Scopus

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14411/19

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The Special Issue to Honor 90th Birthday Professor Sehie Park
(Yokohama Publications, 2025) Karapinar, Erdal
The Objects and Effects of the Restrictions by Object Under Art. 101(1) TFEU: Has the ECJ Solved the Riddle
(Springer Heidelberg, 2026) Korkmaz Goka, Ekin
The concept of by object infringements under Art. 101(1) of the TFEU has been a subject of ongoing controversy. Central to this debate are two primary issues: the interpretation of the term object and whether the effects or non-agreement elements in general should be considered when determining it. Should the latter question be answered affirmatively, further complications arise, particularly regarding the scope of the analysis in relation to the effects analysis and the allocation of the evidentiary burden between the parties. EU case law has significantly contributed to this debate, both in support and contradiction. The ECJ has, albeit implicitly, developed a system which seeks to strike a balance between competing interests while considering economic realities. However, the use of ambiguous terminology, inconsistencies in judicial rulings, and a lack of sufficiently clear explanations in certain cases have hindered the literature and national authorities from fully understanding the underlying system. This study aims to address this gap in the literature by providing a comprehensive legal analysis of the concept of by object infringements under Art. 101(1), in light of the framework established by the ECJ. The objective is to contribute to greater legal certainty in this area.
The Impact of Oil Price Shocks on the Economic Growth of Selected MENA 1 Countries
(International Association for Energy Economics, 2010) Berument, M. Hakan; Ceylan, Nildag Basak; Dogan, Nukhet
The “Carnival” in Edward Albee’s the Zoo Story
(Ovidius University, 2025) Serdaroğlu, Duygu
Software Code Smell Prediction Model Using Shannon, Renyi and Tsallis Entropies
(MDPI, 2018) Blazauskas, Tomas; Gupta, Aakanshi; Misra, Sanjay; Suri, Bharti; Kumar, Vijay; Damasevicius, Robertas
The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Renyi and Tsallis entropy). By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE)). The values of model performance statistics (R-2, adjusted R-2, Mean Square Error (MSE) and standard error) also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers.
Spaces of Technology, Spaces of Reconciliation: The Railway Setting of Mugby Junction in “Barbox Brothers” and “Barbox Brothers and Co.”
(Ovidius University, 2025) Elbir, Belgin
Some Results Related to the Laplacian on Vector Fields
(Kossuth Lajos Tudomanyegyetem, 2006) Erkekoǧlu, Fazilet; Kupeli, Demir N.; Übnal, Bülent
Prognostic Value of the C-Reactive Protein-Albumin-Lymphocyte (CALLY) Index for 1-Year Mortality After Transcatheter Aortic Valve Implantation
(MDPI, 2026) Guney, Murat Can; Suygun, Hakan; Turinay Ertop, Zeynep Seyma; Polat, Melike; Bozkurt, Engin; Ayhan, Huseyin; Keles, Telat
Objectives: Systemic inflammation, malnutrition, and immune dysregulation have emerged as important determinants of long-term outcomes after transcatheter aortic valve implantation (TAVI). The C-reactive protein-albumin-lymphocyte (CALLY) index is a novel immunonutritional biomarker that integrates these pathophysiological domains; however, its prognostic value in TAVI patients has not yet been investigated. This study aimed to evaluate the association between the CALLY index and 1-year mortality after TAVI. Methods: This retrospective observational study included 532 consecutive patients who underwent TAVI at a tertiary-care center between 2014 and 2023. Baseline laboratory parameters were obtained before the procedure, and the CALLY index was calculated as (albumin & times; lymphocyte count)/(C-reactive protein & times; 10). The primary endpoint was 1-year mortality. Receiver operating characteristic (ROC) curve analysis was performed to assess the discriminative ability of the CALLY index and conventional surgical risk scores. Multivariable regression analyses were used to identify independent predictors of mortality. Results: During the 1-year follow-up period, 85 patients (15.9%) died. Patients who died had significantly lower baseline CALLY index values compared to survivors (p < 0.001). The CALLY index demonstrated good discriminative performance for 1-year mortality (AUC: 0.797), outperforming EuroSCORE II (AUC: 0.705) and the Society of Thoracic Surgeons (STS) score (AUC: 0.619). A CALLY cut-off value of 0.45, derived using Youden's index, was associated with a more than threefold increased risk of mortality. In multivariable analysis, the CALLY index remained independently associated with 1-year mortality, along with EuroSCORE II and more than mild mitral regurgitation. Conclusions: The CALLY index is a strong and independent predictor of 1-year mortality after TAVI and provides incremental prognostic value beyond conventional surgical risk scores. Given its simplicity and reliance on routinely available laboratory parameters, the CALLY index may serve as a practical tool for long-term risk stratification in patients undergoing TAVI.
Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug
(MDPI, 2019) Misra, Sanjay; Kumari, Madhu; Misra, Ananya; Damasevicius, Robertas; Fernandez Sanz, Luis; Sanz, Luis Fernandez; Singh, V. B.
A software bug is characterized by its attributes. Various prediction models have been developed using these attributes to enhance the quality of software products. The reporting of bugs leads to high irregular patterns. The repository size is also increasing with enormous rate, resulting in uncertainty and irregularities. These uncertainty and irregularities are termed as veracity in the context of big data. In order to quantify these irregular and uncertain patterns, the authors have appliedentropy-based measures of the terms reported in the summary and the comments submitted by the users. Both uncertainties and irregular patterns have been taken care of byentropy-based measures. In this paper, the authors considered that the bug fixing process does not only depend upon the calendar time, testing effort and testing coverage, but it also depends on the bug summary description and comments. The paper proposed bug dependency-based mathematical models by considering the summary description of bugs and comments submitted by users in terms of the entropy-based measures. The models were validated on different Eclipse project products. The models proposed in the literature have different types of growth curves. The models mainly follow exponential, S-shaped or mixtures of both types of curves. In this paper, the proposed models were compared with the modelsfollowingexponential, S-shaped and mixtures of both types of curves.
Post-Hoc Mixture Models to eBLUPs from Linear Mixed-Effects Models: A Tractable Approach for Clustering Irregular Longitudinal Data
(Taylor & Francis Ltd, 2026) Balakrishnan, N.; Hossain, Md Jobayer
Clustering longitudinal data with irregular and sparse measurement schedules has become important in analyzing many medical data and associated decision-making. These datasets often involve observation times that vary across individuals, making trajectory-based analysis essential for uncovering meaningful patterns. Mixture-based linear mixed-effects models, such as heterogeneous linear mixed-effects models and growth mixture modeling, are commonly used for this purpose. While theoretically powerful, these methods often suffer from convergence issues and computational inefficiency in large-scale applications. This study introduces a computationally efficient two-step approach that applies a post-hoc mixture model to empirical Best Linear Unbiased Predictors (eBLUPs), derived from a fitted (piecewise) linear mixed-effects model under homogeneity assumptions. The method is then demonstrated with real clinical data, in which it effectively identified distinct growth trajectories in early childhood data involving 3,365 children across 51,711 clinic visits. The optimal number of clusters is then selected using the BIC, likelihood ratio tests, and model-based validation, achieving the best balance of model fit, classification stability, and interpretability. Simulation studies have shown that eBLUPs preserve individual-level heterogeneity and that post-hoc mixture modeling outperforms HLME across varying separability. Overall, this approach offers a robust, interpretable, and scalable alternative to traditional clustering methods for irregular longitudinal data.
Prestressed Concrete Technology in Underdeveloped Countries
(ISEC Press, 2018) Lokce, Sevgi; Goksu, Ersan
Nonlinearity and Structural Breaks in Oil Prices: Policy Implications and Macroeconomic Interactions
(Walter de Gruyter GmbH, 2026) Omay, Tolga; Sungur, Nazli Ceylan
This study examines Brent crude oil price dynamics using an integrated framework of bootstrap sequential break detection and Asymmetric Exponential Smooth Transition Autoregressive (AESTAR) modeling. We demonstrate that oil prices follow an AESTAR process where structural breaks emerge endogenously through dual transition functions, reconciling previously competing explanations in the literature. Analysis of monthly data (1985-2023) identifies major structural shifts coinciding with critical economic events, while revealing these breaks emerge automatically through regime-dependent means. Enhanced testing confirms embedded LSTAR-dominant dynamics with ESTAR components, while skeleton analysis validates the dual equilibrium framework with balanced regime distribution. Generalized Impulse Response Function analysis reveals distinct shock transmission patterns: Tier 1 extreme events (delta max > 1.8) exhibit persistent deviations requiring sustained policy intervention, while Tier 2 events demonstrate mean reversion properties suitable for conventional responses. The framework provides observable threshold levels ($53.62, $37.39) enabling real-time policy intervention, supporting regime-contingent monetary policy and strategic petroleum reserve management protocols. This approach offers policymakers actionable tools for managing oil price volatility through empirically validated intervention strategies.
Multi-State Linear Three-Dimensional Consecutive k-Type Systems
(Cambridge Univ Press, 2026) Yi, He; Balakrishnan, Narayanaswamy; Li, Xiang
Consecutive $k$-type systems have become important in both reliability theory and applications; in spite of a large literature existing on them, three-dimensional consecutive $k$-type systems have not yet been studied for multi-state case. In this paper, we introduce several different types of multi-state linear three-dimensional consecutive $k$-type systems for the first time, with due consideration to possible overlapping of failure blocks. The finite Markov chain imbedding approach is then used for the derivation of their reliability functions with state spaces and transition matrices provided in a novel way, and the involved computational process is illustrated through several numerical examples. Finally, some possible applications of the work and potential extensions are pointed out.
Memory, Narrative, and Collective Gendering of Identity: Revolutionary Women in Turkey
(Cambridge Univ Press, 2026) Bektas, Eda; Sensonmez, Gokhan
This article examines the construction of gendered collective identity among leftist women in Turkey through their post-1980 coup prison memory. By analyzing 124 autobiographical narratives, we uncover a process of identity formation grounded in a continuous negotiation between past struggles and present concerns, constituting a counternarrative that challenges the master narrative of defeat and submission prevalent after the coup. The article's tripartite framework of distance, substance, and persistence underscores women's journey from marginalization to collective empowerment, producing shifting subject positions across time. By placing temporality at the center of collective identity formation, this study contributes to feminist memory literature and identity studies while addressing a significant historiographical gap by bringing the neglected struggles of leftist women in Turkey to light.
Machine Learning and Scenario-Based Forecasting of Türkiye’s Renewable Energy Transition toward Net-Zero 2053
(Elsevier, 2026) Sutcu, Muhammed; Yildiz, Baris; Sahin, Nurettin; Almomany, Abedalmuhdi; Gulbahar, Ibrahim Tumay
The issue of global warming has been identified as one of the most critical challenges of the 21st century, with the consumption of fossil fuels being identified as a major contributor to greenhouse gas emissions. In response to these challenges, countries worldwide are expediting their transition towards renewable energy sources to meet international climate commitments, such as the Paris Agreement, and to achieve long-term sustainability goals. Türkiye has established a target to achieve net-zero emissions by 2053. This objective is consistent with both the nation's domestic energy strategy and its international commitments. Nevertheless, the transition from fossil fuels to renewable energy sources is impeded by geographical, economic, and technological constraints. The present study aims to assess the capacity and efficiency of renewable energy in Türkiye with environmental protocols and future electricity demand projections. Electricity generation, transmission data, and national energy plans are used to identify future electricity generation and capacity trends. In the context of this study, a range of machine learning models is executed across diverse scenarios, yielding a series of outcomes. Consequently, the repercussions of regulatory measures and financial investments were examined, and prospective inferences were derived. The findings underscore the pivotal role of scenario-based modeling in formulating sustainable energy policies and directing investment decisions within the context of climate change mitigation.
Malignancy in Toxic Thyroid Adenoma: Revisiting Risk Assessment and Identifying Predictors
(Springer, 2026) Calapkulu, Murat; Cayir, Derya; Sencar, Muhammed Erkam; Cakal, Erman; Sakiz, Davut; Unsal, Ilknur Ozturk; Tekinyildiz, Merve
Background Toxic adenomas have traditionally been considered benign due to chronic TSH suppression, which is believed to inhibit thyroid tumorigenesis. However, emerging data challenge this dogma, reporting non-negligible malignancy rates even in toxic adenoma. This study aimed to assess thyroid cancer frequency and characteristics in surgically selected patients with toxic adenomas and to compare outcomes with propensity score-matched, surgically treated non-functioning nodules. Methods This retrospective, cross-sectional study included 204 surgically treated patients at a tertiary referral center, comprising 102 surgically selected toxic adenomas and 102 propensity score-matched, surgically treated non-functioning nodules. Clinical, biochemical, sonographic, scintigraphic, and histopathological data were analyzed. Multivariate logistic regression analysis was used to identify independent predictors of malignancy among toxic adenomas. Results In this surgically selected cohort, the malignancy rate was 16.7% for toxic adenomas and 40.2% for non-functioning nodules (p < 0.001). Papillary thyroid carcinoma comprised 82.4% of all cases, making it the leading histotype (82.4%). Among toxic nodules, higher fT4/fT3 ratio (cut-off:2.58, sensitivity:93.3%, specificity:54.2%) and European Thyroid Imaging and Reporting Data System categories 4-5 were independent predictors of malignancy. No significant differences were found between groups in terms of tumor size, invasion, American Thyroid Association risk stratification, or 5-year response rates. Conclusion Among surgically treated patients, the observed malignancy rate in toxic adenomas appears to be higher than traditionally expected. Elevated fT4/fT3 ratio and suspicious ultrasound features warrant closer evaluation. These findings support using ultrasound and biochemical markers in risk assessment of all thyroid nodules, regardless of functional status.
Kavaklidere-Ankara: The Formation of a Residential District during the 1950S
(Open House International Association, 2015) Resuloğlu, Çilga; Ergut, Elvan Altan
Investigating the Sheltering Effect of Sustainability Indices During the Coronavirus Crash
(Mesut Dogan, 2023) Tosun, Burcu; Özgen, Pelin
Investigation of Localized Levels in GaS0.5Se0.5 Layered Crystals by Means of Electrical, Space-Charge Limited Current and Photoconductivity Measurements
(Wiley-VCH Verlag GmbH, 2002) Qasrawi, AF; Gasanly, NM
To identify the localized levels in GaS0.5Se0.5 single crystals, the dark electrical conductivity, current-voltage characteristics and photoconductivity measurements were carried out in the temperature range of 250-400 K. Temperature dependence of dark electrical conductivity and the space-charge limited current studies indicate the presence of a single discrete trapping level located at 0.31 eV below the conduction band with a density of about 1.3 x 10(15) cm(-3). The conductivity data above 320 K reveal an additional donor level with activation energy of 0.40 eV indicating the extrinsic nature of conduction. The spectral distribution of photocurrent in the photon energy range of 0.65-5.9 eV reveals an indirect band gap of 2.26 eV. The photocurrent-illumination intensity dependence follows the law I-ph proportional to F-gamma, with gamma being 1.0, 0.65, and 0.5 at low, moderate and high illumination intensities, respectively. The corresponding behavior indicates the domination of monomolecular recombination, near equal densities of trapped and recombination centers and bimolecular recombination. It is observed that the photocurrent increases in the temperature range from 250 K up to T-m = 360 K and decreases for T > T-m. The temperature dependence of the photocurrent reveals two additional impurity levels with activation energies of 0.14 and 0.10 eV below and above Tm, respectively.
Impact of the Severity of the COVID-19 Pandemic on the Interaction Between Foreign Trading and Stock Market Volatility
(World Scientific, 2022) Kantur, Zeynep; Özcan, Gülserim

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