like drowsiness and yawning. Previous works are based on detecting and extracting facial landmarks from images. However, the new suggested system is based on a hybrid approach to detect driver behavior utilizing a deep learning technique using a multilayer perception classifier. In addition, this solution can also collect data about every day trips like start time, end time, average speed, maximum speed, distance and minimum speed. Furthermore, it detects for every fifteen seconds measurements like GPS position, distance, acceleration and rotational velocity along the Roll, Pitch and Yaw axes. The main advantage of the solution is to reduce safety risks on the roads while optimizing safety mitigation costs to a society. The proposed solution has three-layer architecture, namely, the perception, network, and application layers as detailed below. I. The perception layer is the physical layer, composed from several Internet of Thing devices that uses mainly use the smart phones equipped with cameras and sensors (Magnetometer, Accelerometers Gyroscope and Thermometer, GPS sensor and Orientation sensor) for sensing and gathering information about the driver behavior roads and environment as shown in Fig. 1. II. The network layer is responsible for establishing the connection with the servers. Its features are also used for transmitting and processing sensor data. In this solution, hybrid system that collect data and store them locally before sending them to the server is used. This technique proves its efficiency in case of Poor Internet coverage and unstable Internet connection. III. The application layer is responsible for delivering application specific services to end user. It consists in sending the data collected to web server in order to be treat and analyzed before displaying it to the final end user. The web service which part of the application layer is the component responsible for collecting data not only from devices but also from other sources such General Traffic Directorate at Minister of Interior to gather the crash details. This web service stocks all stored data in database server and analyses them. Then, the stored data and analysis will be available for end user via website that has direct access to the web services. Figure 1: Architecture of Car monitoring system Keywords: Driver Monitoring System, DrowsinessDetection, Deep Learning, Real-time Deep Neural Network, Fig. 1: Architecture of IoT solution Keywords: Driver Monitoring System, Drowsiness Detection, Deep Learning, Real-time Deep Neural Network,

With the fast growth of the global economy, energy supply and demand have a strong impact on social, economic, and environmental aspects. As a consequence, this has pushed the decision-makers to formulate objectives, guiding economic policies toward sustainable goals. The process is known as Sustainable Development Goals (SDGs) that have been proposed by the United Nations. This being said, the energy sector is a vital domain with a vast potential for improvments in terms of technologies and ligistalations. Solar energy is among the most efficient solutions proposed to reduce the economic and environmental footprints of energy. In this frame, the current paper aims to localize solar energy within SDGs and analyze the contribution of the solar energy towards the achievement of the SDGs. Moreover, the current work highlights the contributions of Mohammed bin Rashid Al Maktoum (MBR) Solar Park in the United Arab Emirates to achieving the SDGs. Indeed, the MBR Solar Park concept offers valuable insights of environmental impacts by deploying clean and affordable energy sources in place of conventional fossil fuel power plants that are still heavily used in the region. The MBR Solar Park operation has already mitigated 6.5 million tonnes of carbon dioxide equivalent and this number will likely rise when all phases are installed and operational. Moreover, it has been shown that MBR Solar Park achieve several SDGs such SDG 8: decent work and economic growth, SDG 9: industry, innovation and infrastructure, SDG 11: sustainable cities and communities, and SDG 15: life on land.

This article seeks to examine accountants' perceptions of carbon taxes in addressing climate change. Specifically, it aims at (i) a better understanding of accountants opinions on carbon taxes; (ii) how energy companies have adapted their business operations since introducing carbon tax. The article's primary sources of data are (1) a 2018 survey distributed to 45 accounting professionals in Scotland, which specialise in energy finance and a formal interview with a regional finance director of a multinational energy company. The results show how the accountants are in favour the carbon tax due to its positive environmental impact, however, do not agree with the associated rising utility costs. Though, carbon tax, from an energy business' perspective, is viewed negatively due to its effects on the end users of energy. This study contributes to the current research by demonstrating the role of accounting society in boosting public awareness of climate change. The findings of this study will help regulators and policy makers in the UK to evaluate the adequacy of current carbon tax reforms and to promote the public awareness of climate change to reduce carbon emissions.

Abstract Four different anionic surfactants, sodium dodecyl sulfate, dodecyl benzene sulfonic acid (DBSA), dodecyl phosphate and Sodium lauroyl sarcosine, selected from the sulfate, phosphate, and carboxylic family, were investigated as additives in silica dispersions for selective polishing of silicon dioxide over silicon nitride films. We found that all these anionic surfactants suppress the nitride removal rates (RR) for pH ≤4 while more or less maintaining the oxide RRs, resulting in high oxide-to-nitride RR selectivity. The RR data obtained as a function of pH were explained based on pH dependent distributions of surfactant species, change in the zeta potentials of oxide and nitride surfaces, and thermogravimetric data. It appears that the negatively charged surfactant species preferentially adsorb on the positively charged nitride surface below IEP through its electrostatic interactions and form a bilayer adsorption, resulting in the suppression of nitride RRs. In contrast to the surfactants, K2SO4 interacts only weakly with the nitride surface and hence cannot suppress its RR.

This publication was made possible by the NPRP grant (NPRP8-370-2-154) from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of author(s).

Abstract This article examines and conceptualizes the implications of the adoption of circular economy (CE) business models on operations management (OM) decision-making processes, in the areas of product design, production planning and control, and logistics/supply chains. A systematic literature review was conducted in order to identify and examine the following key areas: (1) the new demands faced by OM decision-making regarding changes in capability, work procedures, intra- and inter-organizational relationships and technologies; (2) the specific changes OM decision-making must make in order to support CE business models (based on the ReSOLVE framework); and (3) guidelines which will help designers and operations and logistics/supply chain managers develop the necessary skills to meet society and the global market's emerging demands. The findings of this research will allow operations managers to foresee the unfolding needs for capacity building in CE, and scholars can build on the results of this article to develop new research themes. Furthermore, this is the first article to describe the ways in which OM knowledge can support the transition towards the circular economy based on the perspective of dynamic capabilities.

In hot climatic conditions, the temperature inside a greenhouse is much higher than the ambient temperature and requires significant cooling to maintain the temperature in the optimal range. This study proposes a novel concept for a self‐sustaining greenhouse, which integrates evacuated tube collectors, a multistage flash desalination system, vapor absorption cooling system, and photovoltaic thermal (PV/T) system to produce freshwater, cooling, and electricity. The PV/T system uses spectrum selective nanofluid that is circulated over the roof of the greenhouse and absorbs solar radiation having wavelengths greater than 1400 nm to reduce cooling load inside the greenhouse and optimize the spectrum use. The performance of the proposed system is evaluated using energy and exergy analysis. The energy efficiencies of the evacuated tube collector, desalination unit, and PV/T system are obtained as 45.85%, 46.16%, and 51.25%, respectively. The absorption cooling system achieves an energetic coefficient of performance of 0.83. The overall energy and exergy efficiencies of the proposed system are found to be 30% and 5.88%, respectively. The application of spectrum selective nanofluid on the roof of the greenhouse yields about 26% reduction in the cooling load of the greenhouse.

AbstractStudies have shown that occupant satisfaction is a compelling factor affecting building energy consumption. Occupants' behavior has a direct relationship to their satisfaction, and it is cl...