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  Fueling the bottom line: Decoding the effects of oil on banking performance in net oil-importing economies

  (Elsevier, 2024) Çıkıryel, Burak; Savaşan, Fatih; Nagayev, Ruslan; Görmüş, Şakir

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  Banks play a pivotal role in the financial sector, assuming critical functions such as facilitating the monetary policy transmission mechanism and acting as intermediaries between savers and borrowers. Meanwhile, oil represents a fundamental input for economic activities, and its inherent volatility serves as a significant catalyst for economic instability. Given the critical roles of banking institutions and oil in the economy, their relationship garners attention from various stakeholders. The growing body of literature has examined the nexus between oil and banking performance. However, existing research has predominantly concentrated on either oil-exporting jurisdictions or country-specific analyses. Hence, the present study endeavors to bridge this gap in the litera-ture by investigating the intricate dynamics between oil and banking performance, specifically in net oilimporting countries. The dynamic panel method is employed. The findings indicate that oil has direct and in-direct effects on the profitability of banks operating through transmission channels.

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  Enhancing GPS Accuracy with Machine Learning: A Comparative Analysis of Algorithms

  (INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC, 2024) Zontul, Metin; Ersan, Ziya Gökalp; Yelmen, İlkay; Çevik, Taner; Anka, Ferzat; Gesoğlu, Kevser;

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  In the realm of wireless communications, the Global Positioning System (GPS), integral toGlobal Navigation Satellite Systems (GNSS), finds extensive applications ranging fromvehicle navigation to military operations, aircraft tracking, and Geographic InformationSystems (GIS). The reliability of GPS is often compromised by errors particularly prevalentin dense and structurally complex environments, where signal attenuation by environmentalobstacles like mountains and buildings is common. These challenges necessitate thedeployment of high-cost, precision GPS receivers capable of enhanced signal tracking andacquisition. This study investigates the reduction of GPS positioning errors by implementinga machine learning framework, utilizing a dataset from vehicle tracking devices equippedwith Novatel and Ublox technologies. Ten machine learning prediction algorithms wereevaluated, focusing on techniques that introduce randomness for stability, employ proximityfor predictions, incorporate regularization to prevent overfitting, and leverage both singleand ensemble methods to refine analyses. Among the evaluated algorithms, the Extra Treesalgorithm was distinguished by its superior performance, achieving a coefficient ofdetermination (R²) of 99.6%, with the lowest error rates compared to its counterparts. Theerrors were quantified as Root Mean Square Error (RMSE) at 1.01E-4, Mean Absolute Error(MAE) at 4.14E-5, and Mean Square Error (MSE) at 1.03E+0 for normalized data. Acomparative assessment across ten scenarios demonstrated that the machine learning-enhanced approach deviated by approximately 6.8 meters on average, markedly improvingaccuracy over traditional GPS methods and reducing positional deviations to a scale ofmeters. This advance represents a significant stride towards minimizing GPS inaccuraciesin complex environments, providing a robust framework for enhancing navigationalprecision in critical applications.

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  A Pandemic's grip: Volatility spillovers in Asia-Pacific equity markets during the onset of Covid-19

  (Elsevier, 2024) Salim, Kinan; Dişli, Mustafa; Nagayev, Ruslan; Ilyas, Abubakar; Aysan, Ahmet F.

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  The emergence of Covid-19 in late 2019 rapidly shattered the Asia-Pacific region (APR), a bastion of economic dynamism, and it became the epicenter of the global health crisis. This unprecedented pandemic not only triggered a public health catastrophe but also unleashed a financial storm, exposing vulnerability within the region’s interconnected economies. This study identifies the factors driving volatility spillovers within AsianPacific financial markets during the initial wave of the Covid-19 pandemic (January 2020–February 2021). We analyze the interplay of pandemic transmission dynamics, government interventions, central bank policies, and socioeconomic variables. Our findings reveal a robust and persistent association between the rising number of Covid-19 cases per million and volatility spillovers. We introduce three novel determinants—the number of intensive care unit beds, population density, and the proportion of the elderly population—which significantly impact volatility transmission in response to new cases. Stringent government measures, such as travel bans and lockdowns, mitigate volatility spillovers. Conversely, central bank policies increase volatility spillovers. These insights contribute to a deeper understanding of financial market dynamics in the context of global health emergencies. This knowledge equips policy makers in the APR with valuable tools for navigating future crises.

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  Bridging Educational Research and Teacher Performance: Multiple Mediation Analysis of Professional Development and Teacher Leadership

  (Routledge, 2025) Özgenel, Mustafa; Yalçın, Elif; Yazıcı, Şebnem; Taktak, Mustafa; Uysal, Orhan Kadir; Aşmaz, Adem; Aydoğan, İlker; Özgenel, Mustafa

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  his study explores the mediating roles of professional development and teacher leadership in the relationship between educational research and teacher performance. Data from 465 Turkish teachers were analyzed using a multiple mediation model. Results show that educational research posi-tively influences teacher performance directly and indirectly through profes-sional development and leadership, enhancing overall performance. Significant connections between educational research, professional develop-ment, and leadership are highlighted. The findings suggest that integrating educational research with teacher development and leadership can improve teacher performance, providing valuable insights for educational leaders and policymakers focused on enhancing teacher effectiveness.

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  Investigation of glutamic acid production capacity of Stenotrophomonas sp. strain CG2 isolated from soil

  (Elsevier, 2025) Güner, Cihat; Ermiş, Ertan; Özkan Güner, Kübra; Ermiş, Ertan

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  Glutamic acid is a widely used amino acid in the food and pharmaceutical industries due to its role as a flavor enhancer and a metabolic precursor. This study aimed to identify glutamic acid-producing bacterial strains from soil samples collected across different regions. Among 262 isolates screened, Stenotrophomonas sp. strain CG2 exhibited the highest production capacity and was identified through 16S rRNA sequencing. Fermentation parameters including pH, temperature, incubation time, and agitation speed were optimized using the Plackett–Burman design, leading to a maximum yield of 3.76 ± 0.65 g/L under optimized conditions (pH 7.0, 30 °C, 200 rpm, 84 h), compared to 2.72 g/L in unoptimized TSB medium. The produced glutamic acid was purified using ion-exchange resin, yielding a recovery efficiency of 53.48 ± 3.28 %, and its identity was confirmed by FT-IR, RAMAN, and LC-MS/MS analysis. This study is among the first to systematically explore Stenotrophomonas spp. for glutamic acid biosynthesis under optimized fermentation conditions. The results provide insight into the strain's specific responses to nutrient composition, revealing its potential for future biotechnological applications. By expanding the microbial landscape of amino acid producers, this work offers a foundation for using CG2 in sustainable bioproduction processes, particularly those leveraging food or agro-industrial waste streams.

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