Browsing by Author "Yucekaya,A."

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Citation Count: 0
Blockchain Technology In Loyalty Program Applications
(Association for Computing Machinery, 2022) Bozkurt,H.I.; Gemici,S.; Yucekaya,A.; Hekimoglu,M.
Loyalty programs provide income to the user as a result of their expenditures. It helps businesses if the customer ensures continuity. A loyalty program is based on the bilateral relationship between the customer and the business. It is important to be sure of the reliability of this bilateral relationship and to be able to follow it. Today, many companies are trying to incorporate Blockchain technology into their systems because they pay attention to reliability and transparency. In this study, a loyalty ecosystem based on Blockchain technology, which will include member businesses, change the shopping and reward experience, and encourage employees is explained. We propose an application named Decentralized Loyalty Token (DLOT) to be used by entities where the community is together and the spending potential is high such as restaurants, cafes, and e-commerce platforms that want to be included in this structure. Users will earn rewards while spending DLOT Tokens and can store and accumulate the rewards indefinitely or use them for any payment. In addition, users will be able to convert the loyalty Tokens into a digital value in other businesses included in Wallet. The details of the proposed Blockchain-based loyalty system are explained along with benefits to customers and entities. © 2022 ACM.
Citation Count: 0
A Hesitant Fuzzy Linguistic Terms Set-Based AHP and TOPSIS Methodology for Fuel Coal Type Selection Problem of Industrial Facilities
(Old City Publishing, 2024) Yucekaya,A.; Ayağ,Z.
Coal is still used widely by both industrial facilities and coal fired power plants. Lignite, hard coal, coke, and imported coal are some alternatives. The coal has ash content, moisture content, heat rate, volatile matter, carbon content, sulphur content, and size that need to be considered as well as price. The suppliers provide coal products for each coal type, and the most appropriate coal product needs to be selected considering different parameters. Therefore, in this paper, a hesitant fuzzy linguistic term sets-based AHP (HFLTS-AHP) and TOPSIS method are used to select the best coal type alternative for industrial facilities. As the HFLTS-AHP is used to weight the evaluation criteria, TOPSIS is utilized to rank the fuel coal type alternatives. The proposed methodology offers an innovative and novel approach to help industrial facilities select the appropriate coal product while balancing the outputs, such as carbon, sulphur, ash content, and so on. In another point of view, the motivation of this research is to help industrial firms find out the ultimate fuel coal alternative based on their needs. This objective is realized using the proposed approach that integrates the HFLTS-AHP and TOPSIS approaches for the related problem, also utilizing group decision making. Moreover, this approach is concreted by an Excel template that provides an effective tool for firms to realize the evaluation process without many tiresome fuzzy comparisons and complex calculations. Furthermore, in the paper, a real-life case study in Turkish industrial facilities is presented to demonstrate the effectiveness and applicability of the proposed approach to readers and practitioners. In this case, seven coal type options are evaluated in terms of eight criteria by three decision makers, and the best coal type alternative is determined. © 2024 Old City Publishing, Inc.
Citation Count: 29
Nutrient dynamics in flooded wetlands. I: Model development
(2013) Hantush,M.M.; Kalin,L.; Isik,S.; Yucekaya,A.
Wetlands are rich ecosystems recognized for ameliorating floods, improving water quality, and providing other ecosystem benefits. This part of a two-paper series presents a relatively detailed process-based model for nitrogen and phosphorus retention, cycling, and removal in flooded wetlands. The model captures salient features of nutrient dynamics and accounts for complex interactions among various physical, biogeochemical, and physiological processes. The model simulates oxygen dynamics and the impact of oxidizing and reducing conditions on nitrogen transformation and removal, and approximates phosphorus precipitation and releases into soluble forms under aerobic and anaerobic conditions, respectively. Nitrogen loss pathways of volatilization and denitrification are explicitly accounted for on a physical basis. Processes in surface water and the bottom-active soil layer are described by a system of coupled ordinary differential equations. A finite-difference numerical scheme is implemented to solve the coupled system of ordinary differential equations for various multiphase constituents' concentrations in the water column and wetland soil. The numerical solution algorithm is verified against analytical solutions obtained for simplified transport and fate scenarios. Quantitative global sensitivity analysis revealed consistent model performance with respect to critical parameters and dominant nutrient processes. A hypothetical phosphorus loading scenario shows that the model is capable of capturing the phenomenon of phosphorus precipitation and release under oxic and anoxic conditions, respectively. © 2013 American Society of Civil Engineers.
Citation Count: 11
Nutrient dynamics in flooded wetlands. II: Model application
(2013) Kalin,L.; Hantush,M.M.; Isik,S.; Yucekaya,A.; Jordan,T.
In this paper, the authors applied and evaluated the wetland nutrient model that was described in Paper I. Hydrologic and water quality data from a small restored wetland located on Kent Island,Maryland, which is part of the Delmarva Peninsula on the eastern shores of the Chesapeake Bay, was used for this purpose. The model was assessed through various methods against the observed data in simulating nitrogen (N), phosphorus (P), and total suspended sediment (TSS) dynamics. Time series plots of observed and simulated concentrations and loads generally compared well; better performance was demonstrated with dissolved forms of nitrogen, i.e., ammonia and nitrate. Through qualitative and quantitative sensitivity analysis, dominant processes in the study wetland were scrutinized. Nitrification, plant uptake, and mineralization were the most important processes affecting ammonia. Denitrification in the sediment layer and diffusion to bottom sediments were identified as key processes for nitrate. Settling and resuspension were the most important processes for particulate matter (organic N, sediment) and sediment-bound phosphate (inorganic P). Order of parameter sensitivities and dominant processes exhibited seasonality. Uncertainty bands created from Monte Carlo simulations showed that parameter uncertainty is relatively small; however, uncertainty in the wetland inflow rates and loading concentrations have much more bearing on model predictive uncertainty. N, P, and TSS mass balance analysis showed that the wetland removed approximately 23, 33, and 46%, respectively, of the incoming load (runoff + atmospheric deposition) over the two-year period, with more removal in year 1 (34, 43, and 55%, respectively), which had a long stretch of a dry period. The developed model can be employed for exploring wetland response to various climatic and input conditions, and for deeper understanding of key processes in wetlands. © 2013 American Society of Civil Engineers.
Citation Count: 0
An Overview of Electricity Consumption in Europe: Models for Prediction of the Electricity Usage for Heating and Cooling
(Econjournals, 2024) Bilge, Ayşe Hümeyra; Aktunc,E.A.; Bilge,A.H.; Yucekaya,A.
Although aggregate electricity consumption provides valuable information for market analysis, demand composition, including industrial, residential, illumination, and other uses, and special days, such as national or religious holidays and annual industrial shutdowns, differ for each country. This paper analyzes the hourly electricity consumption of European countries in the European Transmission System Operation for Electricity (ENTSO-E) grid from 2006 to 2018. We propose an outlier detection method to identify special days and a modulated Fourier Series Expansion model to determine the breakdown of industrial versus household consumption and heating versus cooling consumption. The proposed outlier detection method uses the time series for each hour and checks whether a day has more than a threshold number of hours with exceptional electricity consumption levels. The proposed demand prediction model has a 3% average error when electricity usage for heating is not dominant. It also allows country classification based on consumption patterns to efficiently manage regional or country-based electricity markets. © 2024, Econjournals. All rights reserved.