Search Results (2,053)

In recent years, oil and gas exploration in the Lower Cambrian of the central–northern Sichuan Basin, China, has demonstrated enormous resource potential. As a potential interval of high-quality hydrocarbon source rocks, the Canglangpu Formation of the Lower Cambrian remains underdeveloped in exploration and lacks in-depth research. Affected by tectonics, sedimentary environment, and diagenesis, the genetic mechanisms and genetic models of carbonate reservoirs in the Canglangpu Formation within the study area need further clarification. This study utilizes petrological characteristics of dolomite and geochemical data to clarify diagenetic fluids of different reservoir rocks and identifies the main controlling factors and development models of the reservoirs. The results show that the dolomites in Member 1 of the Canglangpu Formation (Cang-1 Member) in central–northern Sichuan are mainly classified into three types: silty–fine crystalline dolomite (D1), granular dolomite (D2), and residual-texture dolomite (D3). The reservoir spaces are dominated by intercrystalline pores, intergranular pores, and structural fractures. The porosity of the Cang-1 Member in the area is relatively low, with an average porosity of 5% or lower. The reservoir porosity average is 3.63%, belonging to low-porosity reservoirs. The permeability average is 2.94 × 10⁻³ mD. Analysis of different geochemical indicators indicates that the diagenetic fluids of the three dolomite types are mainly syndepositional seawater. D1 is formed by penecontemporaneous dolomitization, while both D2 and D3 are formed during the shallow-to-middle burial stage. The main controlling factors of dolomite reservoirs include sedimentary facies, diagenesis, and tectonic movement. This study clarifies the genesis and development model of dolomite reservoirs in the Cang-1 Member, aiming to provide reliable and valuable references for the exploration of dolomite reservoirs in the Canglangpu Formation of the Sichuan Basin. Full article

To clarify the differences in and mechanisms of soil detachment before and after soil collapse, five typical granite soil layers (red soil, red soil–sandy soil, sandy soil, sandy soil–debris, and debris layers) of Benggang in Anxi County, Fujian Province, were studied via laboratory runoff scouring tests, and the detachment capabilities and influencing factors of undisturbed (original) and disturbed (colluvial deposit) soils were compared. The results showed that disturbance due to soil collapse significantly increases the soil detachment capacity by an average of 1046 times, with the greatest increase occurring in the red soil–sand soil layer (3494 times) and the smallest increase occurring in the debris layer (63 times). The undisturbed soil detachment capacity increases with increasing soil depth, whereas the disturbed soil capacity first increases but then decreases, with the sand layer having the highest capacity. Hydrodynamic fitting results revealed that undisturbed red soil has a linear relationship, red soil–sandy soil and sandy soil layers have power function relationships, and sandy soil–debris and debris layers have logarithmic relationships with flow shear stress. Disturbed red soil and red soil–sandy soil layers are linearly related, whereas the other layers are logarithmically related. Correlation analysis revealed that undisturbed soil detachment is significantly negatively correlated with clay, silt, gravel, free iron oxide, and free alumina contents and positively correlated with sand content. Disturbed soil shows similar correlations, but it has a negative correlation with organic matter instead of gravel. Structural equation modelling (SEM) path analysis revealed that undisturbed soil detachment is affected mainly by negative free alumina oxide content (path coefficient of −0.87) and flow shear stress (path coefficient of 0.14), whereas disturbed soil is controlled mainly by negative shear strength (path coefficient of −0.76) and positive flow shear stress (path coefficient of 0.49). This study elucidates the mechanism by which colluvial deposit disturbance accelerates soil detachment, providing a theoretical basis for the prevention and control of Benggang erosion in the hilly regions of southern China with red soil. Moreover, the comparative research strategy adopted in this study offers a reference for related investigations in similar erosion-prone areas. Full article

Objectives: The main aim of this study consists of testing the consistency and reliability of the BIANCA (BIophysical ANalysis of Cell death and chromosome Aberrations) biophysical model across different radiation transport codes in the framework of cancer ion-therapy research. Methods: Spread-Out Bragg Peak (SOBP) profiles for protons, helium ions and carbon ions were simulated at three different depth ranges (2–3 cm, 5–8 cm, and 10–15 cm) applying two radiation transport codes, FLUKA and Geant4. While BIANCA has been interfaced to FLUKA in a previous work, an interface with Geant4 was purposely developed in this work. Cell survival along all considered SOBP profiles was predicted by BIANCA for two cell lines with very different radiosensitivities: Squamous Cell Carcinoma (SCC), with α/β = 12.68 Gy, and chordoma, with α/β = 2.37 Gy. The agreement between the predictions obtained from the two approaches was quantitatively evaluated by means of Root Mean Square Error (RMSE) and Gamma Index analysis, both for physical dose distributions and for cell survival predictions. Results: The comparison between FLUKA and Geant4 simulations demonstrated good agreement. The Gamma Index analysis yielded passing rates exceeding 94.9% for physical dose profiles (criteria: 3%/2 mm) and 96.0% for cell survival probabilities (criteria: 2%/2 mm) across all considered ion species (protons, He, C) and depths. Root Mean Square Error (RMSE) analysis confirmed average discrepancies below 2.5% for physical dose and 1% for biological survival. Conclusions: This study shows that the BIANCA model can be applied to predict cell killing along hadron therapy beams when interfaced both with FLUKA and with Geant4. Furthermore, the successful implementation of the interface with Geant4 expands the accessibility and applicability of BIANCA, paving the way for its future integration into different transport codes and/or treatment planning systems. Full article

Floods pose significant risks to human lives, infrastructure, and the environment. Timely and accurate flood forecasting plays a pivotal role in mitigating these risks. This study proposes a Digital Twin proof-of-concept framework aimed at improving flood forecasting and validated its effectiveness through a pilot study of the 2021 flood event in Luxembourg. The baseline forecasting method combines GloFAS ensemble streamflow forecasts with a high-resolution flood hazard datacube generated using a LISFLOOD-FP hydrodynamic model and then averaging among the member forecasts. To dynamically update the flood forecasts and improve their accuracy, the framework integrates satellite-based Earth observations (EOs)—specifically Sentinel-1-derived flood probability maps from the Global Flood Monitoring service—via a particle filter-based data assimilation (DA) process. As such, the simulations with more coherence with the observed Sentinel-1-derived flood probability maps are prioritized. This results in a Digital Twin capable of delivering daily flood depth forecasts, at detailed spatial resolution, up to 30 days ahead, with reduced prediction uncertainty. Using the 2021 flood event, we evaluate the performance of the Digital Twin in assimilating EO data to refine hydraulic model simulations and issue accurate flood forecasts. Although certain challenges persist—particularly the difficulty in quantifying the error structure of GloFAS discharge forecasts—the proposed approach demonstrates clear improvements in forecast accuracy compared to open-loop simulations. As a result, the approach reduces water level prediction errors by an average of 15–33% and increases the Nash–Sutcliffe Efficiency of discharge predictions by approximately 15–36%. Future work will aim to refine the flood hazard datacube and advance the characterization and modeling of uncertainties associated with both GloFAS streamflow forecasts and Sentinel-1-derived flood maps, thereby further enhancing the system’s predictive capability. Full article

Background: Early detection of dental caries is essential for effective oral health management. Current diagnostic workflows rely heavily on radiographic imaging, which involves infrastructure requirements, workflow coordination, and resource considerations that may limit frequent use in high-throughput or resource-constrained settings. These contextual factors motivate exploration of adjunct screening concepts that could support front-end triage decisions within existing care pathways. This study evaluates, in simulation, whether modeled tooth-percussion response signals contain sufficient discriminative information to justify further translational and managerial investigation. Implementation costs, workflow optimization, and economic outcomes are not evaluated directly; rather, the objective is to assess whether the technical preconditions for a potentially scalable screening concept are satisfied under controlled in silico conditions. Methods: An in silico model of tooth percussion was developed in which enamel, dentin, and pulp/root structures were represented as a simplified layered mechanical system. Impulse responses generated from simulated tapping were used to compute the modeled surface-vibration response (enamel-layer displacement), which served as a proxy for a measurable percussion-related signal (e.g., contact vibration), rather than a recorded acoustic waveform. Carious conditions were simulated through depth-dependent reductions in stiffness and effective mass and increases in damping to represent enamel and dentin demineralization. A synthetic dataset of labeled simulated signals was generated under varying structural parameters and measurement-noise assumptions. Machine-learning models using Mel-frequency cepstral coefficient (MFCC) features were trained to classify healthy teeth, enamel caries, and dentin caries at a screening (triage) level. Results: Under baseline simulation conditions, the classifier achieved an overall accuracy of 0.97 with balanced macro-averaged F1-score (0.97). Misclassifications occurred primarily between healthy and enamel-caries categories, whereas dentin-caries cases were most consistently identified. When measurement noise and structural variability were increased, performance declined gradually, reaching approximately 0.90 accuracy under the most challenging simulated scenario. These results indicate that discriminative information is present within the modeled signals at a screening (triage) level, meaning that higher-risk categories can be distinguished probabilistically rather than with definitive diagnostic certainty. Sensitivity and specificity trade-offs were not optimized in this study, as the objective was to assess separability rather than to define clinical decision thresholds. Conclusions: Within the constraints of the in silico model, simulated tooth-percussion response signals demonstrated discriminative patterns between healthy, enamel caries, and dentin caries categories at a screening (triage) level. These findings establish technical plausibility under controlled simulation conditions and support further investigation of percussion-based screening as a potential adjunct to clinical assessment. From a healthcare management perspective, the present results address a prerequisite question—whether such signals contain sufficient information to justify translational research, rather than demonstrating workflow optimization, cost reduction, or system-level impact. Clinical validation, threshold optimization, and implementation studies are required before managerial or operational benefits can be evaluated. Full article

Tsunami-induced scour around coastal embankments and nearshore structures is a primary cause of structural instability and failure. However, the hydrodynamic mechanisms by which coastal vegetation mitigates this scour remain insufficiently understood. This study employs three-dimensional numerical simulations to investigate the influence of rigid and flexible vegetation on overflow-induced scour downstream of embankments and local scour around structures under tsunami-like inundation. The simulations were conducted using Ansys Fluent 2021R2, utilizing the Volume of Fluid (VOF) method to capture the free surface and the RNG k − ε turbulence model within the Reynolds-averaged Navier–Stokes (RANS) framework. Computational geometries were reconstructed from laboratory experiments, and the model’s reliability was validated against measured water surface profiles. The results demonstrated that vegetation significantly alters flow dynamics, velocity distributions, vortex structures, and both the magnitude and patterns of bed shear stress within scour holes. Specifically, in overflow-induced scour, vegetation suppresses scour intensity by inducing backwater effects, enhancing momentum diffusion, attenuating flow impingement on the bed, and reducing peak bed shear stress. Conversely, for local scour around structures, vegetation increases upstream water depth while intensifying downstream wake vortices, leading to scour hole elongation—particularly under dense and tall vegetation. These findings offer novel insights into the hydrodynamics of vegetation-induced scour mitigation and provide guidelines for optimizing vegetation configurations to enhance the tsunami resilience of coastal infrastructure. Full article

Satellite-derived bathymetry holds significant value for acquiring nearshore bathymetric data. However, in coastal waters, bathymetry is affected by in-water particle scattering and seafloor substrate variability, leading to spatial inconsistency between the logarithmic green band profile derived from multispectral satellite imagery and the actual water depth profile. According to the position information of interpolated points and the inverse distance square relationship with the surrounding 16 points from low-reference bathymetric data (such as the bathymetric map from GEBCO, NOAA Electronic Navigational Charts), this model adopts a third-order inverse distance square bicubic convolution interpolation method to resample a high-resolution bathymetric map with the size of the satellite image. Normalized underwater topography trend data (derived from the low-resolution reference bathymetric map) were combined with normalized green band data to compute an averaged dataset. In this way, a linear bathymetric model was constructed. We invert this model’s parameters and calculate the water depth by using the average data and reference points from reference bathymetric data. Validation tests were conducted across three test areas using independent validation bathymetric data: Weizhou Island, China (Case II waters); Saipan, Northern Mariana Islands, USA (Case I waters); and Molokai Island, Hawaii, USA (Case I waters). Each test area was studied using five error analysis methods (i.e., scatterplot, error histogram, regional bathymetric error, three check lines, and seven check points). Compared to four classic bathymetric models (i.e., single-band model, log-ratio model, ratio-log model, and multi-band model), the proposed model achieved lower root mean square errors (RMSE) of 2.08 m, 1.40 m, and 2.01 m in the three test areas, representing reductions of 35%, 43%, 45%, and 20% and overall averages of 48%, 62%, 64%, and 43%, respectively. Its goodness of fit (R²) reached 0.87, 0.97, and 0.97, showing improvements of at least 5%, 5%, 9%, and 9% and overall averages of 17%, 77%, 84%, and 12%, respectively. The results demonstrate that the proposed model significantly improves bathymetry accuracy while maintaining algorithmic simplicity, providing a new model for acquiring nearshore foundational bathymetric maps. Full article

Electrical Discharge Machining (EDM) enables precision post-weld finishing of AISI 304 stainless steel, but stochastic spark overlaps make the fatigue-critical maximum peak-to-valley height (R_max) difficult to predict. This study develops a validated physics-based framework quantifying how crater overlap governs R_max evolution. Experiments on unwelded AISI 304 cylinders—proxying weld metal while excluding heat-affected zone (HAZ) effects—used Central Composite Design (20 trials, 900–9380 μJ discharge energies). Profilometry and scanning electron microscopy (SEM) correlated the crater size, overlap intensity, micro-cracking, and R_max escalation from 18 to 85 μm. Primary and secondary crater formation under minimum and maximum overlap configurations were simulated using a 2D axisymmetric finite element model with Gaussian heat flux and temperature-dependent thermophysical properties. The predictive metric R_max,num = (d_initial + d_secondary)/2 achieved 11–19% average error against the experimental R_max,exp, with complementary valley depth (R_v) validation at 13% error. The Specimen 7 outlier (~50% error) reveals the limitations of deterministic modelling under stochastic debris accumulation and plasma instability at intermediate energies. Crater overlap generates secondary dimples, sharp inter-crater peaks, and rim micro-crack networks, driving the 4.7-fold R_max increase—approaching International Institute of Welding (IIW) fatigue thresholds (<25 μm for high-cycle categories). The framework explicitly links the discharge energy, plasma channel radius (R_pc), and overlap geometry to surface topography, enabling process optimization (I·t_on < 60 A·s maintains R_max < 25 μm). Mesh independence (<2.5% convergence) and six centre-point replicates (CV = 4.2%) confirm robustness. This validated upper-bound R_max predictor supports the digital co-optimization of welding and EDM parameters for aerospace/energy applications, with planned extensions to stochastic 3D models incorporating adaptive remeshing and real weld topographies. Full article

The paper presents the results of a study of linear wear of gas-flame and ion-plasma coatings of KNA-82 seals with an yttrium content of 0.5%, used in gas turbine engine assemblies, during physical modeling of their thermomechanical loading on small-sized samples. Tribotechnical tests were carried out in four stages, simulating the operating conditions of real gas turbine engines—from the first start-up with running-in of the coating cut-in areas to reaching a steady state with their service properties formed. The surface of the coatings was in contact with the ridges of triangular-shaped plates without heating (20 °C), at average heating (350–470 °C), after holding the samples at 1100 °C and average heating of 410–460 °C, and after grinding off the worn layer that had worn out after holding the samples at 1100 °C at average heating of 320–440 °C. Trends in the change in the linear ear of coatings and the formation of friction tracks caused by the uneven manifestation of the physical and mechanical properties of coatings, which are unevenly distributed throughout their body, were determined. It was found that both coatings tend to stabilize the wear process at certain mechanical pressures in the friction contact zone and only in the temperature range from 20 °C to 400 °C. These pressures range from 4 MPa to 6.7 MPa for gas-flame coatings and from 3 MPa to 4.2 MPa for ion-plasma coatings. It has been determined that within the depth range of 30–100 μm, the wear resistance (as assessed by linear wear) of ion-plasma coatings is higher than that of gas-flame coatings. This predetermines the fact that in the event of a catastrophic collision between the coatings and a blade, the geometry of the damage to the surface of the gas-flame coating will be greater than that of the ion-plasma coating. In the event of damage exceeding 75–100 μm in depth, both coatings become inoperable, since their wear characteristics are no longer maintained. This is indicated by a rapid decrease in their wear resistance under step loading. Moreover, the gas-flame coating is more prone to catastrophic failure than the ion-plasma coating. Full article

An improved depleting sand fracture model was derived in this work using Finite Element Methods, taking into consideration the effect of pore pressure and production on in situ stresses. Sets of governing equations from the commercial finite element simulator COMSOL Multiphysics were used to obtain a model that compares well with the existing fracture model, mainly based on the Mohr–Coulomb failure criterion. The model uniquely couples reservoir depletion-induced stress evolution with fracture initiation and propagation within a unified finite element framework. A constant overburden load was used since its value majorly depends on depth, and the formation is assumed to be fixed at the bottom. The reservoir is assumed to be depleting at a constant rate with no water injection to assist pressure, with an average porosity of 25% and an average permeability of 251 mD at the beginning of production. The reservoir compacted during production, and in turn, porosity and permeability were reduced over the years of observation. Fracturing was observed to be much easier for the depleted reservoir, since horizontal stresses, which might have created friction, are reduced during reservoir production, signifying that for depleted reservoirs, a small fracture pressure is required. Created fractures are observed to propagate in the direction of the maximum horizontal stress and perpendicular to the direction of the minimum horizontal stress. Full article

This study evaluates whether a multimodal retrieval-augmented generation (RAG) pipeline originally developed for early modern woodcuts can be effectively transferred to the domain of medieval manuscript illumination. Using a dataset of Wenzelsbibel miniatures annotated with Iconclass, the pipeline combined page-level image input, LLM description generation, vector retrieval, and hierarchical reasoning. Although overall scores were lower than in the earlier woodcut study, the best-performing configuration still substantially surpassed both image-similarity and keyword-based search, confirming the advantages of structured multimodal retrieval for medieval material. Truncation analysis further revealed that many errors occurred only at the deepest Iconclass levels: removing levels raised precision to 0.64 and 0.73, with average remaining depths of 5.49 and 4.49 levels, respectively. These results indicate that the model’s broader hierarchical placement is often correct even when fine-grained specificity breaks down. Taken together, the findings demonstrate that a woodcut-oriented RAG pipeline can be meaningfully adapted to manuscript illumination and that its strengths lie in contextual reasoning and structured classification. Future improvements should incorporate available textual metadata, explore graph-based retrieval, and refine Iconclass-driven pathways. Full article

Floods are natural disasters that cause significant socioeconomic and environmental losses in both urban and rural areas. Within the framework of spatial planning, precautionary measures against flood hazards can be developed using analytical approaches based on different modeling techniques. In this study, flood-prone areas in the Melen Basin, Türkiye, were identified and mapped using five statistical methods, namely Frequency Ratio (FR), Shannon Entropy (SE), Evidential Belief Function (EBF), and the hybrid models EBF–SE and EBF–FR. The analysis was conducted using a flood inventory and environmental datasets covering the period 2019–2024, including elevation, slope, aspect, land use, plan and profile curvature, drainage density, distance to river, curve number, long-term average precipitation, geological formation, soil depth, topographic wetness index, sediment transport, and stream power index. Model performances were evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC). The results indicate that the SE method achieved the highest predictive performance (AUC = 0.979), followed by FR (0.974), EBF–SE (0.972), EBF–FR (0.968), and EBF (0.966). According to the FR and SE models, elevation, lithology, and slope were identified as the most influential factors in flood occurrence. In the evaluation of the success index of the models, the following values were determined according to their size: EBF–SE (96.0), SE (94.4), EBF (91.8), FR (81.9), and EBF–FR (79.4). In the classification of flood sensitivity maps, Natural Breaks (Jenks) is the most successful method according to the success index. The findings demonstrate that data-driven and hybrid models can effectively support flood risk assessment and provide valuable input for land-use planning and flood risk management. Full article

Geothermal energy assumes an increasingly crucial role in advancing carbon neutrality. However, heat transfer calculations for shallow ground-heat exchangers (GHE) face challenges, including large computational loads for pipe arrays and insufficient long-term operational analysis. This study proposes two key innovations: first, the introduction of the Response Factor Method (RFM), which accelerates long-term heat-transfer calculations by constructing a coefficient matrix library; second, a dimension-reduction algorithm for large-scale pipe arrays (LADR), balancing simulation speed and accuracy. The simulation model is developed and validated experimentally, with the simulated outlet temperature showing a 0.2% average relative error compared to measured values, with a 20-times speed-up of simulation time compared to the original method. Moreover, the LADR can realize a reduction in calculation load into only two or three boreholes while the neglectable errors do not affect numerical results. The study found that heat extraction increases linearly with borehole depth, but with diminishing returns. Increasing pipe diameter and spacing enhances heat extraction, while overloading reduces reliability. Intermittent operation significantly boosts the load-bearing capacity of individual pipes. The thermal effect radius during the transitional period is larger than that during the heating/cooling periods. We observed and explained the ground heat accumulation in a thermally balanced system for the first time. Additionally, there are differences in thermal performance at different borehole locations within the array, along with a load transfer effect. This research provides valuable insights for optimizing shallow GSHPs. Full article

Hand localization in cluttered industrial environments remains challenging due to variations in appearance and the gap between synthetic and real-world data. Domain randomization addresses this “reality gap” by intentionally introducing randomized and unrealistic visual features in simulated scenes, encouraging neural networks to focus on essential domain-invariant cues. In this study, we applied domain randomization to generate a synthetic Red-Green-Blue–Depth (RGB-D) dataset for training multimodal instance segmentation models, with the aim of achieving color-agnostic hand localization in complex industrial settings. We introduce a new synthetic dataset tailored to various hand detection tasks and provide ready-to-use pretrained instance segmentation models. To enhance robustness in unstructured environments, the proposed approach employs multimodal inputs that combine color and depth information. To evaluate the contribution of each modality, we analyzed the individual and combined effects of color and depth on model performance. All evaluated models were trained exclusively on the proposed synthetic dataset. Despite the absence of real-world training data, the results demonstrate that our models outperform corresponding models trained on existing state-of-the-art datasets, achieving higher Average Precision and Probability-Based Detection Quality. Full article

Urban vitality is a critical metric for measuring the quality of sustainable development and overall competitiveness, serving as the core kinetic energy for urban survival and growth. As a key link for land–sea resource coordination and internal–external economic circulation, the urban vitality of China’s coastal regions is of great significance for promoting regional coordinated development. Focusing on 130 cities in China’s coastal regions, this study constructs an evaluation system encompassing five dimensions: economy, society, culture, environment, and population. Utilizing the AHP–entropy combined weighting method, the urban vitality index (UVI) for 2023 is calculated based on a scientific measurement of each dimension’s vitality level. Additionally, spatial autocorrelation and the multiscale geographically weighted regression (MGWR) model are employed to examine the spatial evolution patterns and multidimensional driving mechanisms in depth. The results indicate the following: (1) Coastal regions exhibit significant spatial heterogeneity in vitality, characterized by a distinct south–north gradient (high in the south and low in the north). Geographically, the distribution of overall vitality is highly uneven: high-value clusters are concentrated in southern coastal urban agglomerations—notably the Pearl River Delta and the Yangtze River Delta—whereas northern coastal areas, with the exception of the Shandong Peninsula, generally demonstrate relatively low vitality levels. Administrative rank has a significant effect on vitality agglomeration; the average vitality of provincial capitals and above is approximately four times that of other cities. (2) Environmental vitality performs best but shows significant spatial polarization. High-value areas for economic and population vitality are concentrated in the Yangtze River Delta, Pearl River Delta, and Shandong Peninsula urban agglomerations, while social and cultural vitality only stand out in megacities such as Shenzhen, Guangzhou, and Shanghai. (3) Urban vitality exhibits strong spatial correlation and path dependence. Coastal urban vitality shows a significant positive spatial autocorrelation, with H-H (high–high) clusters primarily concentrated in the Yangtze River Delta and Pearl River Delta, indicating a high degree of spatial aggregation and regional synergy in urban vitality. Conversely, L-L (low–low) “depressed cities” are distributed in contiguous blocks in the north and peripheral areas, indicating that regional collaborative driving forces need to be further strengthened. (4) Multifactor driving mechanisms show obvious spatial heterogeneity and scale effects. The MGWR model results reveal that the medical insurance coverage rate, human capital level, and annual average PM 2.5 concentration are the dominant factors driving coastal urban vitality. Their influence intensity shows significant north–south differences across geographical locations, and the contribution of nonspatial factors is overall higher than that of traditional built environment factors. These findings provide a scientific reference for formulating precise and differentiated regional vitality enhancement strategies, optimizing coastal resource allocation, and promoting high-quality land–sea coordinated development. Full article