Search Results (703)

The durability and serviceability of steel-fiber-reinforced concrete (SFRC) tunnel linings in cold regions are significantly challenged by repeated freeze–thaw actions, making the accurate prediction of frost resistance a critical engineering problem. Although extensive research has been conducted on the freeze–thaw characteristics of concrete, the existing empirical and mechanism-based models remain limited in capturing the complex nonlinear interactions among mixture proportions, steel fiber characteristics, and environmental conditions. Therefore, a data-driven prediction framework based on machine learning was developed in this study. A database containing 277 groups of standardized SFRC freeze–thaw test results was established, incorporating key variables including mixture design parameters, fiber properties, and freeze–thaw cycle conditions. Four machine-learning models, namely, support vector regression, back-propagation neural network, gradient boosting, and extreme gradient boosting (XGB), were constructed and systematically compared. Model accuracy was assessed using MAE, MAPE, MSE, RMSE, and R². The results demonstrate that all models can reflect the nonlinear relationship between the input variables and mass loss rate, while the XGB model exhibits superior predictive performance with a testing R² of 0.91, representing an improvement of approximately 3–28% compared with other models. Meanwhile, the prediction errors are reduced significantly, with RMSE and MAE decreased by about 19–58% and 22–65%, respectively. The proposed approach provides an improved and reliable tool for predicting frost resistance and supports the durability design and optimization of SFRC tunnel linings in severe cold-region environments. Full article

Surface Acoustic Wave (SAW) magnetic sensors are traditionally fabricated on rigid substrates, which severely limits their application on curved or irregular surfaces. To address this critical limitation, this paper presents a novel flexible SAW magnetic sensor based on a grating-arrayed Terfenol-D thin film deposited on a 50 µm thick flexible lithium niobate (LiNbO₃) substrate. Unlike conventional designs using a continuous magnetostrictive layer, the proposed grating-arrayed structure is designed to aid in hysteresis compensation and minimize measurement errors associated with residual magnetization. As demonstrated experimentally, the sensors achieve a high sensitivity of 85.8 kHz/mT for devices with λ-wide gratings and a maximum frequency shift of 377 kHz at 5 mT. A systematic investigation reveals that sensitivity is critically dependent on the grating width and film thickness, with 500 nm thick gratings yielding optimal performance. Crucially, the sensor’s functionality under mechanical deformation is validated, and a differential measurement method is introduced to effectively compensate for stress-induced frequency shifts, ensuring reliable operation in practical, non-ideal conditions. The results confirm the sensor’s robust performance under the tested stress conditions, positioning this flexible SAW magnetic sensor as a promising solution for advanced, conformable sensing applications. Full article

Armenia is located within the central segment of the Arabia–Eurasia continental collision zone and is exposed to significant seismic hazard. This study presents an updated probabilistic seismic hazard assessment (PSHA) for Armenia based on an integrated seismotectonic framework incorporating active fault data, paleoseismological evidence, and historical and instrumental seismicity. A hybrid seismic source model was developed by combining fault-based characteristic earthquake sources with distributed background seismicity. Hazard calculations were performed using the OpenQuake engine within a logic-tree framework to account for epistemic uncertainties in earthquake occurrence and ground-motion prediction. Ground motion was estimated using a weighted set of ground motion prediction equations (GMPEs). Peak ground acceleration (PGA) hazard maps were computed for several return periods, with emphasis on the 475-year return period (10% probability of exceedance in 50 years). The results indicate PGA values across Armenia ranging from approximately 0.2 g to 0.5 g, with the highest hazard levels in northwestern Armenia along the Pambak–Sevan–Syunik Fault System. Hazard deaggregation shows that seismic hazard in major Armenian cities is primarily controlled by shallow earthquakes with magnitudes Mw 6.8–7.4 occurring within ~30 km of urban centers. The results provide a scientific basis for seismic hazard assessment, zonation, and earthquake risk mitigation in Armenia. Full article

This study explored the application of carbon stable isotopes for tracing the geographical origin of Monochamus alternatus, an insect vector responsible for spreading pine wilt disease. The primary vector of pine wilt disease, an aggressive disease caused by the pine wood nematode and affecting pine forests, is Monochamus alternatus. Samples of Monochamus alternatus were collected from 12 provinces across China, and their carbon isotope ratios (δ¹³C) were measured. By analyzing the correlation between these ratios and various environmental factors, including latitude, longitude, altitude, and bioclimatic conditions, it was found that precipitation seasonality and solar radiation were the most important factors influencing the carbon isotope ratio of Monochamus alternatus. The spatial distribution of Monochamus alternatus carbon isotopes in China was predicted using the co-Kriging interpolation method, incorporating these two environmental variables. The findings revealed a gradient in the carbon isotope ratio of Monochamus alternatus, which could help differentiate the species across various geographical regions in China. Full article

This study develops a system dynamics model to simulate the development of postgraduate innovation capacity in smart learning environments. Grounded in the system dynamics view that system behavior emerges from feedback structure, time delays, and nonlinear interaction rather than from isolated factor effects, the model represents postgraduate innovation capacity through three interrelated subsystems—summary ability, imagination, and transformative ability—and captures their interactions with learning support, learning assessment, learning resources, and data analysis. Based on data extracted from publicly available postgraduate education development quality reports, the relationships among variables were formulated, and the model was tested for dimensional consistency, numerical robustness, and behaviorally plausible performance. Simulation experiments were conducted to examine the dynamic evolution of postgraduate innovation capacity under different parameter perturbation scenarios. Scenario-based sensitivity comparisons were performed to identify the key factors influencing system behavior. The simulation results reveal several important system characteristics, including diminishing marginal returns in learning support, saturation effects in learning resources, delayed cumulative effects in learning assessment, and upper-range amplification in data analysis. In addition, the development of imagination exhibits an exponential growth pattern, while transformative ability is constrained by system feedback structures. These findings indicate that postgraduate innovation capacity development is governed by a nonlinear dynamic system rather than by linear factor relationships. From an applied mathematics perspective, the proposed model provides a quantitative simulation framework for examining the structural behavior of a complex educational system under feedback, delay, and scenario perturbations. Full article

Wildfires are among the key drivers of transformation in boreal ecosystems; however, the mechanisms of postfire recovery in the arid regions of Eurasia remain insufficiently understood. The aim of this study was to identify the role of burn severity and associated edaphic and hydrological factors in shaping the natural regeneration trajectories of Scots pine forests in the Kostanay region of northern Kazakhstan. This study is based on the integration of field data on seedling regeneration and soil conditions with the analysis of long-term satellite-derived indices (NDVI, NDMI, and NBR). Sample plots were grouped according to fixed burn severity classes, which enabled a consistent statistical comparison and reduced the interpretative ambiguity that has characterized previous studies in the region. The results indicate that pine forest regeneration is most successful under low and moderate burn severity, where seed sources are preserved and favourable moisture conditions are maintained. In contrast, high burn severity leads to a reduction in regenerative potential and a shift in recovery trajectories toward deciduous or sparsely vegetated communities. The spectral indices derived from the remote sensing data strongly agreed with the field-based indicators, confirming their suitability for assessing postfire vegetation dynamics. Soil properties act as important modifying factors of recovery processes, particularly under conditions of limited water availability. These findings enhance the current understanding of postfire recovery mechanisms in the arid part of the boreal zone and highlight the need for differentiated management of postfire landscapes. The integration of field observations with remote sensing data provides a robust framework for monitoring and forecasting recovery processes under an increasingly intensified fire regime. Full article

Surface Acoustic Wave (SAW) technology, long central to analog signal processing and RF filtering, is undergoing a major renewal. Driven by advances that decouple SAWs from traditional piezoelectric materials and fixed-function devices, the field is gaining unprecedented control over acoustic, optical, and electronic interactions at the micro and nanoscale. This review synthesizes these developments across four fronts: new physical mechanisms for SAW manipulation, emerging material platforms, ranging from thin films to 2D systems, along with reconfigurable device architectures and circuits, and the expanding landscape of applications they enable. Optical methods are reshaping how SAWs are generated and controlled, bypassing the limits of conventional electromechanical coupling. Coherent optical excitation of high-Q SAW cavities via Brillouin-like optomechanical interactions now grants access to modes in non-piezoelectric substrates such as diamond and silicon, while on-chip SAW excitation in photonic waveguides through backward stimulated Brillouin scattering opens new integrated sensing routes. In parallel, magneto-acoustic experiments have revealed nonreciprocal SAW diffraction from resonant scattering in magnetoelastic gratings. On the device side, ZnO thin-film transistors integrated on LiNbO₃ exploit acoustoelectric coupling to realize voltage-tunable phase shifters; UHF Z-shaped delay lines achieve high sensitivity in a compact footprint; and parametric synthesis of wideband, multi-stage lattice filters targets 5G-class performance. Atomistic simulations show that SAW propagation in 2D MXene films can be engineered via surface terminations, while aerosol jet printing and SAW-assisted particle patterning provide agile, cleanroom-light fabrication of microfluidic and magnetic components. These advances enable applications ranging from hybrid quantum systems and quantum links to lab-on-a-chip particle control, SBS-based and UHF sensing, reconfigurable RF front-ends, and soft robotic actuators based on patterned magnetic composites. At the same time, optical techniques offer non-contact probes of dissipation, and MXenes and other emerging materials open new regimes of acoustic control. Conclusively, they are transforming SAW technology into a versatile, programmable platform for mediating complex interactions in next-generation electronic, photonic, and quantum systems. Full article

This study introduces a physics-guided deep learning architecture designed for the simulation, reconstruction, and pattern recognition of biomedical images. By explicitly integrating physical priors into the learning model, the framework addresses the black-box nature of traditional artificial intelligence (AI). It provides an explainable AI pathway that enhances diagnostic accuracy, robustness, and clinical interpretation. The proposed framework was evaluated through systematic simulation studies. It involved complex geometric configurations, multimodal physical fields, and noise-corrupted synthetic three-dimensional brain volumes. Quantitative analysis demonstrates consistent improvements in reconstruction fidelity, with the peak signal-to-noise ratio (PSNR) reaching 47 dB and the structural similarity index exceeding 0.90 across all scenarios. Notably, at moderate noise levels (0.05), the framework maintains a PSNR greater than 32 dB, ensuring structural integrity essential for computer-aided diagnosis. Volumetric brain experiments further reveal a 38–44% reduction in activation localization errors, highlighting the framework’s utility in functional imaging and disease prognosis. By grounding deep learning in physical constraints, this study provides a transparent and robust solution for automated disease classification and advanced biomedical imaging tasks within clinical decision support systems. Full article

Haematopota pluvialis is a widely distributed hematophagic insect occurring across Eurasia. This horse fly may be a highly efficient mechanical vector of pathogens, including viruses, bacteria, and protozoa. Furthermore, its painful bites can cause local skin lesions and systemic symptoms. The aim of this study was to determine human exposure to H. pluvialis attacks in various types of open space habitats in Eastern Poland and to perform molecular screening of these tabanids for the presence of hematopathogens: Bartonella spp. and Anaplasma phagocytophilum. Specimens of H. pluvialis were collected at three distinct sites in Eastern Poland. The presence of Bartonella spp. and A. phagocytophilum was investigated using PCR-based methods. In total, 141 H. pluvialis females were analyzed. The molecular analysis of the rpoB gene fragment yielded one new haplotype of Bartonella sp. in 0.7% (1) of all studied samples, which may hypothetically exhibit zoonotic potential. Anaplasma phagocytophilum was not detected in the studied material. Moreover, a high level of human and animals exposure to horse fly bites was noted in the studied areas of Eastern Poland. The present results highlight the need for further targeted research on H. pluvialis to quantify pathogen prevalence, transmission efficiencies, and conditions facilitating pathogen transmission in natural settings. Full article

This study investigated the technological feasibility of using a pre-structured bovine blood plasma–flaxseed composition as a functional partial substitute for beef in emulsion-type sausages. Five formulations containing 0–30% replacement were evaluated to determine effects on structural, nutritional, and microbiological properties. Incorporation of the structured composition modified the functional balance of the protein system: water-holding capacity remained stable (p > 0.05), while fat-holding and emulsifying capacities improved at higher inclusion levels (p < 0.05), indicating enhanced interfacial stabilization of the fat phase. Progressive softening of texture was observed, with significant reductions in hardness and chewiness at 30% replacement (p < 0.05). Cooking loss increased at elevated substitution levels but remained within acceptable technological limits. During refrigerated storage, microbial counts remained below safety thresholds. A 15–25% replacement level provided the most balanced performance, maintaining sensory acceptability while improving lipid stabilization. The results demonstrate that structured plasma-based systems can function as effective protein–emulsion modifiers in meat formulations, supporting sustainable valorization of slaughter by-products. Full article

The Moroccan locust (Dociostaurus maroccanus) is one of the most economically significant locust species in the Caucasus and Central Asia. In the past, the Mediterranean region also experienced severe damage to crops and pastures, until widespread grassland conversion to cropland began in the second half of the 20th century. However, climate change, environmental shifts, land-use changes, cropland abandonment, and overgrazing are likely to alter the spatial distribution and outbreak patterns of this pest. Understanding potential changes and geographic shifts is essential for proactive pest management, including effective monitoring and control strategies. In this study, we apply Ecological Niche Modelling (ENM) using 12 machine learning algorithms, historical survey data covering the species’ full distribution range, and relevant abiotic variables to identify the most suitable areas for potential mass breeding during 1991–2020 and the near future (2021–2040), based on the “middle-of-the-road” Shared Socioeconomic Pathway (SSP2-4.5) scenario. Our results indicate significant regional shifts. Notably, breeding suitability is projected to increase in parts of Greece, Turkey, Armenia, Georgia, Kyrgyzstan, and Tajikistan. In contrast, countries such as Turkmenistan, Afghanistan, Pakistan, and Spain are likely to experience a decline in optimal breeding areas. The forecast results support field observations of a geographical shift northward and toward higher altitudes. Additionally, higher temperatures in suitable areas suggest more drought-like conditions, which typically promote locust population explosions and outbreaks. If left unaddressed, such outbreaks can cause severe economic damage to affected regions. Full article

The Circum-Rhodope Belt fringes the Rhodope and Serbo-Macedonian zones in the Alpine orogen of the northern Aegean region. This belt contains Late Paleozoic and Mesozoic metasedimentary successions that record depositional history along the continental margin of Eurasia. Critical successions of the eastern Circum-Rhodope Belt, such as those exposed in the Fanari and Petrota areas, are studied here, integrating their structure, whole-rock geochemistry and U-Pb LA-ICP-MS zircon geochronological context. The Fanari turbiditic succession contains quartz arenite, while the Petrota succession consists of Fe-rich shale and sandstone, and both successions are distinguished by REE-depleted and REE-enriched characteristics and acidic and intermediate arc-related sedimentary sources, respectively. Detrital U-Pb zircon geochronology reveals a Late Carboniferous–Early Permian maximum depositional age of 301.2 ± 8.4 Ma for Fanari quartz arenite and a Late Jurassic maximum depositional age of 147.0 ± 2.0 Ma for Petrota Fe-shale. The results are interpreted in terms of Late Paleozoic continental slope deposition of the Fanari succession along the Eurasian margin and trench-arc sedimentation of the Petrota succession linked to the development of a Jurassic island arc system pertinent to the eastern Circum-Rhodope Belt. These tectonic settings and depositional environments can be used to restore an overall picture of a Late Paleozoic to Mid-Mesozoic sedimentation at the Rhodope–Serbo-Macedonian continental margin of Eurasia. Structures that developed in greenschist facies conditions and N-directed kinematics of the studied successions unequivocally relate them to other units of the eastern Circum-Rhodope Belt and its Late Jurassic tectonic evolution. Full article

Sonchus oleraceus L., a member of the Asteraceae family native to Eurasia, is a herbaceous plant whose young stems and leaves are consumed globally as a medicinal and edible wild vegetable; it is rich in flavonoids and exhibits various pharmacological activities, including anti-inflammatory and anti-tumor effects. This study optimized the extraction process of flavonoids from Xinjiang S. oleraceus using response surface methodology and evaluated the anti-inflammatory activity of luteoloside in vitro. Based on single-factor experiments and Box–Behnken design, the effects of ethanol concentration, extraction time, solid-to-liquid ratio, and extraction temperature on flavonoid yield were investigated. The optimal extraction conditions were determined as ethanol concentration 62%, extraction time 30 min, solid-to-liquid ratio 1:91 g/mL, and extraction temperature 64 °C, with a flavonoid yield of 21.64 mg/g. After purification via polyamide column chromatography, the luteoloside content was determined by HPLC to be 44.06 μg/g. Cytotoxicity assays revealed that a luteoloside concentration of 100 μmol/L reduced the viability of Oryctolagus cuniculus colon epithelial cells to approximately 80%. ELISA results demonstrated that luteoloside significantly inhibited the release of pro-inflammatory factors, including TNF-α, while promoting the expression of the anti-inflammatory factor IL-10. These findings indicate that luteoloside effectively alleviates LPS-induced cellular inflammation. Full article

Scots pine (Pinus sylvestris L.) spans most of Eurasia, yet southern and mountainous populations may retain distinctive genetic components shaped by long-term isolation and complex postglacial dynamics. We genotyped 186 trees from four Scots pine stands in Armenia (AM1-AM4) and reference stands from Germany, Russia and Montenegro with the PiSy50k SNP array and integrated these data with published European array datasets from Finland, Poland and the Baltic region. After quality checks and conservative SNP filtering, 627 individuals from 47 populations and 3659 SNP loci were retained. Within-population diversity was generally high; Armenian stands AM2–AM4 were among the most diverse, whereas AM1 showed reduced diversity and the highest differentiation relative to the remainder of the dataset (FST vs. rest = 0.0047). Direct pairwise F_ST and hierarchical AMOVA confirmed pronounced heterogeneity among Armenian stands, with AM1 the most differentiated stand, AM2 and AM4 closest to the broader Eurasian background, and AM3 intermediate. Principal component analysis (PC1 = 1.42%, PC2 = 0.76%) again separated AM1 strongly from all non-Armenian samples, while AM2 overlapped with the central/eastern European cluster and AM3 and AM4 combined continental-like and AM1-like individuals. Structure-like inference with LEA/sNMF showed a broad cross-entropy plateau from approximately K = 4 to K = 6; we therefore use K = 5 as a practical summary, which highlighted a dominant AM1-associated ancestry component and variable continental admixture in AM2–AM4. KING kinship estimates provided little evidence for within-stand family clustering in Armenian stands; no second-degree-or-closer pairs were observed in AM1–AM4. Together, the results reveal pronounced heterogeneity among Armenian Scots pine stands and identify AM1 as a highly differentiated but unresolved genomic component, providing a genomic baseline to support conservation planning, provenance evaluation and the management of forest reproductive material in the Lesser Caucasus. Full article