DSpace Arşivi :: Ana Sayfa

İstanbul Gelişim Üniversitesi Kurumsal Açık Erişim Arşivi

DSpace@Gelişim, İstanbul Gelişim Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.

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A CMIP6-Based Frequency Analysis of the Extreme Precipitation Events in Virginia, USA
(Elsevier, 2026) Hussein, Sura Dhiyaa; Balov, Mustafa Nuri
In recent years, as a result of climate change and global warming, there have been noticeable increases in the number and severity of extreme climate events. The primary reason for these increases is the enhanced water holding capacity of the atmosphere caused by rising temperatures. Climate change, along with other factors such as changes in land use, has a major role in floods becoming more frequent and severe around the World. In this regard, it is important to investigate and model the effects of changes that will occur in the forthcoming years on extreme precipitation. In this study, the outputs of five new general circulation models (GCM) used to analyze future extreme precipitation events under two new socioeconomic scenarios (SSP2-4.5 and SSP5-8.5). The daily precipitation values of five stations in Virginia state,Unites States, measured between 1985 and 2014 were used to correct the biases in GCMs' outputs by using linear scaling method. Frequency analyzes were established on partial-duration series obtained by using two different methods known as annual maximums and peaks-over-threshold. The partial duration time series were fitted to appropriate distribution functions. Then, the intensity of extreme precipitation events with 2, 5, 10, 20, 50, 100, 500, and 1000 years return period was calculated for 1985-2014 (reference period), 2015-2040 (near future), 2041-2070 (med-term future), and 2071-2100 (late future) periods. The intensity of the extreme precipitation events was projected to decrease during the near future and increase significantly by the end of the century, according to the results of this study. The increase is more remarkable for SSP5-8.5 scenario in comparison to SSP2-4.5 scenario. These results highlight the importance of low-emission scenarios in reducing natural disasters.
Business Continuity Perception in Gastronomy Businesses: A Quantitative Study on Corporate Capacity, Digital Infrastructure, and Operational Resilience
(Inderscience Publishers, 2026) Doğan, Murat
This study aims to quantitatively measure the perception levels of business continuity among managers in the Turkish foodservices sector. A survey was administered to 315 managers using the perceived organisational business continuity (POBC) scale adapted for the gastronomy sector. The findings show that business continuity perception is significantly higher in businesses with international management structures, strong IT infrastructure, and high personnel capacity. Small-scale businesses, however, remain operationally vulnerable to crises. Furthermore, the responsibility for food safety did not have a significant impact on perceptions of business continuity. The findings reveal that business continuity should be addressed not only procedurally but also as a management approach based on corporate maturity and digital competence. This research enriches the literature by confirming the theoretical validity of the scale in gastronomy; in practice, the findings suggest prioritising digital and corporate investments in business continuity strategies. Overall, business continuity management is vital for sectoral resilience.
Preface
(IGI Global Scientific Publishing, 2026) Kaya, Aslı
[ No Abstract Available ]
Artificial Intelligence (AI)-Based Business Model Strategies for Competitive Advantage
(IGI Global Scientific Publishing, 2026) Çelik, Derya
Artificial intelligence(AI) stands out as a game-changing force for competition in business. This chapter examines AI's role in reshaping business models, its strategic impact on businesses, and its contribution to competitive advantage. By integrating AI, firms transform their operations,customer interactions, and strategic decisions to increase their competitiveness. As data-driven decision-making becomes more critical, AI solutions increase the efficiency of businesses and improve customer experiences. This chapter discusses the benefits that the integration of AI into business models provides firms, such as operational efficiency, optimization of decision-making processes, and the promotion of innovation. Furthermore, the future long-term implications of AI-based business models were assessed concerning possible strategic opportunities, ethical issues, and legal responsibilities. The chapter provides a comprehensive review of the strategic advantages AI offers to businesses, demonstrating the potential of this technology to deliver a sustainable competitive advantage in business.
Black-Winged Kite Algorithm for Accurate Parameter Estimation in Photovoltaic Systems
(Multidisciplinary Digital Publishing Institute (MDPI), 2026) Mansour Elflew, Mouayed; Yahya, Khalid
This paper evaluates the efficacy of the Black-Winged Kite Algorithm (BKA) for parameter estimation in single-, double-, and triple-diode photovoltaic (PV) models. This study targets key electrical parameters, including photocurrent, reverse saturation current, series, and shunt resistances, and diode ideality factor(s) using experimental I-V data from an RTC France silicon cell. Performance is assessed using the root mean square error (RMSE) and convergence behavior and benchmarked against established metaheuristics including the Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), and Ant Lion Optimizer (ALO). The results show that BKA achieves competitive RMSE values with stable convergence for the investigated dataset. BKA employs coupled exploration and exploitation updates inspired by hunting and migration behaviors, and its limited number of control parameters supports straightforward deployment in nonlinear PV identification tasks. The results support BKA as a viable optimization option for PV model fitting in this setting, while also reflecting the typical trade-offs between search diversity and computational effort inherent to population-based methods.