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Gazi Duman
Department of Technology Management, University of Bridgeport, 221 University Avenue, Bridgeport, CT 06604, USA

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Concept paper
Published: 07 June 2021 in Applied Sciences
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The COVID-19 pandemic has highlighted the need for improved airborne infectious disease monitoring capability. A key challenge is to develop a technology that captures pathogens for identification from ambient air. While pathogenic species vary significantly in size and shape, for effective airborne pathogen detection the target species must be selectively captured from aerosolized droplets. Captured pathogens must then be separated from the remaining aerosolized droplet content and characterized in real-time. While improvements have been made with clinical laboratory automated sorting in culture media based on morphological characteristics of cells, this application has not extended to aerosol samples containing bacteria, viruses, spores, or prions. This manuscript presents a strategy and a model for the development of an airborne pandemic early warning system using aerosol sampling.

ACS Style

Joseph Bango; Sophia Agostinelli; Makayla Maroney; Michael Dziekan; Ruba Deeb; Gazi Duman. A Pandemic Early Warning System Decision Analysis Concept Utilizing a Distributed Network of Air Samplers via Electrostatic Air Precipitation. Applied Sciences 2021, 11, 5308 .

AMA Style

Joseph Bango, Sophia Agostinelli, Makayla Maroney, Michael Dziekan, Ruba Deeb, Gazi Duman. A Pandemic Early Warning System Decision Analysis Concept Utilizing a Distributed Network of Air Samplers via Electrostatic Air Precipitation. Applied Sciences. 2021; 11 (11):5308.

Chicago/Turabian Style

Joseph Bango; Sophia Agostinelli; Makayla Maroney; Michael Dziekan; Ruba Deeb; Gazi Duman. 2021. "A Pandemic Early Warning System Decision Analysis Concept Utilizing a Distributed Network of Air Samplers via Electrostatic Air Precipitation." Applied Sciences 11, no. 11: 5308.

Methodologies and application
Published: 02 April 2020 in Soft Computing
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Growing rates of innovation and consumer demand resulted in rapid accumulation of waste of electrical and electronic equipment or electronic waste (e-waste). In order to build and sustain green cities, efficient management of e-waste rises as a viable response to this accumulation. Accurate e-waste predictions that municipalities can utilize to build appropriate reverse logistics infrastructures gain significance as collecting, recycling and disposing the e-waste become more complex and unpredictable. In line with its significance, the related literature presents several methodologies focusing on e-waste generation forecasting. Among these methodologies, grey modeling approach has aroused interest due to its ability to present meaningful results with small-sized or limited data. In order to improve the overall success rate of the approach, several grey modeling-based forecasting techniques have been proposed throughout the past years. The performance of these models, however, profoundly leans on the parameters used with no established consensus regarding the suitable criteria for better accuracy. To address this issue and to provide a guideline for academicians and practitioners, this paper presents a comparative analysis of most utilized grey modeling methods in the literature improved by particle swarm optimization. A case study employing e-waste data from Washington State is provided to demonstrate the comparative analysis proposed in the study.

ACS Style

Gazi Murat Duman; Elif Kongar; Surendra M. Gupta. Predictive analysis of electronic waste for reverse logistics operations: a comparison of improved univariate grey models. Soft Computing 2020, 24, 15747 -15762.

AMA Style

Gazi Murat Duman, Elif Kongar, Surendra M. Gupta. Predictive analysis of electronic waste for reverse logistics operations: a comparison of improved univariate grey models. Soft Computing. 2020; 24 (20):15747-15762.

Chicago/Turabian Style

Gazi Murat Duman; Elif Kongar; Surendra M. Gupta. 2020. "Predictive analysis of electronic waste for reverse logistics operations: a comparison of improved univariate grey models." Soft Computing 24, no. 20: 15747-15762.

Journal article
Published: 17 June 2019 in Waste Management
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Rapid and revolutionary changes in technology and rising demand for consumer electronics have led to staggering rates of accumulation of electrical and electronic equipment waste, viz., WEEE or e-waste. Consequently, e-waste has become one of the fastest growing municipal solid waste streams in the United States making its efficient management crucial in supporting the efforts to create and sustain green cities. Accurate estimations on the amount of e-waste might help in increasing the efficiency of waste collection, recycling and disposal operations that have become more complicated and unpredictable. Early work focusing on prediction of e-waste generation includes a wide range of methodologies. Among these, grey forecasting models have drawn attention due to their capability to provide meaningful results with relatively small-sized or limited data. The performance of grey models heavily rely on their parameters. The purpose of this study is to present a novel forecasting technique for e-waste predictions with multiple inputs in presence of limited historical data. The proposed nonlinear grey Bernoulli model with convolution integral NBGMC(1,n) improved by Particle Swarm Optimization (PSO) demonstrates superior accuracy over alternative forecasting models. The proposed model and its findings are delineated with the help of a case study utilizing Washington State e-waste data. The results indicate that population density has a major impact on the generated e-waste followed by household income level. The findings also show that the e-waste generation forms a saturated distribution in Washington State. These results can help decision makers plan for more effective reverse logistics infrastructures that would ensure proper collection, recycling and disposal of e-waste.

ACS Style

Gazi Murat Duman; Elif Kongar; Surendra M. Gupta. Estimation of electronic waste using optimized multivariate grey models. Waste Management 2019, 95, 241 -249.

AMA Style

Gazi Murat Duman, Elif Kongar, Surendra M. Gupta. Estimation of electronic waste using optimized multivariate grey models. Waste Management. 2019; 95 ():241-249.

Chicago/Turabian Style

Gazi Murat Duman; Elif Kongar; Surendra M. Gupta. 2019. "Estimation of electronic waste using optimized multivariate grey models." Waste Management 95, no. : 241-249.

Journal article
Published: 18 March 2019 in IEEE Transactions on Engineering Management
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The concept of efficiency has always been and will continue to be important for competitive business environments where limited resources exist. Owing to the growing complexity of organizations and economy in general, this trend is expected to continue to remain a high priority for organizations. Continuous performance evaluations that utilize both qualitative and quantitative information play a significant role in sustaining efficient and effective business processes. Therefore, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency in various industries. Majority of these models, however, focus solely on quantitative criteria, avoiding the interrelations and dependencies between qualitative and quantitative measurements. Furthermore, these methodologies tend to utilize discrete and contemporary information eliminating historical performance data. With these motivations, this paper proposes an integrated approach combining fuzzy decision-making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and artificial neural network (ANN) methodologies for performance evaluation. In the proposed model, DEMATEL and ANP methodologies are utilized in a group decision-making concept to obtain priorities of the evaluation criteria. Following this, an ANN model is designed and trained with historical performance data collected from the organization and the results of the fuzzy DEMATEL-ANP model. The outcomes include the relational data among the criteria and alternatives used in the model in addition to their relative rankings. A food industry case study is presented to demonstrate the steps of the proposed model.

ACS Style

Gazi Murat Duman; Ahmed ElSayed; Elif Kongar; Surendra M. Gupta. An Intelligent Multiattribute Group Decision-Making Approach With Preference Elicitation for Performance Evaluation. IEEE Transactions on Engineering Management 2019, 67, 885 -901.

AMA Style

Gazi Murat Duman, Ahmed ElSayed, Elif Kongar, Surendra M. Gupta. An Intelligent Multiattribute Group Decision-Making Approach With Preference Elicitation for Performance Evaluation. IEEE Transactions on Engineering Management. 2019; 67 (3):885-901.

Chicago/Turabian Style

Gazi Murat Duman; Ahmed ElSayed; Elif Kongar; Surendra M. Gupta. 2019. "An Intelligent Multiattribute Group Decision-Making Approach With Preference Elicitation for Performance Evaluation." IEEE Transactions on Engineering Management 67, no. 3: 885-901.

Original research article
Published: 20 July 2018 in Frontiers in Nutrition
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In addition to retaining high levels of customer satisfaction, sustainability of businesses is also heavily reliant on the efficiency of their internal and external processes. Continuous performance evaluations using key performance metrics to leverage operations are essential in maintaining a sustainable business while achieving growth objectives for revenue and profitability. Traditionally, companies have considered various financial criteria, quality characteristics, and targeted levels of service as their primary factors for performance evaluation. However, increasing environmental and social awareness and accompanying governmental legislations are now requiring companies to integrate these two aspects into their performance evaluations. With this motivation, this study proposes a Balanced Scorecard (BSC)-based approach combining Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) methodologies for performance evaluation. The grey system theory has been utilized in order to capture the vagueness and the uncertainty in decision making. To demonstrate the functionality of the approach, a case study is conducted on a U.S.-based food franchise. The results of the algorithm and a discussion elaborating on the findings are provided.

ACS Style

Gazi M. Duman; Murat Taskaynatan; Elif Kongar; Kurt A. Rosentrater. Integrating Environmental and Social Sustainability Into Performance Evaluation: A Balanced Scorecard-Based Grey-DANP Approach for the Food Industry. Frontiers in Nutrition 2018, 5, 65 .

AMA Style

Gazi M. Duman, Murat Taskaynatan, Elif Kongar, Kurt A. Rosentrater. Integrating Environmental and Social Sustainability Into Performance Evaluation: A Balanced Scorecard-Based Grey-DANP Approach for the Food Industry. Frontiers in Nutrition. 2018; 5 ():65.

Chicago/Turabian Style

Gazi M. Duman; Murat Taskaynatan; Elif Kongar; Kurt A. Rosentrater. 2018. "Integrating Environmental and Social Sustainability Into Performance Evaluation: A Balanced Scorecard-Based Grey-DANP Approach for the Food Industry." Frontiers in Nutrition 5, no. : 65.

Review
Published: 13 June 2017 in Logistics
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Growing environmental awareness coupled with stricter governmental regulations has fueled the need for integrating sustainability into supply chain and logistics activities. Accordingly, recent studies in the literature have emphasized the significance of environmentally concerned logistics operations (ECLO). Research in the broad area of ECLO encompasses a wide range of topics including sustainable supply chain, green supply chain, closed-loop supply chain, low-carbon logistics, and waste management. In this paper, a comprehensive content analysis and area review is presented. Over 800 papers published between 1994 and 2017 in peer-reviewed journals, proceedings, and book chapters are utilized. These papers are analyzed in consecutive stages after being reviewed under a structural dimension process that addresses the fields of environmentally concerned logistics operations. Following the state-of-the-art review, a detailed analysis of ECLO research with a special emphasis on fuzzy applications is provided. The findings clearly indicate that the fuzzy multi-criteria decision making technique is a frequently used hybrid method, whereas fuzzy sets theory and other fuzzy hybrid techniques identify a gap in the related literature. This paper provides further critical analysis and other research suggestions in order to clarify these gaps and offer additional research perspectives. This information may provide extensive data that will enable future researchers to fill these gaps within this field.

ACS Style

Ozden Tozanli; Gazi Duman; Elif Kongar; Surendra Gupta. Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey. Logistics 2017, 1, 4 .

AMA Style

Ozden Tozanli, Gazi Duman, Elif Kongar, Surendra Gupta. Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey. Logistics. 2017; 1 (1):4.

Chicago/Turabian Style

Ozden Tozanli; Gazi Duman; Elif Kongar; Surendra Gupta. 2017. "Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey." Logistics 1, no. 1: 4.