Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/1248

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Citation - WoS: 3
Citation - Scopus: 4
A 2020 Vision for the Black Sea Region: the Commission on the Black Sea Proposes
(Routledge Journals Taylor & Francis Ltd, 2010) Aydın, Mustafa; Triantaphyllou, Dimitrios; International Relations; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
The Black Sea region is coming into its own although it is at times a contested and dangerous neighbourhood. Despite heightened interest in the region its real priorities and needs are still being largely ignored by insiders and outsiders alike. What is needed are regional solutions for regional problems. The authors present the key findings and recommendations of the Commission on the Black Sea a civil society initiative comprising a number of current and former policy-makers scholars and practitioners both from within the region and from outside with the purpose of contributing to a joint vision and a common strategy for the Black Sea region by developing new knowledge in areas of key concern.
Citation - WoS: 5
Citation - Scopus: 6
(3+3+2) Warped-Like Product Manifolds With Spin(7) Holonomy
(Elsevier Science Bv, 2011) Uğuz, Selman; Bilge, Ayşe Hümeyra; Industrial Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
We consider a generalization of eight-dimensional multiply warped product manifolds as a special warped product by allowing the fiber metric to be non-block diagonal. We define this special warped product as a (3 + 3 + 2) warped-like manifold of the form M = F x B. where the base B is a two-dimensional Riemannian manifold and the fibre F is of the form F = F-1 x F-2 where the F-i(i = 1 2) are Riemannian 3-manifolds. We prove that the connection on M is completely determined by the requirement that the Bonan 4-form given in the work of Yasui and Ootsuka [Y. Yasui and T. Ootsuka Spin(7) holonomy manifold and superconnection Class. Quantum Gravity 18(2001)807-816] be closed. Assuming that the F-i are complete connected and simply connected it follows that they are isometric to S-3 with constant curvature k > 0 and the Yasui-Ootsuka solution is unique in the class of (3 + 3 + 2) warped-like product metrics admitting a specific Spin(7) structure. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.
Citation - WoS: 1
Citation - Scopus: 2
Acculturation Strategies of International Higher Education Students in Türkiye: the Role of Social Support, Cultural Capital, Self-Esteem, General Trust, and General Self-Efficacy
(Springer, 2025) Ergin-Kocaturk, Hatice; Tekel, Esra; Su, Ahmet; Kocaturk, Metin; Karadag, Engin; 01. Kadir Has University
Understanding the factors influencing acculturation strategies among international students cannot be overstated, as successful adaptation is crucial for academic success and overall well-being. Although extensive research has explored these dynamics in various contexts, a notable gap remains in the literature on international students in T & uuml;rkiye. This study aimed to investigate the effects of social support, cultural and economic capital, self-esteem, general trust, and general self-efficacy on the acculturation strategies of international higher-education students in T & uuml;rkiye. Utilizing data from 3,554 international students, various scales and questionnaires were employed, including the Acculturation Strategies Scale, Cultural Capital Questionnaire, Economic Capital Questionnaire, Self-Esteem Scale, General Confidence Scale, General Self-Efficacy Scale, and Social Support Questionnaire. The collected data were analyzed using correlation and multiple regression analyses. The results revealed significant relationships between the examined factors and acculturation strategies adopted by international students. These findings highlight the crucial roles of social support, cultural capital, and psychological attributes in shaping how international students adapt to new cultural environments. The implications of these results underscore the importance of targeted support programs to enhance international students' acculturation experiences and overall well-being in T & uuml;rkiye's higher education context.
The Acquisition and Use of Relative Clauses in Turkish-Learning Children's Conversational Interactions: a Cross-Linguistic Approach (vol 46, Pg 1142, 2019)
(Cambridge Univ Press, 2022) Uzundag, Berna A.; Kuntay, Aylin C.; Psychology; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
[Abstract Not Available]
Citation - WoS: 32
Citation - Scopus: 35
Activating Reflective Thinking With Decision Justification and Debiasing Training
(Society for Judgment and Decision making, 2020) İsler, Ozan; Yılmaz, Onurcan; Doğruyol, Burak; Psychology; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
Manipulations for activating reflective thinking, although regularly used in the literature, have not previously been systematically compared. There are growing concerns about the effectiveness of these methods as well as increasing demand for them. Here, we study five promising reflection manipulations using an objective performance measure — the Cognitive Reflection Test 2 (CRT-2). In our large-scale preregistered online experiment (N = 1,748), we compared a passive and an active control condition with time delay, memory recall, decision justification, debiasing training, and combination of debiasing training and decision justification. We found no evidence that online versions of the two regularly used reflection conditions — time delay and memory recall — improve cognitive performance. Instead, our study isolated two less familiar methods that can effectively and rapidly activate reflective thinking: (1) a brief debiasing training, designed to avoid common cognitive biases and increase reflection, and (2) simply asking participants to justify their decisions.
Citation - WoS: 6
Citation - Scopus: 10
An Adaptive Affinity Matrix Optimization for Locality Preserving Projection Via Heuristic Methods for Hyperspectral Image Analysis
(IEEE-Inst Electrıcal Electronıcs Engıneers Inc, 2019) Taşkın, Gülşen; Ceylan, Oğuzhan; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
Locality preserving projection (LPP) has been often used as a dimensionality reduction tool for hyperspectral image analysis especially in the context of classification since it provides a projection matrix for embedding test samples to low dimensional space. However, the performance of LPP heavily depends on the optimization of two parameters of the graph affinity matrix: k-nearest neighbor and heat kernel width, when one considers an isotropic kernel. These two parameters might be optimally chosen simply based on a grid search. In case of using a generalized heat kernel where each feature is separately weighted by a kernel width, the number of parameters that need to be optimized is related to the number of features of the dataset, which might not be very easy to tune. Therefore, in this article, we propose to use heuristic methods, including genetic algorithm (GA), harmony search (HS), and particle swarm optimization (PSO), to explore the effects of the heat kernel parameters aiming to analyze the embedding quality of LPP's projection in terms of various aspects, including 1-NN classification accuracy, locality preserving power, and quality of the graph affinity matrix. The results obtained with the experiments on three hyperspectral datasets show that HS performs better than GA and PSO in optimizing the parameters of the affinity matrix, and the generalized heat kernel achieves better performance than the isotropic kernel. Additionally, a feature selection application is performed by using the kernel width of the generalized heat kernel for each heuristic method. The results show that very promising results are obtained in comparison with the state-of-the-art feature selection methods.
Citation - WoS: 23
Citation - Scopus: 35
An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids
(Springer, 2022) Ahmadi, Bahman; Younesi, Soheil; Ceylan, Oguzhan; Ozdemir, Aydogan; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf Optimization Algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf Optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms.
Citation - WoS: 2
Citation - Scopus: 3
Advanced Restoration Management Strategies in Smart Grids: the Role of Distributed Energy Resources and Load Priorities
(Pergamon-elsevier Science Ltd, 2025) Ahmadi, Bahman; Ceylan, Oguzhan; Ozdemir, Aydogan; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
Fast restoration following long outages is a challenge in the smart city management process. It is necessary to accurately characterize the real operating conditions of the system for optimal restoration. This study focuses on two key factors of a practical distribution system restoration. The first factor is cold load pickup (CLPU), which commonly occurs after an outage and is caused by thermostatically controlled loads. A time-dependent CLPU is modeled to accurately describe the restored load behaviors. The second factor is the effect of the distributed generators (DG), energy storage systems (ESSs), and load priority factors on the system's restoration process. To address this challenge, a robust optimization model is proposed that fully considers the effect of DG, and ESS units and uncertainty of CLPU. The proposed models are tested on the IEEE 33-node and 69-node test systems using the Advanced Grey Wolf Algorithm (AGWO). The simulation scenarios are designed to uncover optimal scheduling strategies for the restoration process corresponding to each Pareto solution of a previous study. The results are discussed for several distinct initial conditions. Moreover, a comparative evaluation is done, contrasting the outcomes achieved through the AGWO algorithm with those stemming from alternative heuristic methods.
Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
(MDPI, 2025) Jamil, Mahnoor; Trpcheska, Hristina Mihajloska; Popovska-Mitrovikj, Aleksandra; Dimitrova, Vesna; Creutzburg, Reiner; 01. Kadir Has University
Image-based spam poses a significant challenge for traditional text-based filters, as malicious content is often embedded within images to bypass keyword detection techniques. This study investigates and compares the performance of six machine learning models-ResNet50, XGBoost, Logistic Regression, LightGBM, Support Vector Machine (SVM), and VGG16-using a curated dataset containing 678 legitimate (ham) and 520 spam images. The novelty of this research lies in its comprehensive side-by-side evaluation of diverse models on the same dataset, using standardized dataset preprocessing, balanced data splits, and validation techniques. Model performance was assessed using evaluation metrics such as accuracy, receiver operating characteristic (ROC) curve, precision, recall, and area under the curve (AUC). The results indicate that ResNet50 achieved the highest classification performance, followed closely by XGBoost and Logistic Regression. This work provides practical insights into the strengths and limitations of traditional, ensemble-based, and deep learning models for image-based spam detection. The findings can support the development of more effective and generalizable spam filtering solutions in multimedia-rich communication platforms.
Citation - WoS: 18
Citation - Scopus: 25
After the Crimean Crisis: Towards a Greater Russian Maritime Power in the Black Sea
(Routledge Journals Taylor & Francis Ltd, 2014) Delanoe, Igor; 01. Kadir Has University
The modernization of the Black Sea Fleet currently underway is believed to be one of the most ambitious parts of the Russian State Arms Procurement programme 2011-2020. Up to 18 units are being built and are expected to be commissioned in the Russian Black Sea Fleet by 2020 while new infrastructures are being developed. However Russia's annexation of Crimea in March 2014 has overthrown the Black Sea maritime context. It is likely to give substantial impetus to Russian naval plans in the Black Sea and by extension to sustain Moscow's resumption of naval activity in the Mediterranean. Yet whereas Russia's maritime power has been dramatically enhanced due to the takeover of Crimea Moscow's naval power in the Black Sea and in the Mediterranean remains challenged by a set of qualitative factors. Beyond the Ukrainian crisis has demonstrated the inability of the European Union to manage its Black Sea environment as well as it has highlighted the United States waning influence and interests in the region.
Citation - WoS: 1
Citation - Scopus: 1
AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
(MDPI, 2025) Yildirim, Senda; Yucekaya, Ahmet Deniz; Hekimoglu, Mustafa; Ucal, Meltem; Aydin, Mehmet Nafiz; Kalafat, Irem; Industrial Engineering; Economics; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning-Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)-were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project.
Citation - WoS: 78
Citation - Scopus: 108
Air Quality Prediction Using Cnn Plus Lstm-Based Hybrid Deep Learning Architecture
(Springer Heidelberg, 2022) Gilik, Aysenur; Ogrenci, Arif Selcuk; Ozmen, Atilla; Electrical-Electronics Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
Air pollution prediction based on variables in environmental monitoring data gains further importance with increasing concerns about climate change and the sustainability of cities. Modeling of the complex relationships between these variables by sophisticated methods in machine learning is a promising field. The objectives of this work are to develop a supervised model for the prediction of air pollution by using real sensor data and to transfer the model between cities. The combination of a convolutional neural network and a long short-term memory deep neural network model was proposed to predict the concentration of air pollutants in multiple locations of a city by using spatial-temporal relationships. Two approaches have been adopted: the univariate model contains the information of one pollutant whereas the multivariate model contains the information of all pollutants and meteorology data for prediction. The study was carried out for different pollutants which are in the publicly available data of the cities of Barcelona, Kocaeli, and Istanbul. The hyperparameters of the model (filter, frame, and batch sizes; number of convolutional/LSTM layers and hidden units; learning rate; and parameters for sample selection, pooling, and validation) were tuned to determine the architecture that achieved the lowest test error. The proposed model improved the prediction performance (measured by the root mean square error) by 11-53% for particulate matter, 20-31% for ozone, 9-47% for nitrogenoxides, and 18-46% for sulfurdioxide with respect to the 1-hidden layer long short-term memory networks utilized in the literature. The multivariate model without using meteorological data revealed the best results. Regarding transfer learning, the network weights were transferred from the source city to the target city. The model has more accurate prediction performance with the transfer of the network from Kocaeli to Istanbul as those neighbor cities have similar air pollution and meteorological characteristics.
Citation - WoS: 41
Citation - Scopus: 50
Alevis and Alevism in the Changing Context of Turkish Politics: the Justice and Development Party's Alevi Opening
(Routledge Journals Taylor & Francis Ltd, 2011) Soner, Bayram Ali; Toktaş, Şule; 01. Kadir Has University
The Justice and Development Party (JDP Adalet ve Kalkinma Partisi) has launched a rapprochement policy toward the Alevis. The JDP's Alevi Opening has presented a unique case in Turkey's latest identity politics not only because Alevi claims for the first time came to be involved in political processes for official recognition and accommodation but also because the process was handled by a political party which is regarded to have retained Islamist roots in Sunni interpretation. This article explores the JDP's Alevi Opening process and tries to explain the motivations behind the party's decision to incorporate the Alevi question in its political agenda. What is more the debate that the opening has caused is also under scrutiny with the positions and arguments held by the actors and the agencies involved in the process e. g. the Alevis (the secularist and the conservative wings) the General Directorate of Religious Affairs the National Security Council the JDP leadership and the Islamist intellectuals.
Citation - WoS: 17
Citation - Scopus: 20
All the Dark Triad and Some of the Big Five Traits Are Visible in the Face
(Pergamon-Elsevıer Scıence Ltd, 2021) Alper, Sinan; Bayrak, Fatih; Yılmaz, Onurcan; Psychology; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
Some of the recent studies suggested that people can make accurate inferences about the level of the Big Five and the Dark Triad personality traits in strangers by only looking at their faces. However, later findings provided only partial support and the evidence is mixed regarding which traits can be accurately inferred from faces. In the current research, to provide further evidence on whether the Big Five and the Dark Triad traits are visible in the face, we report three studies, two of which were preregistered, conducted on both WEIRD (the US American) and non-WEIRD (Turkish) samples (N = 880). The participants in both the US American and Turkish samples were successful in predicting all Dark Triad personality traits by looking at a stranger's face. However, there were mixed results regarding the Big Five traits. An aggregate analysis of the combined dataset demonstrated that extraversion (only female), agreeableness, and conscientiousness were accurately inferred by the participants in addition to the Dark Triad traits. Overall, the results suggest that inferring personality from faces without any concrete source of information might be an evolutionarily adaptive trait.
Citation - WoS: 2
Citation - Scopus: 2
Altered Dynamics of S. Aureus Phosphofructokinase Via Bond Restraints at Two Distinct Allosteric Binding Sites
(Academic Press Ltd- Elsevier Science Ltd, 2022) Celebi, Metehan; Akten, Ebru Demet; Molecular Biology and Genetics; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
The effect of perturbation at the allosteric site was investigated through several replicas of molecular dynamics (MD) simulations conducted on bacterial phosphofructokinase (SaPFK). In our previous work, an alternative binding site was estimated to be allosteric in addition to the experimentally reported one. To highlight the effect of both allosteric sites on receptor's dynamics, MD runs were carried out on apo forms with and without perturbation. Perturbation was achieved via incorporating multiple bond restraints for residue pairs located at the allosteric site. Restraints applied to the predicted site caused one dimer to stiffen, whereas an increase in mobility was detected in the same dimer when the experimentally resolved site was restrained. Fluctuations in C-alpha-C-alpha distances which is used to disclose residues with high potential of communication indicated a marked increase in signal transmission within each dimer as the receptor switched to a restrained state. Cross-correlation of positional fluctuations indicated an overall decrease in the magnitude of both positive and negative correlations when restraints were employed on the predicted allosteric site whereas an exact opposite effect was observed for the reported site. Finally, mutual correspondence between positional fluctuations noticeably increased with restraints on predicted allosteric site, whereas an opposite effect was observed for restraints applied on experimentally reported one. In view of these findings, it is clear that the perturbation of either one of two allosteric sites effected the dynamics of the receptor with a distinct and contrasting character. (c) 2022 Elsevier Ltd. All rights reserved.
Amplitude and Frequency Modulations With Cellular Neural Networks
(Springer, 2015) Tander, Baran; Özmen, Atilla; Computer Engineering; Electrical-Electronics Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
Amplitude and frequency modulations are still the most popular modulation techniques in data transmission at telecommunication systems such as radio and television broadcasting gsm etc. However the architectures of these individual systems are totally different. In this paper it is shown that a cellular neural network with an opposite-sign template can behave either as an amplitude or a frequency modulator. Firstly a brief information about these networks is given and then the amplitude and frequency surfaces of the generated quasi-sine oscillations are sketched with respect to various values of their cloning templates. Secondly it is proved that any of these types of modulations can be performed by only varying the template components without ever changing their structure. Finally a circuit is designed simulations are presented and performance of the proposed system is evaluated. The main contribution of this work is to show that both amplitude and frequency modulations can be realized under the same architecture with a simple technique specifically by treating the input signals as template components.
Analogy Is Indispensable but Rule Is a Must: Insights From Turkish
(Cambridge Univ Press, 2022) Nakipoglu, Mine; Uzundag, Berna A.; Ketrez, F. Nihan; Psychology; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
Inflectional morphology provides a unique platform for a discussion of whether morphological productivity is rule-based or analogy-based. The present study testing 140 children (range = 29 to 97 months; M(SD) = 64.1(18.8)) on an elicited production task investigated the acquisition of the irregular distribution in the Turkish aorist. Results suggested that to discover the allomorphs of the Turkish aorist, children initially carried out similarity comparisons between analogous exemplars, which helped them tap into phonological features to induce generalizations for regulars and irregulars. Thereafter to tackle the irregularity, children entertained competing hypotheses yielding overregularizations and irregularizations. While the trajectory of overregularizations implicated the gradual formulation of an abstraction based on type-frequency, irregularizations suggested both intrusion of analogous exemplars and children's attempts to default to an erroneous micro-generalization. Our findings supported a model of morphological learning that is driven by analogy at the outset and that invokes rule-induction in later stages.
Citation - WoS: 5
Citation - Scopus: 5
Analysis of Mixed-Element Structures Formed With Shunt Capacitors Separated by Transmission Lines
(IEEE-Inst Electrical Electronics Engineers Inc, 2019) Şengül, Metin Y.; Çakmak, Gökhan; Electrical-Electronics Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
In this brief, the analysis of mixed-element structures formed with shunt capacitors separated by commensurate transmission lines is performed first time in the literature. First, a low-pass lumped-element ladder network is considered. Then the series inductors are replaced with commensurate transmission lines. As a result, a practically important mixed-element structure is obtained. Then the description of the structure by means of two frequency variables (one for shunt capacitors and one for transmission lines) is detailed: Explicit expressions for the coefficients of the descriptive two-variable polynomials in terms of the coefficients of the single variable boundary polynomials are derived for various numbers of elements, which are obtained first time in the literature. Finally, a mixed-element broadband matching network is designed to illustrate the usage of the obtained expressions. If it is preferred not to have shunt capacitors, they can be replaced with open-ended stubs via Richard's transformation. So the resultant circuit is extremely suitable for microstrip fabrication.
Citation - WoS: 7
Citation - Scopus: 12
An Analysis of Price Spikes and Deviations in the Deregulated Turkish Power Market
(Elsevier, 2019) Gayretli, Gizem; Yücekaya, Ahmet; Bilge, Ayşe Hümeyra; Industrial Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
The successful operation of a real time market is related to the planning in the day ahead market. We analyze the day ahead and real time market data for the Turkish power market for the period 2012-2015 to classify price spikes and their causes. We also focus on the levels of deviation between the day ahead market values and the real time market values. We define price deviation and load deviation ratios to measure the level of deviation both in price and demand. The analysis for the load is based on load shedding and cycling values. We analyze the mean and standard deviation in market prices and we determine the price spike as a two sigma deviation from the mean value. It is shown that 60% of the price deviation ratios are in the range of ( +/- 20%), while 44% are in the range of ( +/- 10%) and 35% are in the range of (+/- 5%). We also show that 56.9% of the spikes are due to problems in the generation of natural gas based power plants which affect the day ahead and real time prices. A total of 29.2% of the spikes are due to power plant and system failures that affect only real time prices. The share of high temperature based spikes is 13.9% which is a result of air conditioner usage.
Citation - Scopus: 88
An Analytic Network Process-Based Approach To Concept Evaluation in a New Product Development Environment
(Taylor & Francis, 2007) Ayağ, Zeki; Özdemir, Rifat Gürcan; Industrial Engineering; 05. Faculty of Engineering and Natural Sciences; 01. Kadir Has University
Selecting the best product concept is one of the most critical tasks in a new product development (NPD) environment. Making decisions at this stage becomes very difficult due to imprecise and uncertain product requirements. So the evaluation process of determining the most satisfying conceptual design has been a very vital issue for companies to survive in fast-growing markets for a long time. Therefore most companies have used various methods to successfully carry out this difficult and time-consuming process. Of these methods an analytic hierarchy process (AHP) has been widely used in multiple-criteria decision-making problems (i.e. concept selection equipment evaluation). In this study however we use an analytic network process (ANP) a more general form of AHP due to the fact that AHP cannot accommodate the variety of interactions dependencies and feedback between higher and lower level elements. Briefly in this paper an ANP-based approach is presented to evaluate a set of conceptual design alternatives in order to reach to the best concept satisfying the needs and expectations of both customers and company. In addition a numerical example is presented to illustrate the proposed approach.

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