WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Predicting Respiratory Infection and Symptoms Development Using Gene Set Enrichment Scores and Machine Learning(Elsevier Sci Ltd, 2026) Aydin, Zafer; Isik, Yunus EmreRecent advancements in precision medicine enable personalized predictions grounded in individual-level genetic data. However, relying solely on a single type of data can decrease prediction accuracy and limit the biological interpretability of the resulting models. Incorporating predefined genetic knowledge, such as derived gene sets, can improve performance and provide deeper biological insights for complex diseases, including respiratory infections. This study aimed to evaluate the usability of enrichment scores (ES), calculated using gene sets from the Molecular Signatures Database (MSigDB), as a feature representation for machine learning models to predict respiratory viral infections and symptom development. In addition, the proposed feature representation approach was extensively compared with the de facto gene-level expression representation. A total of 36,834 predefined gene sets were compiled from the MSigDB, and their ES values were calculated. Experiments used the GSE73072 dataset from Gene Expression Omnibus, containing gene expression profiles before and after virus exposure. Various machine learning and feature selection algorithms were applied to ES-based and probe-level feature sets. The results showed that both feature representation approaches achieved an area under the precision-recall curve (AUPRC) value greater than 0.90 for all tasks. Compared with the Respiratory Viral DREAM Challenge leaderboard phase, our models showed a 14.8% improvement in pre-exposure predictions (T0) and a 17.4% improvement in symptom classification. Using enrichment scores as a feature representation generally resulted in better performance than probe-level representation when predicting respiratory infections and symptom development. Identifying key gene sets through feature selection and comparing them with essential genes for respiratory viruses enabled a more comprehensive analysis, providing deeper insights into the pathways that contribute to these predictions.Article System-Level Design and Experimental Demonstration of a Laser Power Transfer System(Elsevier Sci Ltd, 2026) Boyekin, Tahsin; Sari, A. Yigit; Boynuegri, Ali Rifat; Yigit, Hayri; Herdem, Yusuf; Kalebasi, M. Talha; Kumru, Celal F.This study presents the design and experimental validation of a laser-based wireless power transfer (LPT) system that integrates electrical, optical, and control subsystems within a unified framework. The proposed setup consists of a silicon carbide (SiC)-based LLC resonant converter driving a high-power laser diode, an optical transmission link of 20 m, and a photovoltaic (PV) receiver equipped with a maximum power point tracking (MPPT) unit. The system was designed to evaluate the feasibility of stable, continuous optical power transfer under realistic alignment and thermal conditions. Experimental results demonstrate that the laser driver achieves an electrical efficiency of 95.96%, while the overall end-to-end LPT efficiency reaches 12.4% at a delivered power level of approximately 99 W. The findings confirm that coordinated design of the driver, optical path, and receiver sub systems can significantly improve operational stability and energy conversion consistency, offering a practical reference for the development of medium-range optical power transfer systems.Article Integrative Bioinformatics Prediction of West Nile Virus-Derived microRNAs Reveals Potential Host Regulatory Interactions(Elsevier Sci Ltd, 2026-08) Demirci, Muserref Duygu Sacar; Orhan, Mehmet Emin; Erginkoc, Altay Nida; Saçar Demirci, Müşerref DuyguWest Nile virus (WNV) is a mosquito-borne flavivirus linked to severe neuroinvasive disease. Although host and vector microRNAs (miRNAs) have been implicated in viral infection, the presence and functional relevance of WNV-encoded miRNAs remain largely unexplored. Here, we developed an integrative bioinformatics pipeline that combines multiple miRNA prediction algorithms with secondary structure screening and host transcriptomic data to identify high-confidence candidate WNV-derived mature miRNAs. Overlap-based confidence scoring and differential expression support from RNA-seq datasets prioritized a small subset of putative miRNA-mRNA interactions with potential roles in infection-associated gene regulation. A competitive endogenous RNA network constructed from predicted mRNA, lncRNA, and circRNA targets highlighted pathways involving innate immunity, GPCR and Wnt signaling, RNA degradation, and viral replication. Together, these findings provide a reproducible computational workflow and nominate testable regulatory interactions for future experimental validation.Article Citation - WoS: 2Citation - Scopus: 2Numerical Analysis and Experimental Comparison of Stress and Stiffness Parameters of Steel Reinforced Geopolymer Concrete Columns(Elsevier Sci Ltd, 2026-01) Ozbayrak, Ahmet; Kucukgoncu, Hurmet; Aslanbay, Huseyin Hilmi; Aslanbay, Yuksel Gul; Altun, FatihDespite extensive research, Geopolymer concrete (GPC) lacks reinforced concrete construction and design specifications. Developing such specifications requires comprehensive studies to promote the use of GPC, which is known for its superior performance and environmental benefits compared to ordinary Portland cement concrete (OPC). This study numerically investigated and compared the behavior and strength of fly ash-based geopolymer-reinforced concrete columns with the experimental results. Comparisons with OPC were made based on existing specifications. Herein, FEM analyses were conducted on 16 GPC and 4 OPC columns under eccentric axial compressive loads. Parameters such as eccentricity, reinforcement ratio, curing method, and activation solution ratios were varied. According to average numerical results, the GPC columns have 7% more moment capacity and 30% more curvature values than OPC. Moreover, GPC columns absorbed more energy than OPC columns. Also, GPC columns have higher axial load and bending moment carrying capacities than OPC for numerical results. Error analysis between FEM and experimental data revealed a strong correlation, with MAPE values of 8.88% (axial load) and 7.20% (moment) for GPC columns, confirming the reliability of the numerical model. ACI 318 and Eurocode 2 specifications were deemed applicable for GPC columns, provided axial loads are limited per TEC 2018.Article Citation - WoS: 42Citation - Scopus: 57What Are the Key Success Factors for Strategy Formulation and Implementation? Perspectives of Managers in the Hotel Industry(Elsevier Sci Ltd, 2020-08) Koseoglu, Mehmet Ali; Altin, Mehmet; Chan, Eric; Aladag, Omer FarukThis study investigates how hotel managers describe strategy and identify key success factors for its formulation and implementation. The study analyzes qualitative data collected through semi-structured interviews with property level top managers of hotels in Hong Kong. The findings show that hotel managers prioritize competition analysis and macro-environmental conditions over internal characteristics such as teamwork in strategy formulation. In the implementation phase, however, internal considerations such as employee involvement and strategic consensus are given prominence. This study provides a significant contribution by examining how top level practitioners in the industry interpret success factors in their strategic management efforts, and it highlights a largely neglected area in the hospitality and tourism management literature.Article Citation - WoS: 259Citation - Scopus: 288The Role of Institutional Quality and Environment-Related Technologies in Environmental Degradation for BRICS(Elsevier Sci Ltd, 2021-07) Hussain, Muzzammil; Dogan, EyupAn expanding body of literature has highlighted the environment-growth nexus. However, the literature is scarce on the role of environmental technologies and institutional quality in environmental pollution. The present study aims to contribute to the existing knowledge by utilizing environment-related technologies (ERT), institutional quality (IQ), and energy consumption to investigate ecological footprints (EF) as a proxy for the environment in BRICS economies in a framework based on environmental Kuznets curve (EKC) theory. By using data from 1992 to 2016, long-and short-term relationships are estimated through cross-section augmented autoregressive distributive lag model, augmented mean group estimator, and common correlated effects mean group. The second-generation econometric tools indicate that IQ and ERT negatively affect ecological footprints, thereby implying reductions in environmental degradation. The EKC hypothesis is not validated, implying that an increase in economic activities causes an increase in pollution. Overall, BRICS economies should improve their quality of institutions and enhance investments in environmental technologies to achieve a sustainable environment in the future. Findings are robust to practical policy implications. (c) 2021 Elsevier Ltd. All rights reserved.Article Citation - WoS: 14Citation - Scopus: 15The Role of Energy Efficiency, Renewable Resources, Green Innovation, and Fiscal Decentralization in Sustainable Development: Evidence From OECD Countries(Elsevier Sci Ltd, 2025-08) Binsaeed, Rima H.; Khan, Zeeshan; Dogan, Eyup; Rahim, SyedEnergy efficiency and renewable resources for sustainable development are novel discussion areas for academics and researchers. Similarly, most developed and emerging countries are experiencing fiscal decentralization to enhance regional development. However, the importance of these sectors in sustainable development is still unclear in the literature. This research investigates the influence of energy efficiency, renewable energy, green innovation, and fiscal decentralization on sustainable development. Using the data for 18 fiscally decentralized OECD countries from 1995 to 2020, the roles of linear and nonlinear green innovation and renewable energy are also considered. This study uses novel moment quantile regression and finds that revenue decentralization, expenditure decentralization, and fiscal decentralization are significant drivers of sustainable development. Additionally, energy efficiency and value-added manufacturing significantly enhance sustainability in the region. However, green innovation and renewables are resources that exhibit a U-shaped association with sustainable development. The robustness of these results is validated via a series of parametric and nonparametric approaches. From the policy perspective, this research suggests improved research and development on renewable energy, green innovation, and energy efficiency could significantly encourage the OECD's journey towards sustainable development. Additionally, subnational governments should be given more fiscal autonomy, which may encourage regional level investments and boost the confidence of clean energy producing sectors to accelerate sustainable regional development.Article Citation - WoS: 23Citation - Scopus: 32The Path of Least Resistance Explaining Tourist Mobility Patterns in Destination Areas Using AirBNB Data(Elsevier Sci Ltd, 2021-06) Turk, Umut; Osth, John; Kourtit, Karima; Nijkamp, PeterDestination attractiveness research has become an important research domain in leisure and tourism economics. But the mobility behaviour of visitors in relation to local public transport access in tourist places is not yet well understood. The present paper seeks to fill this research gap by studying the attractiveness profile of 25 major tourist destination places in the world by means of a 'big data' analysis of the drivers of visitors' mobility behaviour and the use of public transport in these tourist places. We introduce the principle of 'the path of least resistance' to explain and model the spatial behaviour of visitors in these 25 global destination cities. We combine a spatial hedonic price model with geoscience techniques to better understand the place-based drivers of mobility patterns of tourists. In our empirical analysis, we use an extensive and rich database combining millions of Airbnb listings originating from the Airbnb platform, and complemented with TripAdvisor platform data and OpenStreetMap data. We first estimate the effect of the quality of the Airbnb listings, the surrounding tourist amenities, and the distance to specific urban amenities on the listed Airbnb prices. In a second step of the multilevel modelling procedure, we estimate the differential impact of accessibility to public transport on the quoted Airbnb prices of the tourist accommodations. The findings confirm the validity of our conceptual framework on 'the path of least resistance' for the spatial behaviour of tourists in destination places.Article Citation - WoS: 65Citation - Scopus: 73The Nexus Between Poverty, Inequality and Environmental Pollution: Evidence Across Different Income Groups of Countries(Elsevier Sci Ltd, 2022-03) Ehigiamusoe, Kizito Uyi; Majeed, Muhammad Tariq; Dogan, EyupEven though the literature has extensively focused on a number of determinants of environmental pollution, it lacks to incorporate the importance of poverty and inequality on the environment. The nexus of poverty-inequality-environment is indeed in line with the agenda of the United Nations' Sustainable Development Goals. Furthermore, the existing studies usually rely on carbon dioxide (CO2) emissions as the proxy for the pollution in their analysis. This study fills the mentioned gaps by investigating the impacts of income inequality and poverty on environmental pollution using ecological footprint (a comprehensive measure of the pollution) in addition to CO2 emissions for 70 countries categorized by income groups. This research employs the dynamic panel system Generalized Method of Moments (GMM) and the Dumitrescu-Hurlin Granger causality techniques which are strong to several econometric issues that may frequently arise in the estimation procedures. The empirical outcomes show that income inequality and poverty increase carbon emissions and ecological footprint in the entire panel. However, when the panel is split into groups, the results indicate that income inequality mitigates carbon emissions and ecological footprint in high-income group but aggravates them in middle-income group. Though poverty has no significant impact on carbon emissions in high-income group, it raises the levels of carbon emissions and ecological footprint in middle-income group. This study overall implies that income inequality and poverty are significant determinants of environmental pollution. Hence, efforts to abate envi-ronmental degradation should give adequate attention to poverty and inequality in order to attain environmental sustainability.Article Citation - WoS: 69Citation - Scopus: 74The Nexus Between Global Carbon and Renewable Energy Sources: A Step Towards Sustainability(Elsevier Sci Ltd, 2023-09) Dogan, Eyup; Luni, Tania; Majeed, Muhammad Tariq; Tzeremes, PanayiotisThe energy transition is at the core of sustainable development as it helps to combat global warming and climate change. Similarly, carbon markets also support the climate change mitigation. Therefore, by realizing the potential role of clean energy and carbon markets in ensuring environmental sustainability, this study analyzes the spillovers and connectedness between the environment (global carbon) and renewable energy sources (wind, solar, geothermal, biofuel, and fuel cell). The empirical analysis is conducted by applying the novel "TVP-VAR" connectedness framework of Balcilar et al. (2021) on the daily data over the period from August 1, 2014, to February 4, 2022. The findings show that solar and biofuel appear as the highest net shock transmitter among alternative renewable sources while global carbon is shown as the net receiver of shocks. The largest transmission of shocks to global carbon is observed from wind followed by solar. Although these findings support the connectedness between renewable energy and the environment, however this connectedness is influenced by economic crises such as the oil crisis and pandemic crisis. During COVID-19, the fuel cell was the highest transmitter of shocks. The results are important for policy formulation, investment, and portfolio management as they provide insights into the interconnectedness and help in boosting climate actions.