258 research outputs found
Facile Access to Amides from Oxygenated or Unsaturated Organic Compounds by Metal Oxide Nanocatalysts Derived from Single-Source Molecular Precursors
No full textNanocatalysts (Co/CoO, NiO, and CuO) have been explored for amide synthesis using oxygenates or unsaturated organic compounds. The highly efficient oxidative amidation of alcohols, aldehydes, carboxylic acids, and alkynes has been achieved by using N,N-dimethylformamide (DMF) as the solvent and as an amine source
Hydraulic simulations to evaluate and predict design and operation of the Chashma Right Bank Canal
No full textIrrigation systems / Irrigation canals / Flow control / Velocity / Canal regulation techniques / Hydraulics / Simulation models / Design / Operations / Crop-based irrigation / Distributary canals / Water delivery / Policy / Protective irrigation / Water allocation / Water requirements / Sedimentation / Water distribution / Equity / Water conveyance / Pakistan / Chashma Right Bank Canal
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Basic Lines of Revolutionary Duties of Government of Democratic Republic of Afghanistan
No full textThis item is the statement of Noor Mohammad Taraki, Chairman of the Revolutionary Council and Prime Minister, as broadcast on Radio Afghanistan on May 10th, 1978.This material from the personal archives of Professor M. Mobin Shorish is made available by the University of Arizona Libraries as part of the Afghanistan Digital Collections. Email [email protected] with your questions about this collection
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Noor Mohammad Taraki Announcement of 1979
No full textThis material from the personal archives of Professor M. Mobin Shorish is made available by the University of Arizona Libraries as part of the Afghanistan Digital Collections. Email [email protected] with your questions about this collection
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Government Policy of Mohammad Musa Shafiq
No full textThis material from the personal archives of Professor M. Mobin Shorish is made available by the University of Arizona Libraries as part of the Afghanistan Digital Collections. Email [email protected] with your questions about this collection
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Kholase Akhbar Vatan e Aziz: Bayanyeh Shagheli Mohammad
No full textThis material from the personal archives of Professor M. Mobin Shorish is made available by the University of Arizona Libraries as part of the Afghanistan Digital Collections. Email [email protected] with your questions about this collection
Corrosion: Basics, Adverse Effects and Its Mitigation
No full textCorrosion is a worldwide problem affecting the industrial economy and safety, causing excessive losses from electronics to infrastructure, energy, and healthcare. Corrosion is the material deterioration into its constituent atoms due to a complex electrochemical process with the rusting environment. This degradation process, which usually occurs over a metal surface, is commonly known as rusting and depends mainly on the environment as well as the nature of the affected material. Naturally, metal elements are affected by atmospheric environments (rural, urban, industrial, or marine places), climatic factors (humidity, pH, and temperature), or pollutants compounds (CO2, SO2, artificial fertilizers, etc.) generated by anthropogenic activity. Corrosion inhibition is controlled by underlying thermodynamic and kinetic factors. Applied technologies comprise metallurgical approaches such as substrate chemical modification or specialized protective layers. Plating, painting, and applying enamel are the most common anticorrosive methodologies that provide a physical barrier between the metallic substrate and the damaging surroundings. However, new regulations on chemicals and coating manufacturing are increasing consumer environmental awareness. As a result, demand for eco-friendly products drives the protective coating industry towards circularity and sustainable development.Fil: Lobo, Rene Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Tucuman. Facultad de Bioquímica, Química y Farmacia. Instituto de Química Analitica; ArgentinaFil: Guzmán, Bautista. Universidad Nacional de Tucuman. Facultad de Bioquímica, Química y Farmacia. Instituto de Química Analitica; ArgentinaFil: Orrillo, Patricio Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Química del Noroeste. Universidad Nacional de Tucumán. Facultad de Bioquímica, Química y Farmacia. Instituto de Química del Noroeste; Argentina. Universidad Nacional de Tucumán. Facultad de Bioquímica, Química y Farmacia. Instituto de Química Física; ArgentinaFil: Dominguez, Cecilia Carolina. Universidad Nacional de Tucuman. Facultad de Bioquímica, Química y Farmacia. Instituto de Química Analitica; ArgentinaFil: Jimenez, Luis Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Química del Noroeste. Universidad Nacional de Tucumán. Facultad de Bioquímica, Química y Farmacia. Instituto de Química del Noroeste; Argentina. Universidad Nacional de Tucumán. Facultad de Bioquímica, Química y Farmacia. Instituto de Química Física; ArgentinaFil: Torino, Maria Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; Argentin
Multi-Modal Traffic Signal Control using Deep Reinforcement Learning
No full text© 2024 Mohammad Mobin YazdaniThe urban population growth has made traffic congestion a huge concern in large cities. To tackle this issue, transport authorities have heavily invested in the expansion of road infrastructure and the development of existing transportation systems. The latter is of great interest since it offers cost-effective and time-efficient benefits over the former. Traffic signal control systems are one of these solutions that play a pivotal role in the management of traffic congestion. Popular systems such as Sydney Coordinated Adaptive Traffic Systems (SCATS) and Split Cycle and Offset Optimisation Technique (SCOOT) have shown their effectiveness in hundreds of cities across the globe. Nevertheless, the logic behind the existing traffic signal optimization methods is mainly heuristic or rule-based. Moreover, the existing traffic sensors (e.g., loop detectors) have been around for more than five decades, limiting the capability of these adaptive traffic signal control systems to improve further.
With recent advancements in Artificial Intelligence (AI), there is an unprecedented growth of its applications in diverse fields. Intelligent Transport Systems (ITS) have also enabled the observation of stochastic traffic environments using advanced sensor technologies. AI in ITS has created opportunities for future mobility solutions, especially smart traffic signals. In traffic signal control literature, deep Reinforcement Learning (RL) has been extensively studied which takes advantage of Deep Neural Networks (DNN) to extract complex features. Deep RL interacts with the traffic environment and gains experience to learn the optimal signal timings. Despite showing promising results compared to the conventional methods, the conducted studies have not yet examined the multi-modality of traffic flow and, their challenges when implemented in real intersections. For example, the existing literature has only focused on optimizing vehicles' traffic signals. A multi-modal traffic flow includes vulnerable road users (i.e., pedestrians and cyclists) and public transport (e.g., buses, trams) which needs to be taken into account when adjusting the signals.
This thesis contributes to the literature in three areas: 1) we propose a novel deep RL-based traffic signal model to control the vehicles and pedestrian flows in their real setting. For traffic states, the pedestrian volumes are fed to the model as well as vehicle traffic. To fairly distribute the green times, an extended reward function is developed that captures the residual delays due to the real interaction between vehicles and pedestrians. Also, the data at the cross-box area of the intersection is included in the reward function which considers the turning movement delays ignored in signal optimization. The experimental results show the superiority of our model compared to baselines in terms of total user delays. 2) we develop a deep RL-based signal priority method that controls trams and vehicles in a multi-modal traffic environment. Instead of typical Transit Signal Priority (TSP) strategies that heavily prioritize trams over vehicles, we grant a fair priority that benefits both modes. The reward function includes the number of passengers on board the tram and penalizes the tram bunching because of headway deviations. The results indicate a significant improvement in signal priority with minimal impact on the side street traffic, promoting average user speed. 3) We conduct comprehensive experiments to evaluate the performance of deep RL models when using video camera data with limited detection ranges. The methodologies tested in the literature assumed a full availability of traffic data along the road, a scenario that is often unrealistic. We show how effective the deep RL traffic signals can perform compared to actual traffic signals, hence, justifying the implementation of deep RL in the real world.
In summary, this thesis develops deep RL-based multi-modal adaptive traffic signal control methods with the consideration of real-world challenges. The experiments are conducted in a simulation environment which are calibrated based on actual signal phasing structures and parameters and, the data collected from the intersection. Then, the proposed methodologies are tested and evaluated over learning-based models and existing gap-based signals with advanced logic. We hope that this thesis is helpful for the next studies in the literature and the implementation of deep RL methods in real intersections
Strong interfacial polarization in graphene/ZnO nanocomposite for high-performance miniscule permittivity materials
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