Search Results (8,067)

This study presents a scenario-based, two-dimensional flood modeling approach to assess the potential downstream impacts of a piping-induced dam failure triggered by seismic activity. The case study focuses on the Doğantepe Dam in northwestern Türkiye, located near an active branch of the North Anatolian Fault. Critical deformation zones were previously identified through PLAXIS 2D seismic analyses, which served as the physical basis for a dam break scenario. This scenario was modeled using the HEC-RAS 2D platform, incorporating high-resolution topographic data, reservoir capacity, and spatially varying Manning’s roughness coefficients. The simulation results show that the flood wave reaches downstream settlements within the first 30 min, with water depths exceeding 3.0 m in low-lying areas and flow velocities surpassing 6.0 m/s, reaching up to 7.0 m/s in narrow sections. Inundation extents and hydraulic parameters such as water depth and duration were spatially mapped to assess flood hazards. The study demonstrates that integrating physically based seismic deformation data with hydrodynamic modeling provides a realistic and applicable framework for evaluating flood risks and informing emergency response planning. Full article

This study investigates the downstream wake characteristics of a helical hydrokinetic turbine through combined experimental and numerical analyses. A four-bladed helical turbine with a 20 cm rotor diameter and blockage ratio of 53.57% was tested in an open water channel under a flow rate of 180 m³/h, corresponding to a Reynolds number of approximately 90 × 10³. Velocity measurements were collected at 13 downstream cross-sections using an Acoustic Doppler Velocimeter, with each point sampled repeatedly. Standard error analysis was applied to quantify measurement uncertainty. Complementary numerical simulations were conducted in ANSYS Fluent using a steady-state k-ω Shear Stress Transport (SST) turbulence model, with a mesh of 4.7 million elements and mesh independence confirmed. Velocity deficit and turbulence intensity were employed as primary parameters to characterize the wake structure, while the analysis also focused on the recovery of cross-sectional velocity profiles to validate the extent of wake influence. Experimental results revealed a maximum velocity deficit of over 40% in the near-wake region, which gradually decreased with downstream distance, while turbulence intensity exceeded 50% near the rotor and dropped below 10% beyond 4 m. In comparison, numerical findings showed a similar trend but with lower peak velocity deficits of 16.6%. The root mean square error (RMSE) and mean absolute error (MAE) between experimental and numerical mean velocity profiles were calculated as 0.04486 and 0.03241, respectively, demonstrating reasonable agreement between the datasets. Extended simulations up to 30 m indicated that flow profiles began to resemble ambient conditions around 18–20 m. The findings highlight the importance of accurately identifying the downstream distance at which the wake effect fully dissipates, as this is crucial for determining appropriate inter-turbine spacing. The study also discusses potential sources of discrepancies between experimental and numerical results, as well as the limitations of the modeling approach. Full article

Transportation infrastructure is a fundamental driver of economic growth and regional connectivity; and the supply of this infrastructure is often assumed to reduce spatial disparities. This study investigates the impact of transportation accessibility on regional disparities in land prices across South Korea from 2010 to 2019. Using spatial econometric models and geographically weighted regression (GWR), this study evaluates how variations in transportation networks influence land price differentials between regions. The results confirm that transportation accessibility positively affects land prices; but GWR coefficients reveal substantial regional variations in the extent to which accessibility improvements drive land price growth. Furthermore, while the overall distribution of transportation accessibility remained relatively stable, its influence on land price appreciation varied significantly, contributing to a widening gap in land values between regions. These findings underscore the critical role of transportation infrastructure in shaping regional inequalities and highlight the need for more equitable transportation policies to mitigate spatial disparities and promote balanced regional development Full article

Chloroplasts, as the organelles primarily responsible for photosynthesis, require a substantial supply of iron ions. Conversely, due to Fe toxicity, the homeostasis of these ions is subject to tight regulation. Permease in chloroplast 1 (PIC1) has been identified as the primary iron importer into chloroplasts. However, previous studies suggested the existence of a distinct pathway for Fe transfer to chloroplasts, likely involving mitoferrin-like 1 (MFL1) protein. In this work, Arabidopsis MFL1 (AtMFL1) and its cucumber homolog (CsMFL1) were characterized using, among others, Arabidopsis protoplasts as well as both yeast and Arabidopsis mutants. Localization of both proteins in chloroplasts has been shown to be mediated via an N-terminal transit peptide. At the gene level, MFL1 expression profiles differed between the model plant and the crop plant under varying Fe availability. The expression of other genes involved in chloroplast Fe homeostasis, including iron acquisition, trafficking, and storage, was affected to some extent in both AtMFL1 knockout and overexpressing plants. Moreover, root growth and photosynthetic parameters changed unfavorably in the mutant lines. The obtained results imply that AtMFL1 and CsMFL1, as putative chloroplast iron transporters, play a role in both iron management and the proper functioning of the plant. Full article

Coronary artery disease (CAD) constitutes a major contributor to morbidity, mortality and healthcare burden worldwide. Recent innovations in imaging modalities, pharmaceuticals and interventional techniques have revolutionized diagnostic and treatment options, necessitating the reevaluation of established drug protocols or the consideration of newer alternatives. The utilization of beta blockers (BBs) in the setting of acute myocardial infarction (AMI), shifting from the pre-reperfusion to the thrombolytic and finally the primary percutaneous coronary intervention (pPCI) era, has become increasingly more selective and contentious. Nonetheless, the extent of myocardial necrosis remains a key predictor of outcomes in this patient population, with large trials establishing the beneficial use of beta blockers. Computed tomography coronary angiography (CTCA) has emerged as a highly effective diagnostic tool for delineating the coronary anatomy and atheromatous plaque characteristics, with the added capability of MESH-3D model generation. Induction and preservation of a low heart rate (HR), regardless of the underlying sequence, is of critical importance for high-quality results. Landiolol is an intravenous beta blocker with an ultra-short duration of action (t1/2 = 4 min) and remarkable β1-receptor specificity (β1/β2 = 255) and pharmacokinetics that support its potential for systematic integration into clinical practice. It has been increasingly recognized for its importance in both acute (primarily studied in STEMI and, to a lesser extent, NSTEMI pPCI) and chronic (mainly studied in elective PCI) CAD settings. Given the limited literature focusing specifically on landiolol, the aim of this narrative review is to examine its pharmacological properties and evaluate its current and future role in enhancing both diagnostic imaging quality and therapeutic outcomes in patients with CAD. Full article

Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article

As digital marketing becomes more targeted and interactive, it is more critical to understand how young audiences perceive and react to compelling content. This research examines the extent to which consumer responses are affected by neuromarketing knowledge, interest, and screen-based advert exposure for middle school kids. Based on responses from 244 Greek adolescents aged 12–15 years, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to investigate direct and mediated influences on purchase intentions with advertisement skepticism and persuasion knowledge as mediating factors. Results indicate that exposure and recognition have a significant influence on intentions both by means of cognitive as well as attitudinal processes, while interest only increases skepticism but not interaction. Multi-group analysis yielded significant differences according to age and experience, referring to the development path of advertising literacy. The results provide strong cues to educators, policymakers, and marketers who want to develop media-critical competencies among adolescents in an ever-shaping digital age. Full article

Modern aerospace engineering places increasing emphasis on materials that combine low weight with high mechanical performance. Fiber metal laminates (FMLs), which merge metal layers with fiber-reinforced composites, meet this demand by delivering improved fatigue resistance, impact tolerance, and environmental durability, often surpassing the performance of their constituents in demanding applications. Despite these advantages, inspecting such thin, layered structures remains a significant challenge, particularly when they are difficult or impossible to access. As with any new invention, they always come with challenges. This study examines the effectiveness of the fundamental anti-symmetric Lamb wave mode (A0) in detecting weak interfacial defects within Carall laminates, a type of hybrid fiber metal laminate (FML). Delamination detectability is analyzed in terms of strong wave dispersion observed downstream of the delaminated sublayer, within a region characterized by acoustic distortion. A three-dimensional finite element (FE) model is developed to simulate mode trapping and full-wavefield local displacement. The approach is validated by reproducing experimental results reported in prior studies, including the author’s own work. Results demonstrate that the A0 mode is sensitive to delamination; however, its lateral resolution depends on local position, ply orientation, and dispersion characteristics. Accurately resolving the depth and extent of delamination remains challenging due to the redistribution of peak amplitude in the frequency domain, likely caused by interference effects in the acoustically sensitive delaminated zone. Additionally, angular scattering analysis reveals a complex wave behavior, with most of the energy concentrated along the centerline, despite transmission losses at the metal-composite interfaces in the Carall laminate. The wave interaction with the leading and trailing edges of the delaminations is strongly influenced by the complex wave interference phenomenon and acoustic mismatched regions, leading to an increase in dispersion at the sublayers. Analytical dispersion calculations clarify how wave behavior influences the detectability and resolution of delaminations, though this resolution is constrained, being most effective for weak interfaces located closer to the surface. This study offers critical insights into how the fundamental anti-symmetric Lamb wave mode (A0) interacts with delaminations in highly attenuative, multilayered environments. It also highlights the challenges in resolving the spatial extent of damage in the long-wavelength limit. The findings support the practical application of A0 Lamb waves for structural health assessment of hybrid composites, enabling defect detection at inaccessible depths. Full article

Wildfires accelerate soil erosion. Preventive fuel management and post-fire control measures are two distinct strategies that can be used to mitigate wildfire-induced soil loss with varying effectiveness and costs. Here, we quantified the impacts and effectiveness of pre- versus post-fire treatment strategies on soil loss mitigation. We coupled fire simulations with soil erosion modelling to estimate annual wildfire-induced soil loss for two watersheds in Portugal. We identified optimal treatment locations with the aim of maximizing the reduction in soil loss, and estimated treatment effectiveness using treatment leverage and cost-effectiveness. Both mitigation strategies were predicted to reduce post-fire soil loss, with effects increasing with treatment extent. Treatments had a strong mitigation effect particularly in extreme fire years. Results indicated that there was no single mitigation strategy that fits all watersheds, and the choice was largely influenced by wildfire and treatment frequency. For the most fire-prone watershed, Castelo de Bode, fuel treatments were the most effective strategy, being approximately 2-fold cheaper and more effective than post-fire treatments. Treatments were more effective and exhibited lower variability in years with higher soil loss. Our results show that the most cost-effective combinations of treatment strategies vary with the soil loss reduction objective. Relevant treatment synergies were identified that can help land managers to maximize the attainment of soil loss mitigation goals ensuring the best use of resources. This work contributes to a better understanding of how post-fire soil loss can be mitigated, contributing for better resource allocation while maximizing specific management goals. Full article

The Qiangtang Basin (Tibetan Plateau) poses significant geophysical challenges for seismic exploration due to near-surface widespread permafrost and steeply dipping Mesozoic strata induced by the Cenozoic Indo-Eurasian collision. These seismic geological conditions considerably contribute to lower signal-to-noise ratios (SNRs) with complex wavefields, to some extent reducing the reliability of conventional seismic imaging and structural interpretation. To address this, the common-reflection-surface (CRS) stack method, derived from optical paraxial ray theory, is implemented to transcend horizontal layer model constraints, offering substantial improvements in high-SNR prestack gather generation and prestack depth migration (PSDM) imaging, notably for permafrost zones. Using 2D seismic data from the basin, we detailedly compare the CRS stack with conventional SNR enhancement techniques—common midpoint (CMP) FlexBinning, prestack random noise attenuation (PreRNA), and dip moveout (DMO)—evaluating both theoretical foundations and practical performance. The result reveals that CRS-processed prestack gathers yield superior SNR optimization and signal preservation, enabling more robust PSDM velocity model building, while comparative imaging demonstrates enhanced diffraction energy—particularly at medium (20–40%) and long (40–60%) offsets—critical for resolving faults and stratigraphic discontinuities in PSDM. This integrated validation establishes CRS stacking as an effective preprocessing foundation for the depth-domain imaging of complex permafrost geology, providing critical improvements in seismic structural resolution and reduced interpretation uncertainty for hydrocarbon exploration in permafrost-bearing basins. Full article

This study conducts a comprehensive systematic review of the economic impact of Digital Twin (DT) technology within the Architecture, Engineering, and Construction (AEC) industry, following the PRISMA methodology. While DT adoption has been accelerated by advancements in Building Information Modelling (BIM), the Internet of Things (IoT), and data analytics, significant challenges persist—most notably, high initial investment costs and integration complexities. Synthesising the literature from 2016 onwards, this review identifies sector-specific barriers, regulatory burdens, and a lack of standardisation as key factors constituting DT implementation costs. Despite these hurdles, DTs demonstrate strong potential for enhancing construction productivity, optimising lifecycle asset management, and enabling predictive maintenance, ultimately reducing operational expenditures and improving long-term financial performance. Case studies reveal cost efficiencies achieved through DTs in modular construction, energy optimisation, and infrastructure management. However, limited financial resources and digital skills continue to constrain the uptake across the sector, with various extents of impact. This paper calls for the development of unified standards, innovative public–private funding mechanisms, and strategic collaborations to unlock and utilise DTs’ full economic value. It also recommends that future research explore theoretical frameworks addressing governance, data infrastructure, and digital equity—particularly through conceptualising DT-related data as public assets or collective goods in the context of smart cities and networked infrastructure systems. Full article

Oceanic mesoscale activity influences the atmosphere in the southwestern and southern sectors of the Atlantic Ocean. However, the influence of high latitudes, specifically sea ice, on mid-latitudes and a better understanding of mesoscale ocean–atmosphere thermodynamic interactions still require further study. To quantify the effects of oceanic mesoscale activity during the periods of maximum and minimum Antarctic sea ice extent (September 2019 and February 2020), numerical experiments were conducted using a coupled regional model and an online two-dimensional spatial filter to remove high-frequency sea surface temperature (SST) oscillations. The largest SST anomalies were observed in the Brazil–Malvinas Confluence and along oceanic fronts in September, with maximum SST anomalies reaching 4.23 °C and −3.71 °C. In February, the anomalies were 2.18 °C and −3.06 °C. The influence of oceanic mesoscale activity was evident in surface atmospheric variables, with larger anomalies also observed in September. This influence led to changes in the vertical structure of the atmosphere, affecting the development of the marine atmospheric boundary layer (MABL) and influencing the free atmosphere above the MABL. Modulations in precipitation patterns were observed, not only in oceanic regions, but also in adjacent continental areas. This research provides a novel perspective on ocean–atmosphere thermodynamic coupling, highlighting the mesoscale role and importance of its representation in the study region. Full article

Flood hazards have increasingly threatened the East Kazakhstan region in recent decades due to climate change and growing anthropogenic pressures, leading to more frequent and severe flooding events. This article considers an approach to modeling and forecasting river runoff using the example of the small Kurchum River in the East Kazakhstan region. The main objective of this study was to evaluate the numerical performance of the flood hazard model by comparing simulated flood extents with observed flood data. Two types of data were used as initial data: topographic data (digital elevation models and topographic maps) and hydrological data, including streamflow time series from stream gauges (hourly time steps) and lateral inflows along the river course. Spatially distributed rainfall forcing was not applied. To build the model, we used the software packages of HEC-RAS version 5.0.5 and MIKE version 11. Using retrospective data for 3 years (2019–2021), modeling was performed, the calculated boundaries of possible flooding were obtained, and the highest risk zones were identified. A dynamic map of depth changes in the river system is presented, showing the process of flood wave propagation, the dynamics of depth changes, and the expansion of the flood zone. Temporal flood inundation mapping and performance metrics were evaluated for each individual flood event (2019, 2020, and 2021). The simulation outcomes closely correlate with actual flood events. The assessment showed that the model data coincide with the real ones by 91.89% (2019), 89.09% (2020), and 95.91% (2021). The obtained results allow for a clarification of potential flood zones and can be used in planning measures to reduce flood risks. This study demonstrates the importance of an integrated approach to modeling, combining various software packages and data sources. Full article

The prediction of reservoir and caprock thickness is important in geological evaluations for site selection for aquifer underground gas storage. Therefore, high-resolution seismic identification of reservoirs and caprocks is crucial. High-resolution time–frequency decomposition is one of the key methods for identifying sedimentary layers. Based on this, we propose a least squares constrained spectral analysis method using a greedy fast shrinkage algorithm. This method replaces the traditional Tikhonov regularization objective function with an L1-norm regularized objective function and employs a greedy fast shrinkage algorithm. By utilizing shorter window lengths to segment the data into more precise series, the method significantly improves the computational efficiency of spectral analysis while also enhancing its accuracy to a certain extent. Numerical models demonstrate that compared to the time–frequency spectra obtained using traditional methods such as wavelet transform, short-time Fourier transform, and generalized S-transform, the proposed method can achieve high-resolution extraction of the dominant frequencies of seismic waves, with superior noise resistance. Furthermore, its application in a research area in southern China shows that the method can effectively predict thicker sedimentary layers in low-frequency ranges and accurately identify thinner sedimentary layers in high-frequency ranges. Full article

The dynamic aperture is an essential concept in circular particle accelerators, providing the extent of the phase space region where particle motion remains stable over multiple turns. The accurate prediction of the dynamic aperture is key to optimising performance in accelerators such as the CERN Large Hadron Collider and is crucial for designing future accelerators like the CERN Future Circular Hadron Collider. Traditional methods for computing the dynamic aperture are computationally demanding and involve extensive numerical simulations with numerous initial phase space conditions. In our recent work, we have devised surrogate models to predict the dynamic aperture boundary both efficiently and accurately. These models have been further refined by incorporating them into a novel active learning framework. This framework enhances performance through continual retraining and intelligent data generation based on informed sampling driven by error estimation. A critical attribute of this framework is the precise estimation of uncertainty in dynamic aperture predictions. In this study, we investigate various machine learning techniques for uncertainty estimation, including Monte Carlo dropout, bootstrap methods, and aleatory uncertainty quantification. We evaluated these approaches to determine the most effective method for reliable uncertainty estimation in dynamic aperture predictions using machine learning techniques. Full article