Ertuğrul Karaçuha

The aviation Maintenance, Repair and Overhaul (MRO) sector faces an acute tacit knowledge continuity challenge as a generation of master technicians approaches retirement, yet existing knowledge management (KM) frameworks offer no tools calibrated to aviation's safety critical, highly regulated operating environment. Addressing this gap, this paper develops a conceptual framework: the GenAI SECI MRO Framework that integrates the SECI model of knowledge creation with emerging generative artificial intelligence (GenAI) capabilities. Following a PRISMA guided systematic literature review of research at the KM, aviation, and AI intersection, the study identifies four critical gaps and translates them into the framework's design requirements. The framework reconceptualises each SECI phase through GenAI augmented mechanisms operating within EASA/FAA regulatory compliance boundaries and embeds a regulatory validation layer as a co‐equal design constraint. Theoretically, the study extends the SECI model to high reliability organisational (HRO) contexts and draws on the resource based view (RBV) to position institutional knowledge as a strategic asset. This is a conceptual framework development study: the proposed mechanisms are theoretically grounded propositions that require empirical validation in operational MRO settings, for which a structured research agenda is provided. The framework offers KM scholars a sector specific extension of knowledge creation theory and gives MRO managers a structured roadmap for knowledge continuity.

This study proposes a two-variable fractional calculus–based modeling framework for analyzing gross domestic product (GDP) growth rates using key macroeconomic indicators of G8 countries and Türkiye over the period of 1998–2022. The economic variables considered include exports, imports, inflation, foreign direct investment, and unemployment rates, with data obtained from the World Bank. Caputo-type fractional derivatives combined with the least squares method are employed to construct two-variable economic models, where GDP growth rate is treated as the dependent variable. Multiple combinations of economic factors are systematically examined to evaluate their modeling performance. The accuracy of the proposed models is assessed using the Mean Absolute Percentage Error (MAPE). The results indicate that the export–inflation combination yields the lowest modeling error for Japan, while the inflation–unemployment combination produces the highest error for Russia. Overall, the findings demonstrate that fractional calculus–based bivariate models provide an effective and flexible framework for capturing nonlinear and memory-dependent dynamics in economic growth analysis.

Aviation is one of the most global industries, and if we can model and predict a country’s air transportation flow and indicators ahead of time, we may be able to use it as a key decision-making tool for the management and operation process. This study proposes a new modeling, and prediction method that employs both fractional calculus and Multi Deep Assessment Methodology (MDAM) techniques. For the application, air passengers carried, air freight, available seat kilometers, number of flights, destination points, international travelers, international destination points, and international flight data between 2011 and 2019 for eight countries with the busiest airports were chosen. As a result, the highest modeling error was discovered to be Germany’s air transport freight factor expressed as a percentage of 1,59E-02. The percentage of predictions with errors less than 10% was 90.278. We also compared the performance of two different MDAM methodologies. The novel MDAM wd methodology proposed in this paper has a higher accuracy in aviation factors prediction and modeling.

This study addresses the challenge of predicting the passenger load factor (PLF) in air transportation to optimize capacity management and revenue maximization. Leveraging historical reservation data from 19 Turkish Airlines market routes and sample flights, we propose a novel approach combining deep assessment methodology (DAM) with fractional calculus theory. By modeling the relationship between PLF and the number of days remaining until a flight, our method yields minimal errors compared to traditional techniques. Through a continuous curve constructed using the least-squares approach, we enable the anticipation of future flight values. Our analysis demonstrates that the DAM model with a first-order derivative outperforms linear techniques and the Fractional Model-3 in both modeling capabilities and prediction accuracy. The proposed approach offers a data-driven solution for efficiently managing air transport capacity, with implications for revenue optimization. Specifically, our modeling findings indicate that the DAM wd model improves prediction accuracy by approximately 0.67 times compared to the DAM model, surpassing the fractional model and regression analysis. For the DAM wd modeling method, the lowest average mean absolute percentage error (AMAPE) value achieved is 0.571, showcasing its effectiveness in forecasting flight outcomes.

Today, operators have become the resource of telecommunications data. Therefore, knowledge about subscriptions is easily available for most countries. In recent years, high-speed mobile internet access subscriptions are also increasing rapidly. There are two important subscriptions reviewed by The International Telecommunication Union (ITU): mobile broadband (MBB) subscriptions and Fixed broadband (FBB) subscriptions. In this study, we proposed an original mathematical model employing the fractional analysis theory and evaluate its validity by modeling the mobile broadband and fixed broadband subscriptions of six countries including France, Italy, Turkey, Germany, Spain, and the U.K. Later, we compared the Fractional Model we developed with the Polynomial Model. The results show that the Fractional Model is superior to the conventional Polynomial Model in modeling broadband subscriptions. For all selected countries, our proposed Fractional approach outperforms conventional polynomial regression. For all investigation categories, on average, the fractional approach works better by at least 10% and at most 30%.

This study focuses on modeling the daily deaths per new case of COVID-19 by using the Fractional Calculus and the Least Squares Method. Based on our prior work, we proposed a new modeling approach, assessed the strength of outbreak response, and analyzed possible underlying factors of the outbreak for 8 countries including China, France, Germany, Italy, Russia, Spain, the UK, and the US. First, we modeled weekly deaths per new case of COVID-19 using our new modeling method Deep Assessment Methodology - Second Derivative (DAM-SD). Later, we defined a performance indicator to understand how well each country copes with the pandemic. Lastly, Pearson correlations between the performance indicator and several economic, social, and environmental indices, such as Human Development Index, Human Freedom, Democracy, Competitiveness, and Trust Index are computed, and p-values are reported. Results showed that DAM-SD successfully models the daily new cases of COVID-19 with 3.7390% Mean Average Precision Error and outperforms the DAM by 1.5678% MAPE. China is the best-modeled country with 4.0975e-08% MAPE whereas the model produced the highest error rate for France as 8.8317% MAPE. According to the analysis with the performance indicators, China is the most successful country against the pandemic while the United States and France fail to confine COVID-19 outbreak compared to the others. Indicators such as Human Development Index, Human Freedom, Human Democracy, GINI Index, Workers Rights, the Trust Index, and Air Pollution are found significant for COVID-19 response according to the p-values. In the correlation analysis, Average Class Size, Government’s Long Term Vision, Responsiveness to Change, Better Life Index, and Population Density were the least significant indicators. Long Term Care Beds, Social Capital, and Global Social Mobility indicators are found correlated with the COVID-19 response. Household Spending and Student Skills are found insignificant.

This study explores the modeling of the share of telecommunication revenues in gross domestic product from the year 2000 to 2018 for 5 countries including France, Germany, Italy, Turkey, the UK, and the OECD average. First, a new mathematical model based on Fractional Calculus and Least Square Method is proposed. Later, the telecommunication revenues in GDP dataset is modeled. Further, we compare the new Fractional approach to the classical Polynomial approach in three different settings. The results show that employing Fractional Calculus yields better modeling performance when compared to the classical Polynomial Approach in terms of Mean Absolute Percentage Error (MAPE). The Fractional approach outperforms the Polynomial approach by 0.1329 % MAPE on average. The largest MAPE is found for Turkey while the smallest MAPE is obtained for Italy in all settings.