Middle East Technical University

This research focused on enhancing the recovery of bioactive compounds from pine nut skin (PNS) through two green extraction approaches: Microwave-assisted (MAE) and ultrasonic-assisted (UAE) techniques. The results showed that UAE outperformed MAE in terms of both total phenolic content (TPC) and antioxidant activity (AOA). Under optimized UAE conditions (10% ultrasonic power, 8.56 g/100 mL solid-liquid ratio, and 60% ethanol concentration), the extract exhibited significantly higher phenolic content (835.15 ± 0.28 mg GAE/g) and antioxidant activity (76.27 ± 2.25%) compared to MAE. The optimized PNS extract was applied to postharvest pine nuts, and the effect of extract and glycerol content were evaluated through oxidation analyses (peroxide value, thiobarbituric acid reactive substances (TBARS), p-anisidine value, and Totox value) during accelarated storage. Among the nine coating formulations tested, the G20E10 coating solution (containing 10% extract and 20% glycerol) was the most effective one in preventing oxidation and quality loss in postharvest pine nuts. It reduced peroxide, TBARS, and Totox value increases during storage by approximately 86%, 82%, and 91%, respectively, compared to the uncoated samples, thereby demonstrating a protective effect against lipid oxidation. The G20E10 coating significantly reduced lipid oxidation under accelerated storage conditions, indicating its potential as a natural approach to improve oxidative stability and maintain quality parameters of pine nuts.

We present a comprehensive coseismic landslide inventory for the 6 February 2023 Türkiye earthquake sequence, together with a companion pre-earthquake geomorphic inventory, covering an area of approximately 80,000 km². The earthquake sequence comprised two major events (Mw 7.8 and Mw 7.5) occurring nine hours apart, affecting 11 provinces and subjecting large mountainous regions to ground shaking levels capable of triggering slope failures (peak ground acceleration > 0.08 g). Given that nearly 15% of the affected terrain exhibits slopes steeper than 20°, extensive landsliding was anticipated, although early satellite observations were hindered by widespread snow cover immediately following the earthquakes. Landslide mapping was conducted through systematic, expert-based visual interpretation of high-resolution pre- and post-event optical imagery, including 29,085 post-earthquake aerial photographs (0.3 m resolution). A pre-event geomorphic inventory was generated using 5 m digital elevation model–based Red Relief Image Maps to identify pre-existing slope instabilities. Multi-temporal post-seismic optical image stacks were employed to overcome cloud and snow limitations and to discriminate coseismic landslides from failures initiated approximately one month later during an intense rainfall event; the latter were excluded from the coseismic inventory. Landslides were mapped as full-footprint polygons and classified according to movement type (fall, avalanche, slide, flow, lateral spread, and complex) and material (earth, debris, rock, and rock–debris). The final coseismic inventory comprises 20,270 landslides, predominantly rock falls and avalanches. Surface rupture through mountainous terrain locally generated large and, in some cases, fatal failures, while incipient landslides and ground cracking are widespread, particularly in northern sectors. Lithology, spatial variability of ground motion, and topographic relief emerge as primary controls on landslide distribution. This study provides one of the most detailed datasets of earthquake-triggered landslides in an arid-to-semiarid landscape, offering valuable insights for hazard assessment and landslide modeling in complex seismic environments.

Tsunami deposits are products of wave-train propagation triggered by dynamic sources and shaped by complex hydrodynamic processes. While the Arabian Gulf is traditionally characterized as a low-energy, shallow-marine environment, our findings present the first evidence of a high-energy, multi-bed tsunami succession discovered within the Miocene Dam Formation in southwest Qatar. The tsunami sequence consists of three beds (Beds 1, 2, and 3) separated by two intervening backwash horizons.Bed 1 marks the initial impact, unconformably overlying the sabkha facies, characterized by shale and evaporite alternation. This unit begins with wave-splash and traction-carpet structures, grading upward into a yellowish-gray sandy matrix containing pebbles and mollusk shells. Bed 2 represents a significant increase in wave energy, characterized by inverse grading and the presence of large, porous sandstone rip-up clasts and oversized mollusk shells. Bed 3 reflects the waning phase of the tsunami wave train, deposited as a suspension-load unit exceptionally rich in centimeter-scale echinoid fossils. Notably, the tsunami deposit exhibits a significant landward thinning geometry, tapering from a thickness of ~200 cm to only a few centimeters along an S to N transect.Paleocurrent analysis derived from sedimentary structures reveals a primary run-up direction towards the northwest, while backwash trends exhibit high variability, generally from NE to SE. Given the paleogeographic position of Qatar, these vectors possibly indicate that the active Zagros Thrust Belt was the main source of the tsunami. Despite the shallow bathymetry of the Arabian Gulf, the seismic potential of the Zagros Thrust Belt and the Makran Subduction Zone -either through direct seismic displacement or secondary submarine landslides- remains a critical threat to the region. This study provides an essential baseline for re-evaluating tsunami hazard potential in supposedly stable platform environments.Keywords: Arabian Gulf, Multi-bed Tsunami, Miocene Paleo-tsunami, Tsunami Wave-Train

Abstract In this Review article, a brief description of the stochastic mean-field (SMF) theory for describing reaction dynamics in low-energy heavy-ion collisions at bombarding energies in the vicinity of the Coulomb barrier is presented. In these collisions, as a result of strong Pauli blocking, binary nucleon collisions do not have a significant effect on the dissipation and fluctuations. At low energies, the mean-field fluctuations, due to initial correlations, have a dominant effect on fluctuations of macroscopic variables. The SMF theory proposes the determination of an ensemble of single-particle density matrices by specifying random initial fluctuations according to a distribution law. Employing an ensemble of single-particle density matrices, not only the mean values but also the distribution functions of the one-body observables can be determined. If the di-nuclear structure is maintained in heavy-ion collisions, such as deep inelastic collisions and fast quasi-fission reactions, a much simpler description of the reaction mechanism can be derived in terms of several macroscopic variables such as mass and charge asymmetry, and relative linear and relative angular momentum. In this case, by geometric projection of the SMF equations, it is possible to derive the quantal Langevin equations for macroscopic variables. As an application of quantal transport description, an analysis of multinucleon transfers and kinetic energy dissipation and fluctuations is presented for selected quasi-fission reactions.

Financial time series forecasting poses significant challenges due to the diverse risk profiles and dynamic behaviors of assets such as the S&P 500, NASDAQ, and Bitcoin, especially across different market periods. This study introduces a novel framework, waveletstochastic-chaos informed machine learning, that integrates wavelet transforms, stochastic processes, and chaos theory to improve machine learning prediction accuracy over a decade (2015–2025). The analysis is divided into four distinct periods: All Time, PreCOVID, COVID, and Post-COVID. The aim is to capture the multi-scale patterns, volatility, and complexity inherent in financial data, which will be assessed across various market conditions. The framework outperforms the baseline maximum likelihood models in most scenarios, achieving significant root mean squared errors for the scaled price predictions of S&P 500 (e.g., from 0.0348 to 0.0122 in All Time), NASDAQ (e.g., from 0.0284 to 0.0180 in All Time) and Bitcoin (e.g., from 0.0838 to 0.0288 in All Time) based on 1000 experimental trials. It excels in volatile periods like COVID and for high-risk Bitcoin, though it slightly underperforms in the stable Post-COVID recovery for S&P 500. Wavelet features are found to be critical for accuracy. Additionally, stochastic and chaos-based elements enhance performance in volatile and complex contexts, respectively, as confirmed by ablation studies. This study provides empirical evidence of predictive utility for financial time-series forecasting in assets with different dynamics and market regimes. The results indicate that multi-scale, stochastic, and complexity-based feature representations can improve forecasting performance within the examined datasets, suggesting that the framework may apply to other non-stationary time-series settings, although such extensions remain for future investigation.

This study investigates microplastic content in 79 personal care and cosmetic products (PCCPs)-shampoos, shower gels, peeling gels, and toothpastes-from various commercial brands in Türkiye, each analyzed through multiple extractions. Microplastics were identified using stereomicroscopy, Nile Red staining for fluorescence-based screening, and confirmed by FTIR spectroscopy. Shower gels contained the highest levels, while no microplastics were detected in toothpastes. Two types of microplastics -fibers and microbeads- were found, with fibers most common in shower gels (46%), followed by peeling gels (40%) and shampoos (14%). Polyethylene-based microbeads appeared only in one shower gel brand. Statistical analysis showed significant differences between product types (p < 0.05). FTIR analysis revealed that bead-like particles in peeling gels and toothpastes were primarily silica- or cellulose-based. Findings suggest a declining trend in plastic microbeads, possibly influenced by global awareness and policy changes. The study highlights the lack of standardized methodologies in PCCP microplastic research and calls for harmonized international regulations to reduce environmental contamination and ecological risks.