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This study develops and evaluates a regionally calibrated magnitude scaling and early warning framework based on the ElarmS–EPIC algorithm using the 23 April 2025 Silivri (Istanbul) Earthquake (Mw = 6.2) scenario. A comprehensive dataset comprising the mainshock and its aftershocks was used to derive local regression relationships between earthquake magnitude (Mw) and the peak displacement amplitude (Pd) and predominant period (Tpmax) parameters. Replay simulations were conducted to assess real-time performance, and the results of the regional models were compared with those of the default EPIC configuration. The results indicate that the Pd-based magnitude estimation model produces faster and more stable results than the Tpmax-based approach, significantly improving accuracy and operational reliability. The region-specific Pd–Mw scaling provided higher consistency with catalog magnitudes compared to the default EPIC relationships. The calculated distribution of warning times shows that the system can provide actionable warning times of 5–9 s in districts near the epicenter (e.g., Silivri, Avcılar, Beylikdüzü) and 20–50 s in more distant districts and city centers (e.g., Kadıköy, Pendik, Bursa, Sakarya). These values demonstrate that a regionally optimized early warning system can provide critical decision-making time for automatic safety systems and emergency responses in the densely populated Marmara Region. Overall, this study emphasizes the importance of regional calibration in improving earthquake early warning (EEW) performance in Türkiye. The findings show that the success of EEW systems depends on station density, network latency, data transmission speed, processing capacity, and algorithmic optimization. The proposed Pd-based regional framework provides a scientifically robust and operationally applicable foundation for future EEW implementations in Istanbul and the Marmara Region.

Earthquake early warning systems aim to reduce the potential danger by providing a warning in the seconds before strong ground shaking occurs. In this study, we implemented EPIC, an early warning algorithm for Bursa province and its surroundings, which is a seismically active region. We replayed 104 earthquakes of M ≥ 3.5, which occurred in and around the study area between 2012 and 2021. We derived period and amplitude-based magnitude-scaling relationships using peak displacement amplitude (Pd) and predominant period (Tpmax) parameters of the first 4 s of P-wave arrivals. We investigated the performance of magnitude-scaling relationships through testing with real-time data. We observed an improvement when comparing the magnitude estimates made with the newly developed equations with the default equations used for California. We have also found that magnitude estimation with Pd gives better results than Tpmax for estimating the accurate final magnitude. We aim to adapt the EPIC early warning system, implemented for Bursa province and its surroundings, specifically for each region of Türkiye where the earthquake risk is high.

Earthquake early warning systems (EEWSs) are real-time seismology-based applications that purpose to reduce earthquake damage by taking some precautions for environments that are likely to be damaged. These systems are divided into two categories as on-site and regional. In this study, an on-site EEWS called ESenTRy, consisting of an accelerometer, GPS and communication components, has been designed. In this scope, a Windows operating system-based software has been developed using the .NET Framework and C# language. The developed software has the ability to read the Güralp Compressed Format strong motion data in real-time, compute the strong motion indices such as destructive intensity, real-time intensity and instrumental modified Mercalli intensity, issue an alarm in the target area and alert the defined Modbus clients. To test the reliability of ESenTRy, two simulations have been performed using the strong motion data belonging to the magnitude 7.7 Kahramanmaraş-Pazarcık earthquake that occurred in Türkiye on February 6, 2023, at 01:17:32 UTC and magnitude 5.9 Düzce-Gölyaka earthquake that occurred in Türkiye on December 23, 2022, at 01:08:15 UTC. In the test applications, depending on the distance between the stations and earthquake epicenters, the time difference between the Level-2 alarm trigger times and S-wave arrival times varied between 1.5 and 16 s, while the time difference between the Level-2 alarm trigger times and P wave arrival times varied between 0.5 and 29.5 s. The flowchart diagrams of the developed algorithm and results obtained from the simulations are presented in detail.

The aim of this 2-group study was to investigate the following question: Are there significant differences between scaffolded design-based learning controlled using 7 forms and teacher-directed instruction methods for the toy crane project on grade 7 students’ posttest scores on the simple machines achievement test, attitude toward simple machines, and attitude toward creativity in simple machines after adjusting for their respective pretest scores and the prior semester science grades? The study group (N = 65) consisted of grade 7 students in public middle schools in Burdur, Turkey. There were significant treatment effects favoring the first method with large effect sizes on both achievement and creative attitude scores. Teaching topics about simple machines with the design-based method increased students’ achievement and creative attitude without disadvantaging their attitude toward the topic as they constructed new knowledge through each step of the precise process.

The proposed Pd-based regional scaling model enhances the real-time performance of ElarmS-type EEW systems, improving alert reliability and response time across the Marmara Region. This study develops and evaluates a regionally calibrated magnitude scaling and early warning framework based on the ElarmS–EPIC algorithm using the 23 April 2025 Silivri (Istanbul) Earthquake (Mw = 6.2) scenario. A comprehensive dataset comprising the mainshock and its aftershocks was used to derive local regression relationships between earthquake magnitude (Mw) and the peak displacement amplitude (Pd) and predominant period (T[sub.p] [sup.max]) parameters. Replay simulations were conducted to assess real-time performance, and the results of the regional models were compared with those of the default EPIC configuration. The results indicate that the Pd-based magnitude estimation model produces faster and more stable results than the T[sub.p] [sup.max]-based approach, significantly improving accuracy and operational reliability. The region-specific Pd–Mw scaling provided higher consistency with catalog magnitudes compared to the default EPIC relationships. The calculated distribution of warning times shows that the system can provide actionable warning times of 5–9 s in districts near the epicenter (e.g., Silivri, Avcılar, Beylikdüzü) and 20–50 s in more distant districts and city centers (e.g., Kadıköy, Pendik, Bursa, Sakarya). These values demonstrate that a regionally optimized early warning system can provide critical decision-making time for automatic safety systems and emergency responses in the densely populated Marmara Region. Overall, this study emphasizes the importance of regional calibration in improving earthquake early warning (EEW) performance in Türkiye. The findings show that the success of EEW systems depends on station density, network latency, data transmission speed, processing capacity, and algorithmic optimization. The proposed Pd-based regional framework provides a scientifically robust and operationally applicable foundation for future EEW implementations in Istanbul and the Marmara Region.

Research background. Given the potential of microbial exopolysaccharides from lactic acid bacteria in various industrial processes, alternative sources for the isolation of lactic acid bacteria are highly topical. In this study, we used a traditional sourdough from einkorn (Triticum monococcum L. ssp. monococcum) as a source of lactic acid bacteria for the isolation, identification and determination of exopolysaccharide producers. Experimental approach. The sourdough was prepared from einkorn according to the traditional method. Lactic acid bacteria were isolated and purified using the single colony technique on MRS and M17 media. The isolates were characterised using matrix-assisted laser desorption ionization-time of flight mass (MALDI-TOF) spectrometry. All isolates were analysed for extracellular polysaccharide production and one isolate was selected for purification and characterisation of its polysaccharide. Results and conclusions. The isolates were identified as Lactobacillus plantarum, L. paraplantarum, L. brevis, Pediococcus pentosaceus, Enterococcus faecium and E. durans. The production of exopolysaccharides by all lactic acid bacteria was evaluated and it was found that all strains (except one) were capable of producing exopolysaccharides. One polysaccharide (EPS-SL70) was purified from the isolates of E. durans SL70. This anionic heteropolysaccharide had, in addition to the carbohydrate backbone, a protein structure that did not contain nucleic acid. The carbohydrate backbone consisted of mannose, glucose, rhamnose, arabinose, xylose and galactose. Novelty and scientific contribution. The microbial flora of traditional einkorn sourdough has been identified in this study and represents the first report on the exopolysaccharide production by lactic acid bacteria in traditional einkorn sourdough. Additionally, Enterococcus durans from einkorn sourdough was identified as a new exopolysaccharide producer.

In this study, the fermentation process of bioethanol production conditions were optimized by response surface methodology. Mulberry molasses production waste was used as the only carbon source for yeast fermentation to produce bioethanol. Hydrodistillation (HD) and ohmic-assisted hydrodistillation (OAHD) methods were employed to concentrate the bioethanol. Fermentation time (48–168 h), waste matter rate (5–45%) and pH (5–7) were selected as independent variables. Alcohol concentration was treated as the response. Optimum fermentation conditions were determined as 96.894 h fermentation time, 45% waste ratio and pH 7. At these optimum conditions, alcohol concentration was determined as 3.77 ± 0.33%. While the distillate obtained in the HD method contained 22.50 ± 1.89% alcohol, it showed 27.72 ± 0.24% in the OAHD method. The energy consumption values for OAHD and HD was 2.92 ± 0.51 and 53.24 ± 1.74 Wh/mL bioethanol, respectively. Results of study showed that the OAHD could be a cost-effective and green method to distillation of bioethanol from mulberry molasses waste.

The production of the extracellular polysaccharide from the thermophilic bacterium Aeribacillus pallidus was carried out in the study. The polysaccharide was isolated and characterized by means of GC–MS, FT-IR, DSC, and XRD analyses. The rheological, foaming, and emulsifying properties of the polysaccharide were determined. Using a sucrose-rich medium, 27.1 mg dried EPS/100 mL was obtained with 94% carbohydrate and 1.5% protein. The monosaccharide profile of water-soluble EPS-IM17 was composed of rhamnose, arabinose, xylose, mannose, glucose, and galactose. The foaming capacity and stability of EPS-IM17 were 26.67% (± 4.71) and 40.01% (± 4.95), respectively, and the foaming stability was not affected by time. The emulsification index of EPS-IM17 was 64.54 (± 8.71) and decreased to 38.47 (± 10.44) after 24 h. EPS-IM17 had a crystalline structure. Solutions at different concentrations (10, 20, 40 mgmL -1 ) showed pseudoplastic behavior. In conclusion, this report could be a lead study for the use of Aeribacillus pallidus extracellular polysaccharide for different applications.

This paper examines the impact of oil price fluctuations on a large set of stock market returns in net-oil importer countries and net-oil exporter countries. It applies multivariate cDCC-GARCH model, which has greater flexibilities, allowing the conditional variance covariance matrix of stock market returns to vary over time. Daily data spanning from January 2005 to February 2016 is used to obtain dynamic correlations between crude oil and stock market returns. Moreover, it employs the commonly recognized vector auto regression (VAR) specification and the corresponding Granger causality test in order to examine the linear relationship between crude oil and stock market volatility within each country, revealing whether there is a causal relationship between the variables in terms of time precedence. The influence of bullish and bearish market conditions is also measured by dividing the sample period into two sub-periods: Global Financial Crisis Period (2007–2010) and Post-Crisis Period (2010–2016). Main findings of this research indicate time-varying correlation of oil and stock prices for oil-importing countries is more pronounced than that for oil-exporting countries. This result shows that the correlation between the volatilities of stock market and oil price returns varies depending on the net position of the country in global oil market.