1,090 research outputs found
Accommodating High Penetrations of Renewable Distributed Generation Mix in Smart Grids
No full textA Master of Science thesis in Electrical Engineering by Mohammad Tarek Khayata entitled, "Accommodating High Penetrations of Renewable Distributed Generation Mix in Smart Grids," submitted in April 2017. Thesis advisor is Dr. Mostafa Shaaban. Soft and hard copy available.This work proposes a new method for renewable distributed generation (DG) allocation in smart grid. The main objective is to minimize the overall investment which includes the capital cost of DG units, the operation and maintenance costs of DG units, and the cost of purchasing energy from the grid. The proposed approach takes into consideration the uncertainty and variability associated with generation, demand, and energy cost in addition to the communication infrastructure which is the main contribution of this work. The communication infrastructure under the smart grid paradigm will allow real-time control of the system assets. Therefore, considering this property during the planning phase enhances the system performance and optimizes the overall investment. The proposed approach relies on developing probabilistic models for each generation technology, energy prices, and demand. Then, these models are combined into one multi-state gen-load-price probabilistic model that describes all possible conditions of the system. The number of states in the final model is a tradeoff between the accuracy of results and computational time. Genetic algorithm (GA) optimization technique is utilized in this study to solve the DG planning problem. Simulation results on a typical distribution system are provided to prove the effectiveness of the proposed approach in increasing the renewable DG penetration in smart grids while maximizing the profit of the investment. Moreover, the results obtained through the use of the proposed smart operation are compared with the conventional planning methodologies to demonstrate the targeted added value. A significant cost saving of 28.3% and 254% higher percentage of DG penetration are achieved with the proposed DGs curtailment technique to mitigate technical system violations, which proves the significant advantage of adopting smart grid operation in planning problems.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Trends in Maths and Science Study (TIMSS): National Report for England
Get PDFThe Trends in International Mathematics and Science Study (TIMSS) is overseen by the International Association for the Evaluation of Educational Achievement (IEA). It provides participating countries internationally comparable data on the performance and attitudes of 9 to 10 (year 5) and 13 to 14 year-olds (year 9) in maths and science as well as comparisons of the curriculum and the teaching of these subjects in primary and secondary schools. Fifty-seven countries and seven benchmarking entities participated in TIMSS 20151. England has participated in TIMSS since the study was first carried out in 1995 and in each subsequent four-yearly cycle, meaning that 2015 represents the study’s sixth cycle. The study therefore provides valuable trends in England’s absolute and relative performance over a twenty-year period. In England, testing was conducted with pupils in years 5 and 9 in May and June 2015, with a sample of over 8,800 pupils across 290 schools. England’s year 5 cohort started school in 2009 and sat the new Key Stage 2 tests in the summer of 2016. The year 9 cohort started school in 2005 and will take the new GCSEs in summer 2017, having started secondary school in 2012. This TIMSS National Report for England focuses on comparisons of our pupils’ performance and their experiences of maths and science teaching compared to: high-performing and rapidly improving countries; other English-speaking countries; and similar countries in terms of context and geography. The TIMSS International Report 2015 offers comparisons across all participating countries
Supplemental Material, sj-docx-1-cpt-10.1177_10742484221132671 - Randomized Clinical and Biochemical Study Comparing the Effect of L-arginine and Sildenafil in Beta Thalassemia Major Children With High Tricuspid Regurgitant Jet Velocity
No full textSupplemental Material, sj-docx-1-cpt-10.1177_10742484221132671 for Randomized Clinical and Biochemical Study Comparing the Effect of L-arginine and Sildenafil in Beta Thalassemia Major Children With High Tricuspid Regurgitant Jet Velocity by Eman El-Khateeb, Sahar Mohamed El-Haggar, Osama El-Razaky, Mohamed Ramadan El-Shanshory and Tarek Mohamed Mostafa in Journal of Cardiovascular Pharmacology and Therapeutics</p
sj-docx-2-vmj-10.1177_1358863X241231942 – Supplemental material for Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment
No full textSupplemental material, sj-docx-2-vmj-10.1177_1358863X241231942 for Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment by Mengwei Zhang, Saran Lotfollahzadeh, Nagla Elzinad, Xiaosheng Yang, Murad Elsadawi, Adam C Gower, Mostafa Belghasem, Tarek Shazly, Vijaya B Kolachalama and Vipul C Chitalia in Vascular Medicine</p
sj-pdf-1-vmj-10.1177_1358863X241231942 – Supplemental material for Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment
No full textSupplemental material, sj-pdf-1-vmj-10.1177_1358863X241231942 for Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment by Mengwei Zhang, Saran Lotfollahzadeh, Nagla Elzinad, Xiaosheng Yang, Murad Elsadawi, Adam C Gower, Mostafa Belghasem, Tarek Shazly, Vijaya B Kolachalama and Vipul C Chitalia in Vascular Medicine</p
sj-docx-3-vmj-10.1177_1358863X241231942 – Supplemental material for Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment
No full textSupplemental material, sj-docx-3-vmj-10.1177_1358863X241231942 for Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment by Mengwei Zhang, Saran Lotfollahzadeh, Nagla Elzinad, Xiaosheng Yang, Murad Elsadawi, Adam C Gower, Mostafa Belghasem, Tarek Shazly, Vijaya B Kolachalama and Vipul C Chitalia in Vascular Medicine</p
sj-docx-4-vmj-10.1177_1358863X241231942 – Supplemental material for Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment
No full textSupplemental material, sj-docx-4-vmj-10.1177_1358863X241231942 for Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment by Mengwei Zhang, Saran Lotfollahzadeh, Nagla Elzinad, Xiaosheng Yang, Murad Elsadawi, Adam C Gower, Mostafa Belghasem, Tarek Shazly, Vijaya B Kolachalama and Vipul C Chitalia in Vascular Medicine</p
sj-xlsx-5-vmj-10.1177_1358863X241231942 – Supplemental material for Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment
No full textSupplemental material, sj-xlsx-5-vmj-10.1177_1358863X241231942 for Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment by Mengwei Zhang, Saran Lotfollahzadeh, Nagla Elzinad, Xiaosheng Yang, Murad Elsadawi, Adam C Gower, Mostafa Belghasem, Tarek Shazly, Vijaya B Kolachalama and Vipul C Chitalia in Vascular Medicine</p
Machine-Learning Framework for Efficient Multi-Asset Rehabilitation Planning
Get PDFWhile smart cities are viewed as the way of the future, the infrastructure assets expected to support the different smart services are currently managed using frameworks that are outdated, subjective, and inefficient. Such inefficiencies have led to huge maintenance and rehabilitation backlogs that are far beyond the financial capabilities of cities, municipalities and large asset owners like school boards. For example, the cost to bring Ontario schools facilities to an acceptable level of service is estimated to be as high as $16 billion. Currently, most “smart asset initiatives” are geared towards building new assets and using sensors to get periodic info about their condition, with little thought given regarding the condition of existing assets. As such, there is a need to introduce a “smart rehabilitation” framework that answers the question “how to bring the current infrastructure assets up to speed to satisfy the needs of current and future generations?”.
To contribute to the overall vision of smart cities (data-driven interconnected services), the introduced framework uses machine learning and smart analytics to tackle three main functions of smart asset rehabilitation frameworks: (1) it automates the inspection and condition assessment processes by using convolutional neural networks (CNNs) to develop a machine learning system where defects can be automatically detected, classified, and quantified from images; (2) it uses data mining and clustering techniques to classify the assets according to their condition and need for repairs, and then uses optimization to select which assets are most worthy of immediate repairs subject to the existing funding constraints, thus enhancing the fund allocation phase by reducing its subjectivity; and (3) it uses novel computations, visualizations, and algorithms to facilitate cost-effective and fast-tracked delivery of the required rehabilitation works by considering them as units of a large repetitive project.
To verify the strengths and versatility of the model, the proposed framework is applied to built-up roofs of educational buildings such as schools and university campuses. First, images were collected from the University of Waterloo campus buildings to develop the image-based analysis module; a two-step CNN framework that can detect damages and classify them according to their type. Information from the image-based analysis were then combined with textual information related to building age and description and unsupervised learning was applied to develop the prioritization and fund allocation module. Results from this module are used as the inputs to an optimization procedure where the overall performance of the entire asset portfolio is maximized by selecting which buildings should undergo immediate repairs, given strict budgetary constraints. Finally, the selected rehabilitation works were scheduled as units in a large repetitive project for delivery planning. Accordingly, novel computations and algorithms were developed to create compact schedules with minimal gaps that comply with deadline constraints, and novel visualizations were introduced to showcase the crews movements and the timing of all tasks required in each unit.
The proposed framework offers powerful decision support features for a proposed smart rehabilitation layer to be included into the overall smart city vision. This framework deals with existing assets and provides objective assessments, cost-effective prioritization, and time-effective delivery plans. While this study used the case of built-up roofs as an example application, the framework is scalable towards other asset components as well as other assets in general. For example, components such as parking lots and concrete elements would rely heavily on the image-based inspection module, while other components such as HVAC systems would place more emphasis on the data analytics component, including more parameters related to different performance metrics as part of the analysis. Overall, this framework has the potential to revolutionize the multi-billion-dollar business of infrastructure renewal and provide cost effective decisions that save taxpayers’ money on the long run
- …
