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abdeshahi--ashkan
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Sustainable-Resilient Supplier Selection Through Fuzzy Multi-Criteria Decision Making Approaches
, M.Sc. Thesis Sharif University of Technology ; Akbari Jokar, Mohammad Resa (Supervisor) ; Memari, Ashkan (Co-Supervisor)
Abstract
Today, with the expansion of business relations at international levels, there are many organizations that operate in global supply chains. Activities at such levels face new types of challenges in various fields of "supply chain management" pillars, of which "supplier selection" is one of them. The possibility of unexpected events, the need to comply with environmental protection laws, as well as the need to take into account social considerations, are among the things that have increased by being in larger supply chains, making it challenging to provide the basic resources needed in such chains and forcing their managers to considering considerations other than what they have always...
Design of a Sustainable Supply Chain Network for Urban Spent Batteries
, M.Sc. Thesis Sharif University of Technology ; Akbari Jokar, Mohammad Reza (Supervisor) ; Memari, Ashkan (Supervisor)
Abstract
This research addresses the design and optimization of a sustainable supply chain for an environmental startup, with a focus on the collection and recycling of urban alkaline batteries in Tehran. This problem is motivated by the lack of proper management of this type of waste, its environmental pollution, and the resulting loss of natural resources. The main concern is the optimal selection of metro stations to be equipped with collection boxes and the determination of the transportation and recycling network structure in such a way that the economic performance of the network (total profit/cost) is optimized while environmental impacts (carbon emissions) are controlled simultaneously. To...
Recovery of uranium from phosphate ore in the sheikh habil-Iran mine: part I— multivariable optimization of leaching process using the response surface method
, Article Frontiers in Chemistry ; Volume 11 , 2023 ; 22962646 (ISSN) ; Outokesh, M ; Nejad, D. G ; Zare, M. H ; Sadeghi, M. H ; Sharif University of Technology
Frontiers Media SA
2023
Abstract
In this research, the recovery of uranium from the phosphate ore of the Sheikh Habil-Iran mine using flotation/calcination-leaching processes has been investigated. A 75–150 μm phosphate ore particle size, sodium oleate as a collector with a concentration of 2,000 g/ton of rock, pH = 10, and 5 min flotation time were obtained as the optimum parameters of flotation using the reverse method, leading to phosphate ore with a grade of 180 ppm UO2, 36.1% P₂O₅, 7.22% SiO2, and CaO/P₂O₅ = 1.23. The optimum calcination parameters were selected as 100 μm phosphate ore particles size at D80, 900°C temperature, and 2 h heating time, which resulted in phosphate ore with a grade of 173 ppm UO2 and 31.9%...
Leaching of Uranium from pHospHate Ores of Sheikh Habil Region in the Process of pHospHoric Acid Production (in a Wet Way)
, M.Sc. Thesis Sharif University of Technology ; Outokesh, Mohammad (Supervisor) ; Godocynejhad, Davood (Supervisor) ; Khoshnodi, Khalegh (Co-Supervisor) ; Ghadiri, Ahmad (Co-Supervisor)
Abstract
In this research, uranium is leached from pHospHate ore of Sheikh Habil region by leaching operation using leaching agent (sulfuric acid) along with production of pHospHoric acid. In the following, the production of yellow cake from the produced liqueur solution (pHospHoric acid) by solvent extraction process is also mentioned. PHospHate ore contains a large amount of calcite and dolomite, which removal of these annoying elements by wet, dry and acid washing methods is the key to success in the production of pHospHoric acid. For this purpose, in the wet method of a flotation system, the dry method of heat furnace and in the acid washing method, various organic and inorganic acids were used...
Artificial intelligence for detection of external cervical resorption using label-efficient self-supervised learning method
, Article Journal of Endodontics ; Volume 50, Issue 2 , 2024 , Pages 144-153.e2 ; 00992399 (ISSN) ; Dianat, O ; Abbasi, R ; Zahedrozegar, S ; Ashkan, A ; Motamedian, S. R ; Rohban, M. H ; Nosrat, A ; Sharif University of Technology
Elsevier
2024
Abstract
Introduction: The aim of this study was to leverage label-efficient self-supervised learning (SSL) to train a model that can detect ECR and differentiate it from caries. Methods: Periapical (PA) radiographs of teeth with ECR defects were collected. Two board-certified endodontists reviewed PA radiographs and cone beam computed tomographic (CBCT) images independently to determine presence of ECR (ground truth). Radiographic data were divided into 3 regions of interest (ROIs): healthy teeth, teeth with ECR, and teeth with caries. Nine contrastive SSL models (SimCLR v2, MoCo v2, BYOL, DINO, NNCLR, SwAV, MSN, Barlow Twins, and SimSiam) were implemented in the assessment alongside 7 baseline deep...