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This dataset is utilized to conduct a comparative analysis of various deep learning methods and techniques for global horizontal irradiance forecasting in three cities with different climates in the US. The cities are Rochester, Seattle, and Tucson. The data is for the year 2021.

The importance of the sustainability concept has gained increasing attention from scholars and practitioners. In this context, the aim of this study was to examine the determinants of intention to continue to use the new Metro in Qatar, which is considered a sustainable mode of travel. Therefore, in this paper, a special attention was paid to certain variables, such as environmental concern, perceived quality, and perceived benefits. A quantitative method approach was employed, in which data were collected based on self-administered questionnaires. A total of 1334 Qatar residents responded to the survey questionnaire, and Structural Equation Modeling (SEM) was used for hypothesis testing. The results confirmed the direct effects of perceived behavioral control, social influence, and attitudes on the intention of residents to continue to use the Metro network in their travel. Moreover, perceived behavioral control was found to play a mediating role, enhancing the indirect relationship between perceived benefits and intention to continue to use the Metro network. More importantly, environmental concerns significantly and positively affected the attitudes and perceived behavioral control of travelers; furthermore, these variables presented a mediated influence—through service quality—on the intention to continue to use the Metro network. This study enriched the sustainability behavior literature by examining the influences of crucial factors. Thus, this study is considered of great value for policy-makers who hope to maximize the awareness level of environmental sustainability among the population, in order to encourage them to embrace changes in their lifestyle towards becoming more environmentally responsible.

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.

Cylindrical graphite microbial fuel cell (MFC) configuration designed by eliminating distinct casing and membrane was evaluated for bioelectrogenesis and treatment of real-field wastewaters. Both petroleum refinery wastewater (PRW) and Labanah whey wastewater (LW) were used as substrates, and investigated for electricity generation and organic removal under batch mode operation. PRW showed higher bioelectricity generation (current and power generation of 3.35mA and 1.12mW at 100Ω) compared to LW (3.2mA and 1.02mW). On the contrary, higher substrate degradation efficiency was achieved using LW (72.76%) compared to PRW (45.06%). Superior function of MFC operation in terms of volumetric power density (PRW, 28.27W/m3; LW, 23.23W/m3) suggesting the feasibility of using these wastewaters for bioelectricity generation. Large sources of wastewater that generating in the Middle-East countries have potential to produce renewable energy from the treatment, which helps for the sustainable wastewater management and simultaneous renewable energy production.

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 thesis reports on the development and testing of a form of participatory budgeting in which citizens are asked to choose from a set of local authority interventions whilst having to comply with two constraints – one financial and the other relating to greenhouse gas emissions. The project has its roots in the weak performance to date of the local government sector in responding to climate change, despite its considerable influence. It is also informed by the troubled relationship between local authorities and citizens. Participatory budgeting is selected as the starting point because it has been found to draw a larger and more diverse audience than more orthodox forms of citizen participation and because it can present participants with a requirement to trade off priorities. The core of the thesis describes the design and development of “participatory emissions budgeting”, a central aspect being the estimation of emissions attributable to local authority interventions. This culminates in formal trials of the method with citizens, followed by quantitative and qualitative evaluation. The method is then presented to a range of local authority stakeholders to gauge their views concerning its potential application. Participatory emissions budgeting is found to be technically feasible: participants consistently arrive, through deliberation, at choice sets that comply with the constraints set. Whilst they report finding the experience interesting and enjoyable, they are critical of the imposition of an emission constraint, in the context of general scepticism concerning the value or legitimacy of tackling climate change through such a decision-making process. Local authority stakeholders see some value in the method but would not wish to apply it as designed – to decide on the allocation of resources. They would rather use it to support decision making within their organisations, as a market-research or educational tool.