Search Results (1,418)

The impact of wind on precipitation measurements from the OTT Parsivel² optical transmission disdrometer is quantified using computational fluid dynamics simulations. The numerical velocity field around the instrument body and above the instrument sensing area (the laser beam) shows significant disturbance that depends heavily on the wind direction. By computing the trajectories of raindrops approaching the instrument, the wind-induced bias is quantified for a wide range of environmental conditions. Adjustments are derived in terms of site-independent catch ratios, which can be used to correct measurements in post-processing. The impact on two integral rainfall variables, the rainfall intensity and radar reflectivity, is calculated in terms of collection and radar retrieval efficiency assuming a sample drop size distribution. For rainfall intensity measurements, the OTT Parsivel² shows significant bias, even much higher than the wind-induced bias typical of catching-type rain gauges. Large underestimation is shown for wind parallel to the laser beam, while limited bias occurs for wind perpendicular to it. The intermediate case, with wind at 45°, presents non negligible overestimation. Proper alignment of the instrument with the laser beam perpendicular to the prevailing wind direction at the installation site and the use of windshields may significantly reduce the overall wind-induced bias. Full article

This study quantifies the influence of soil–structure interaction (SSI) on key parameters of performance-based seismic design (PBSD) for steel moment-resisting frames. Specifically, PBSD is extended as a methodology in which explicit structural performance levels, such as immediate occupancy, damage limitation, life safety, and collapse prevention, serve as the basis for sizing and detailing structural members under specified seismic hazard levels, instead of traditional force-based design. The PBSD framework is further developed to incorporate SSI by adopting a beam on a nonlinear Winkler foundation model. This model captures the nonlinear soil response and its interaction with the structure, enabling a more realistic design framework within a performance-based context. To evaluate and quantify the influence of SSI in the PBSD method, an extensive parametric study is performed using 100 far-field ground motions, categorized into four groups (25 records each) corresponding to EC8 soil types A, B, C, and D. Nonlinear time history analyses reveal consistent trends across the examined frames. When SSI is neglected, the fundamental natural period (T) is systematically underestimated by approximately up to 3.5% on EC8 soil type C and up to 15% on soil type D. As a result, the base shear and the mean values of maximum interstorey drift ratios (IDRs) are overestimated compared to cases accounting for soil flexibility, with the largest drift discrepancies observed in frames with eight or more storeys on soil D. The analyses further reveal that softer soils (e.g., Soil D) lead to significantly higher q values, particularly for moderate-to-long period structures, whereas stiffer soils (e.g., Soil B) cause only minor deviations, remaining close to fixed-base values. A complementary machine learning module, trained on the same dataset, is employed to predict base shear, maximum IDR, and the behavior factor q. It successfully reproduces the deterministic SSI trends, achieving coefficients of determination (R²) ranging from 0.986 to 0.992 for maximum IDR, 0.947 to 0.948 for base shear, and 0.944 to 0.952 for q. Feature importance analysis highlights beam and column ductility, soil class, and performance level as the most influential predictors of structural response. Full article

The GFED (Global Fire Emissions Database) series products are widely used in global fire research, yet their applicability in mainland China remains insufficiently evaluated. Additionally, large fires and small fires are rarely studied separately. This study first evaluates GFED4’s applicability for monitoring forest fire burned areas in Chinese mainland (2001–2015) through multi-temporal (annual, seasonal, and monthly) and multi-spatial (national, regional, provincial, and 0.25° grid) analyses, using Pearson correlation (CC), root mean square error (RMSE), and mean error (ME) alongside official statistical data. Then, the forest fire-burned areas of small fires were extracted based on the difference between GFED4 and GFED5. The results show that GFED4 exhibits strong consistency at the national level and in key fire-prone regions such as Northeast, North, and Central South China, especially during high-fire years and in spring. However, systematic overestimation occurs in the Northwest, while underestimation or seasonal bias is observed in parts of East and Southwest China. The results show a clear decline in large-fire burned area, but a significant increase in small fires, particularly in Northeast, Central South, and East China. Spatial analysis indicates small fires exhibit strong clustering (Moran’s I = 0.270, p < 0.01), whereas large fires are spatially dispersed. The study concludes that GFED4 is reliable for monitoring large fires in forested zones but should be applied cautiously in non-forested and small-fire-dominated regions. Full article

The accurate motion of roller-bearing-supported rings is critically influenced by shape and positional tolerances, which are often underestimated in conventional modeling approaches. The aim of this study is to develop and validate a multibody dynamic framework capable of quantifying the impact of roundness and positional errors on the motion accuracy of roller-bearing-supported rings. Shape errors are modeled using Fourier series and incorporated into a high-fidelity multibody simulation environment. Experimental validation using laser triangulation reveals a maximum runout error of 72.9 μm, compared to a numerically predicted value of 88.6 μm, resulting in a quantified numerical overestimation of 21.5%. Parametric studies investigated the effects of harmonic order, error amplitude, and combined error scenarios on key performance metrics, including trajectory runout and initial offset displacement. Results reveal that the trajectory errors range between 0.29 mm and 0.63 mm for shape error orders and can escalate to 2.84 mm for high amplitude errors, demonstrating the critical role of error order and amplitude. Furthermore, combined simulations show that bearing position errors exert a more pronounced effect on radial accuracy than shape deviations alone. The proposed approach enables precision design evaluation and tolerance optimization in high-accuracy applications, including robotics, aerospace mechanisms, and optical alignment systems. Full article

This study investigates the sensitivity of an urban parameterization scheme of the Weather Research and Forecasting model (WRF). The model sensitivity is tested during the period April–May 2020 over the greater Paris region. The parent domain covers Europe with a 12 km horizontal resolution, with a nested one covering the greater Paris region with a 3 km horizontal resolution. A multi-layer urban scheme called Building Effect Parameterization coupled with the Building Energy Model (BEP-BEM) was applied in two simulations: (1) BEP-BEM Paris, with urban options tailored for the Paris region, which were derived from Earth Observation data, and (2) BEP-BEM Europe, which uses an updated urban parameter table with an estimated average profile for European cities. These two simulations were compared with observations and a WRF simulation using a simple urban parameterization (BULK approach). BULK and multi-layer urban scheme experiments present a similar general error for April, underestimating temperature, while the BEP-BEM runs overestimate temperature for May. The simulation with the advanced tailored urban parameterization over Paris appears to have the best overall performance in this 2-month period. Full article

Today, as planners and urban designers increasingly rely on computational modeling to study complex urban systems, a methodological shift toward virtual experimentation is discernible because the real-world factors are difficult to control. This paper investigates the effect of the realism of computer simulations on distance perception in urban squares and streets. This study used Autodesk 3ds Max^® for modeling and V-Ray for rendering to create systematic variations in distances, with 172 participants providing distance estimates for 216 images. Results indicated that realism had a significant effect on distance perception, increasing estimation accuracy from r = 0.8 to r = 0.94. Lower realism was always associated with an underestimation of the distance, whereas higher realism manifested both overestimation and underestimation. Underestimation is dominant at long distances (>20 m), attributable to a lack of cues, common in low realism; overestimation happens only for short distances (≤20 m) due to high realism. These findings underscore the importance of simulation fidelity for urban designers and planners, enhancing the validity of virtual tools in design, research, and decision-making. Full article

Formation damage sensitivity is a primary constraint on productivity in coalbed methane (CBM) reservoirs. Conventional experimental methods, which often employ crushed or plug coal samples, disrupt the natural fracture network, thereby overestimating matrix damage and underestimating fracture-related damage. In this study, synchronous comparative experiments were conducted using raw coal and briquette coal cores, integrated with scanning electron microscopy (SEM) and nuclear magnetic resonance (NMR) analyses to characterize coal composition and pore structure. This approach elucidates the underlying mechanisms controlling reservoir sensitivity. The main findings are as follows: The dual-sample comparative system reveals substantial deviations in traditional experimental assessments. Due to post-dissolution compaction, briquette coal samples overestimate acid sensitivity while underestimating water sensitivity. Stress sensitivity is primarily attributed to the irreversible compression of natural fractures. Differences in acid sensitivity are governed by structural integrity: mineral dissolution leads to collapse in briquette coal, whereas fractures help maintain stability in raw coal. Raw coal exhibits a lower critical flow rate for velocity sensitivity and undergoes significant water sensitivity damage below 1 MPa. Both sample types show weak alkaline sensitivity, with damage acceleration observed within the pH range of 7 to 10. Full article

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

Fluctuations in precipitation usually affect the ecological environment and human socioeconomics through events such as floods and droughts, resulting in substantial economic losses. The high-resolution models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are vital for simulating precipitation patterns in China; however, significant uncertainties still exist. This study utilized the quantile mapping (QM) method to correct biases in nine high-resolution Earth System Models (ESMs) and then comprehensively evaluated their precipitation simulation capabilities over the Chinese mainland from 1985 to 2014. Based on the selected models, the Bayesian Model Averaging (BMA) method was used to integrate them to obtain the spatial–temporal variation in precipitation over the Chinese mainland. The results showed that the simulation performance of nine models for precipitation from 1985 to 2014 was significantly improved after the bias correction. Six out of the nine high-resolution ESMs were selected to generate the BMA ensemble model. For the BMA model, the precipitation trend and the locations of significant points were more closely aligned with the observational data in the summer than in other seasons. It overestimated precipitation in the spring and winter, while it underestimated it in the summer and autumn. Additionally, both the BMA model and the worst multi-model ensemble (WMME) model exhibited a negative mean bias in the summer, while they displayed a positive mean bias in the winter. And the BMA model outperformed the WMME model in terms of mean bias and bias range in both the summer and winter. Moreover, high-resolution models delivered precipitation simulations that more closely aligned with observational data compared to low-resolution models. Full article

Changes in the amount and distribution of rainfall highly impact agricultural production in predominantly rainfed farming systems in Africa. Reliable rainfall forecasts on a daily timescale are vital for in-season decision-making. This study evaluated the relative prediction abilities of the European Centre for Medium-Range Weather Forecasts Season 5.1 (ECMWFSv5.1) and the Climate Forecast System version 2 (CFSv2) gridded rainfall models across Africa and three sub-regions from 2012–2022. The results indicate that the performance of both models declines with increasing lead times and improves with aggregated or coarser temporal resolutions. ECMWFv5.1 consistently represented observed daily rainfall better than CFSv2 at all lead times, particularly in West Africa. On dekadal timescales, ECMWFv5.1 outperformed CFSv2 across all sub-regions. CFSv2 tended to overestimate low- and high-intensity rainfall events, whereas ECMWFv5.1 slightly underestimated low-intensity rainfall but accurately captured high-intensity events. While ECMWFv5.1 showed superior skill overall, model reliability was generally limited to West Africa; in contrast, both models performed poorly in East Africa. The high probability of detection (POD) indicates that the models are generally effective at identifying rainy days. However, their overall accuracy in forecasting rainfall across Africa varies depending on lead time, region, rainfall intensity, and elevation. While we did not apply bias-correction methods in this study, we recommend that such techniques be used in future work to improve the reliability of forecasts for operational and sectoral applications. This study therefore highlights both the strengths and the limitations of CFSv2 and ECMWFv5.1 for climate impact assessments, particularly in West Africa and low-elevation regions. Full article

Background/Objectives: Comparison between the 10-year probability of major osteoporotic fracture (MOF) calculated with FRAX (pFRAX) and the actual MOF rate was conducted, and the availability of pFRAX was evaluated with a one-center cohort study. Methods: Eligible patients were followed up for 10 years. Risk factors listed as items in the FRAX, and presence of lifestyle-related diseases (LS-RDs), escalated ability to fall (Fall-ability), cognitive impairment (CI), etc., were evaluated concerning MOF. The 10-year probability and actual MOF rate were compared. Risk factors contributing to the discrepancy between the probability and the actual rate were evaluated after dividing subgroups. Results: The study included 931 patients. Factors that contributed to the significantly higher ratio for incident MOF besides items in the FRAX were LS-RD, Fall-ability, CI, and anti-osteoporotic drug intervention. The higher the number of factors presented, the higher the actual MOF prevalence compared to the probability rise. Presenting LS-RD, Fall-ability, and CI are independent of the items in the FRAX. pFRAX was overestimated in the low-risk groups and underestimated in the high-risk group compared to the actual MOF rate. These phenomena are caused by the lack of consideration of these three comorbidity risks. Conclusions: A discrepancy between pFRAX and the actual MOF rate exists. LS-RD, Fall-ability, and CI should be listed in the items of the FRAX for more concision. Full article

Low-temperature oxidation of titanomagnetite in oceanic basalts distorts the primary thermoremanent magnetization (TRM) signal essential for reconstructing Earth’s magnetic field history, though the specific impact of magma intrusion-induced oxidation on paleointensity preservation remains poorly constrained. This investigation simulates such oxidation processes using a novel experimental design involving isothermal annealing (260 $°$C; 50 µT field; durations 12.5–1300 h) of Red Sea rift basalts (P72/4), employing the Thellier-Coe method to quantify how chemical remanent magnetization (CRM) overprinting affects TRM fidelity under controlled field orientations aligned either parallel or perpendicular to the initial TRM. Results demonstrate two-sloped Arai-Nagata diagrams with reliable TRM preservation below 360 $°$C but significant alteration artifacts above this threshold. Crucially, field orientation during oxidation critically influences accuracy: parallel configurations maintain fidelity (±3% deviation at $Z=0.48$), while perpendicular fields introduce systematic biases (38% overestimation at $Z=0.15$; 20% underestimation at $Z>0.48$), which is attributable to magnetostatic interactions in core-shell grain structures. These findings establish that paleointensity reliability in basalt prone to low-temperature oxidation depends fundamentally on the alignment between oxidation-era magnetic fields and primary TRM direction, necessitating stringent sample selection and directional constraints in marine paleomagnetic research to mitigate CRM-TRM interference. Full article

Reliable, high-resolution emission inventories are essential for accurately simulating air quality and for designing evidence-based mitigation policies. Yet their performance over Japan—where transboundary inflow, strict fuel regulations, and complex source mixes coexist—remains poorly quantified. This study therefore benchmarks four widely used anthropogenic inventories—REAS v3.2.1, CAMS-GLOB-ANT v6.2, ECLIPSE v6b, and HTAP v3—by coupling each to WRF-Chem (10 km grid) and comparing simulated surface PM₂.₅, SO₂, CO, and NO_x with observations from >900 stations across eight Japanese regions for the years 2010 and 2015. All simulations shared identical meteorology, chemistry, and natural-source inputs (MEGAN 2.1 biogenic VOCs; FINN v1.5 biomass burning) so that differences in model output isolate the influence of anthropogenic emissions. HTAP delivered the most balanced SO₂ and CO fields (regional mean biases mostly within ±25%), whereas ECLIPSE reproduced NO_x spatial gradients best, albeit with a negative overall bias. REAS captured industrial SO₂ reliably but over-estimated PM₂.₅ and NO_x in western conurbations while under-estimating them in rural prefectures. CAMS-GLOB-ANT showed systematic biases—under-estimating PM₂.₅ and CO yet markedly over-estimating SO₂—highlighting the need for Japan-specific sulfur-fuel adjustments. For several pollutant–region combinations, absolute errors exceeded 100%, confirming that emissions uncertainty, not model physics, dominates regional air quality error even under identical dynamical and chemical settings. These findings underscore the importance of inventory-specific and pollutant-specific selection—or better, multi-inventory ensemble approaches—when assessing Japanese air quality and formulating policy. Routine assimilation of ground and satellite data, together with inverse modeling, is recommended to narrow residual biases and improve future inventories. Full article

Background: Dental age (DA) estimation, which is widely used in orthodontics, pediatric dentistry, and forensic dentistry, predicts chronological age (CA) by assessing tooth development and maturation. Most methods rely on radiographic evaluation of tooth mineralization and eruption stages to assess DA. With the increasing adoption of large language models (LLMs) in medical sciences, use of ChatGPT has extended to processing visual data. The aim of this study, therefore, was to evaluate the performance of ChatGPT-4 in estimating DA from panoramic radiographs using three conventional methods (Nolla, Haavikko, and London Atlas) and to compare its accuracy against both orthodontist assessments and CA. Methods: In this retrospective study, panoramic radiographs of 511 Turkish children aged 6–17 years were assessed. DA was estimated using the Nolla, Haavikko, and London Atlas methods by both orthodontists and ChatGPT-4. The DA–CA difference and mean absolute error (MAE) were calculated, and statistical comparisons were performed to assess accuracy and sex differences and reach an agreement between the evaluators, with significance set at p < 0.05. Results: The mean CA of the study population was 12.37 ± 2.95 years (boys: 12.39 ± 2.94; girls: 12.35 ± 2.96). Using the London Atlas method, the orthodontists overestimated CA with a DA–CA difference of 0.78 ± 1.26 years (p < 0.001), whereas ChatGPT-4 showed no significant DA–CA difference (0.03 ± 0.93; p = 0.399). Using the Nolla method, the orthodontist showed no significant DA–CA difference (0.03 ± 1.14; p = 0.606), but ChatGPT-4 underestimated CA with a DA–CA difference of −0.40 ± 1.96 years (p < 0.001). Using the Haavikko method, the evaluators underestimated CA (orthodontist: −0.88; ChatGPT-4: −1.18; p < 0.001). The lowest MAE for ChatGPT-4 was obtained when using the London Atlas method (0.59 ± 0.72), followed by Nolla (1.33 ± 1.28) and Haavikko (1.51 ± 1.41). For the orthodontists, the lowest MAE was achieved when using the Nolla method (0.86 ± 0.75). Agreement between the orthodontists and ChatGPT-4 was highest when using the London Atlas method (ICC = 0.944, r = 0.905). Conclusions: ChatGPT-4 showed the highest accuracy with the London Atlas method, with no significant difference from CA for either sex or the lowest prediction error. When using the Nolla and Haavikko methods, both ChatGPT-4 and the orthodontist tended to underestimate age, with higher errors. Overall, ChatGPT-4 performed best when using visually guided methods and was less accurate when using multi-stage scoring methods. Full article

This study investigated the tensile behaviors of 12.70 mm and 15.20 mm diameter anchor cable specimens with ultimate tensile strengths of 1860 MPa and their material specimens through experiments and finite element (FE) simulations. Material specimens and anchor cable specimen tensile samples were prepared, and the complete engineering stress–strain curves were obtained via uniaxial tensile tests. FE analysis was used to simulate the uniaxial tensile tests, and the applicability of different constitutive models for describing the true stress–strain relationships was evaluated by comparing the simulated and experimental engineering stress–strain curves. The results showed that the Ludwik, Hollomon, and Swift models, fitted using the pre-necking hardening stage, overestimated the post-necking true stress, while the Voce model underestimated it. In contrast, the Ling and Swift + Voce models provided accurate post-necking true stress predictions. Based on the Ling model and the Rice and Tracey fracture criterion, the load–displacement relationship and fracture behavior of the 12.7 mm anchor cable specimen were best described with W = −0.1 and a = 2, whereas W = −0.1 and a = 3 yielded optimal predictions for the 15.2 mm anchor cable specimen. Full article