Search Results (62)

To address limitations in structural preservation and detail fidelity in existing text-driven image editing methods, we propose MSHEdit—a novel editing framework built upon a pre-trained diffusion model. MSHEdit is designed to achieve high semantic alignment during image editing without the need for additional training or fine-tuning. The framework integrates two key components: the High-Order Stable Diffusion Sampler (HOS-DEIS) and the Multi-Scale Window Residual Bridge Attention Module (MS-WRBA). HOS-DEIS enhances sampling precision and detail recovery by employing high-order integration and dynamic error compensation, while MS-WRBA improves editing region localization and edge blending through multi-scale window partitioning and dual-path normalization. Extensive experiments on public datasets including DreamBench-v2 and DreamBench++ demonstrate that compared to recent mainstream models, MSHEdit reduces structural distance by 2% and background LPIPS by 1.2%. These results demonstrate its ability to achieve natural transitions between edited regions and backgrounds in complex scenes while effectively mitigating object edge blurring. MSHEdit exhibits excellent structural preservation, semantic consistency, and detail restoration, providing an efficient and generalizable solution for high-quality text-driven image editing. Full article

The volumetric Hirst method is considered a golden standard in aerobiology for determining particle number concentrations of bioaerosols. Using Hirst-type pollen and spore traps on mobile platforms (i.e., aircraft, cars, motorbikes, bicycles or carried by pedestrians) is anticipated to significantly enhance the spatial and temporal granularity of data for bioaerosol monitoring. Mobile sampling promises to enhance our understanding of bioaerosol dynamics, ecological interactions and the impact of human activities on airborne biological particles. In this article, we present the design and test of an airborne Hirst-type volumetric sampler. We followed a structured approach and incorporated the fundamental principles of the original design, while optimizing for size, weight, power and cost. Our portable Hirst-type volumetric sampler (FlyHirst) was attached to an ultralight aircraft, together with complementing instrumentation, and was tested for collection of atmospheric concentrations of pollen, fungal spores and hyphae. By linking the temporal resolution of the samples with the spatial position of the aircraft, using flight time, we calculated the spatial resolution of our measurements in 3D. In six summer flights over Denmark, our study revealed that the diversity of the recorded spores corresponded to the seasonal expectance. Urtica pollen was recorded up to 1300 m above ground (a.g.l.), and fungal spores up to 2100 m a.g.l. We suggest that, based on this proof-of-concept, FlyHirst can be applied on other mobile platforms or as a personal sampler. Full article

We present new spectroscopic orbits for the bright binaries Mizar B, 3 Pup, $\nu$ Gem, 2 Lac, and $\varphi$ Aql. Our analysis is based on medium-resolution ($R\approx$ 12,000) échelle spectra obtained with the 0.81-m telescope and fiber-fed eShel spectrograph of the Three College Observatory (Greensboro, NC, USA) between 2015 and 2024. Orbital elements were inferred with an affine-invariant Markov-chain Monte-Carlo sampler; convergence was verified through the integrated autocorrelation time and the Gelman–Rubin statistic. Errors quote the 16th–84th-percentile credible intervals. Compared with previously published orbital solutions for the studied stars, our method improves the root-mean-square residuals by 25–50% and bring the 1$\sigma$ uncertainties on the radial velocity (RV) semi-amplitudes down to 0.02–0.15 km $s^{-1}$. These gains translate into markedly tighter mass functions and systemic RVs, providing a robust dynamical baseline for future interferometric and photometric studies. A complete Python analysis pipeline is openly available in a GitHub repository, ensuring full reproducibility. The results demonstrate that a Bayesian RV analysis with well-motivated priors and rigorous convergence checks yields orbital parameters that are both more precise and more reproducible than previous determinations, while offering fully transparent uncertainty budgets. Full article

This paper presents an automatic oil sampling system designed for vertical cylindrical oil tanks on land, focusing primarily on the structural design and control optimization for oil level measurement and liquid sampling inside the tank. First, the key structure and control architecture of the automatic sampler are introduced, explaining the collaborative working principles of its components to ensure good stability in system structure and motion control. On this basis, an improved Sparrow Search Algorithm (CSSA) is proposed, which integrates the Coati Optimization Algorithm (COA) and the traditional Sparrow Search Algorithm (SSA). This algorithm is used to optimize the parameters of the Proportional–Integral–Derivative (PID) control system in the oil sampler, aiming to address issues such as response delay, large overshoot, and insufficient stability that commonly occur in traditional PID control under complex conditions. This method achieves consistent response behavior over time and adaptiveness in the control process by dynamically adjusting the PID parameters in real time. To verify the effectiveness of the proposed control strategy, system simulations were conducted in the MATLAB 2024B environment, and a physical experimental platform was built for testing. The simulation compares the CSSA-PID controller with traditional PID, COA-PID, and SSA-PID control methods. In addition, a load disturbance was introduced at 300 ms to perform anti-interference comparative simulations. The results show that under CSSA-PID control, the system response time was shortened by up to 112 ms, the convergence speed improved by 72.3%, the global optimization capability was significantly enhanced, and the anti-interference ability was stronger. In the actual tests, the average error was reduced by approximately 45.3%. These results indicate that CSSA-PID can significantly enhance the stability and response speed of the control system. The efficient control of the automatic oil sampler will greatly enhance the intelligence and efficiency of oil level detection in tanks and reduce labor costs, having significant implications for the development of the grain and oil storage industry. Full article

Ammonia (NH₃) emissions affect the environment, climate and human health and originate mainly from agricultural sources like synthetic nitrogen fertilizers. Accurate and replicable measurements of NH₃ emissions are crucial for research, inventories and evaluation of mitigation measures. There exist specific application limitations of NH₃ emission measurement techniques and a high variability in method performance between studies, in particular from small plots. Therefore, the aim of this study was the assessment of measurement methods for ammonia emissions from replicated small plots. Methods were evaluated in 18 trials on six sites in Germany (2021–2022). Urea was applied to winter wheat as an emission source. Two small-plot methods were employed: inverse dispersion modelling (IDM) with atmospheric concentrations obtained from Alpha samplers and the dynamic chamber Dräger tube method (DTM). Cumulative NH₃ losses assessed by each method were compared to the results of the integrated horizontal flux (IHF) method using Alpha samplers (Alpha IHF) as a micrometeorological reference method applied in parallel large-plot trials. For validation, Alpha IHF was also compared to IHF/ZINST with Leuning passive samplers. Cumulative NH₃ emissions assessed using Alpha IHF and DTM showed good agreement, with a relative root mean square error (rRMSE) of 11%. Cumulative emissions assessed by Leuning IHF/ZINST deviated from Alpha IHF, with an rRMSE of 21%. For low-wind-speed and high-temperature conditions, NH₃ losses detected with Alpha IDM had to be corrected to give acceptable agreement (rRMSE 20%, MBE +2 kg N ha⁻¹). The study shows that quantification of NH₃ emissions from small plots is feasible. Since DTM is constrained to specific conditions, we recommend Alpha IDM, but the approach needs further development. Full article

The stability of modern power systems has become critically dependent on precise inertia estimation of synchronous generators, particularly as renewable energy integration fundamentally transforms grid dynamics. Increasing penetration of converter-interfaced renewable resources reduces system inertia, heightening the grid’s susceptibility to transient disturbances and creating significant technical challenges in maintaining operational reliability. This paper addresses these challenges through a novel Bayesian inference framework that synergistically integrates PMU data with an advanced MCMC sampling technique, specifically employing the Affine-Invariant Ensemble Sampler. The proposed methodology establishes a probabilistic estimation paradigm that systematically combines prior engineering knowledge with real-time measurements, while the Affine-Invariant Ensemble Sampler mechanism overcomes high-dimensional computational barriers through its unique ensemble-based exploration strategy featuring stretch moves and parallel walker coordination. The framework’s ability to provide full posterior distributions of inertia parameters, rather than single-point estimates, helps for stability assessment in renewable-dominated grids. Simulation results on the IEEE 39-bus and 68-bus benchmark systems validate the effectiveness and scalability of the proposed method, with inertia estimation errors consistently maintained below $1\%$ across all generators. Moreover, the parallelized implementation of the algorithm significantly outperforms the conventional M-H method in computational efficiency. Specifically, the proposed approach reduces execution time by approximately $52\%$ in the 39-bus system and by $57\%$ in the 68-bus system, demonstrating its suitability for real-time and large-scale power system applications. Full article

Aerosol uniformity in the mixing chamber is one of the key factors in evaluating performance of aerosol samplers and accuracy of aerosol monitors which could output the direct reading of particle size or concentration. For obtaining high uniformity and a stable test aerosol sample during evaluation, a portable mixing chamber, where the sample and clean air were dual-vortex turbulent mixed, was designed. By using computational fluid dynamics (CFD), particle motion within the mixing chamber was illustrated or explained. By adjusting critical structure parameters of chamber such as height and diameter, the flow field structure was optimized to improve particle mixing characteristics. Accordingly, a novel portable aerosol mixing chamber with length and inner diameter of 0.7 m and 60 mm was developed. Through a combination of simulations and experiments, the operating conditions, including working flow rate, ratio of carrier/dilution clean air, and mixture duration, were studied. Finally, by using the optimized parameters, a mixing chamber with high spatial uniformity where variation is less than 4% was obtained for aerosol particles ranging from 0.3 μm to 10 μm. Based on this chamber, a standardized testing platform was established to verify the sampling efficiency of aerosol samplers with high flow rate (28.3 L·min⁻¹). The obtained results were consistent with the reference values in the sampler’s manual, confirming the reliability of the evaluation system. The testing platform developed in this study can provide test aerosol particles ranging from sub-micrometers to micrometers and has significant engineering applications, such as atmospheric pollution monitoring and occupational health assessment. Full article

The study at the Peggy Guggenheim Collection in Venice highlights critical interactions between indoor air quality, visitor dynamics, and microclimatic conditions, offering insights into preventive conservation of modern artworks. By analyzing pollutants such as ammonia, formaldehyde, and organic acids, alongside visitor density and environmental data, the research identified key patterns and risks. Through three seasonal monitoring campaigns, the concentrations of SO₂ (sulphur dioxide), NO (nitric oxide), NO₂ (nitrogen dioxide), NO_x (nitrogen oxides), HONO (nitrous acid), HNO₃ (nitric acid), O₃ (ozone), NH₃ (ammonia), CH₃COOH (acetic acid), HCOOH (formic acid), and HCHO (formaldehyde) were determined using passive samplers, as well as temperature and relative humidity data loggers. In addition, two specific short-term monitoring campaigns focused on NH₃ were performed to evaluate the influence of visitor presence on indoor concentrations of the above compounds and environmental parameters. NH₃ and HCHO concentrations spiked during high visitor occupancy, with NH₃ levels doubling in crowded periods. Short-term NH₃ campaigns confirmed a direct correlation between visitor numbers and the above indoor concentrations, likely due to human emissions (e.g., sweat, breath) and off-gassing from materials. The indoor/outdoor ratios indicated that several pollutants originated from indoor sources, with ammonia and acetic acid showing the highest indoor concentrations. By measuring the number of visitors and microclimate parameters (temperature and humidity) every 3 s, we were able to precisely estimate the causality and the temporal shift between these quantities, both at small time scale (a few minute delay between peaks) and at medium time scale (daily average conditions due to the continuous inflow and outflow of visitors). Full article

We investigate the impact of significant life events on two critical health outcomes: insomnia and obesity. Using data from the Household, Income, and Labour Dynamics in Australia (HILDA) survey, we focus on significant life events experienced in the preceding 12 months. To model these health outcomes jointly, we employ a bivariate random effects probit panel data model and a longitudinal random effects panel data model whose outcomes can be a combination of discrete/categorical and continuous variables. Estimating these random effects panel data models is challenging because the likelihood is an integral over the latent individual random effects. In addition, the models often have a large number of predictors. In this paper, Bayesian inference is carried out using a particle Metropolis within a Gibbs sampler, which is particularly well suited for statistical models with latent variables. Additionally, within this inference framework, we integrate a Hamiltonian Monte Carlo (HMC) step to sample the high-dimensional vector of regression coefficients efficiently. The HMC step enables faster convergence and improved mixing of the Markov chain. Our article contributes to a better understanding of how stress-related life events shape health outcomes and demonstrates the advantages of combining particle Metropolis within Gibbs and HMC in the estimation of complex panel data models. Full article

Complex air pollution, including particulate matter and ozone, is a significant environmental issue in China, with volatile organic compounds (VOCs) as key precursors. Traditional ground-based monitoring methods struggle to capture the vertical distribution and changes of pollutants in the troposphere. To address this, we developed a vertical monitoring and sampling platform using a quadcopter unmanned aerial vehicle (UAV). The platform, equipped with lightweight quartz sampling canisters and miniaturized sensors, collects air samples for VOC analysis and vertical data on meteorological parameters and particulate matter. Performance tests showed the quartz canisters had less than 15% adsorption loss, with sample storage stability exceeding 80% over three days. Sensor data showed strong correlations with standard instruments (R² > 0.80). Computational fluid dynamics simulations optimized the sampler’s inlet position and ascertained that ascending flight mitigates rotor-induced air recirculation. Field campaigns were conducted at six sites along the Chengdu Metropolitan Circle Ring Expressway. Vertical data from 0~300 m revealed particulate matter concentrations peaked at 50~70 m. Near-surface VOCs were dominated by alkanes, while aromatics were found concentrated at 150~250 m, indicating significant regional transport influences. The results confirmed the platform’s effectiveness for pollutant distribution analysis. Full article

The COVID-19 pandemic has highlighted the intricate nature of disease dynamics, extending beyond transmission patterns to the complex interplay of intervention strategies. In the post-COVID-19 era, reinfection has emerged as a critical factor, shaping how we model disease progression, evaluate immunity, and assess the effectiveness of public health interventions. This research uniquely explores the varied efficacy of existing vaccines and the pivotal role of vaccination timing in the context of COVID-19. Departing from conventional modeling, we introduce two models that account for the impact of vaccines on infections, reinfections, and deaths. We estimate model parameters under the Bayesian framework, specifically utilizing the Metropolis–Hastings Sampler. We conduct data-driven scenario analyses for the State of Qatar, quantifying the potential duration during which the healthcare system could have been overwhelmed by an influx of new COVID-19 cases surpassing available hospital beds. Additionally, the research explores similarities in predictive probability distributions of cumulative infections, reinfections, and deaths, employing the Hellinger distance metric. Comparative analysis, utilizing the Bayes factor, underscores the plausibility of a model assuming a different susceptibility rate to reinfection, as opposed to assuming the same susceptibility rate for both infections and reinfections. Results highlight the adverse outcomes associated with delayed vaccination, emphasizing the efficacy of early vaccination in reducing infections, reinfections, and deaths. Our research advocates for prioritization of early vaccination as a key strategy in effectively combating future pandemics, thereby providing vital insights for evidence-based public health interventions. Full article

Soil loss due to wind erosion and dust deposition has become a growing concern, particularly in arid regions like Al-Baha, Saudi Arabia. The aim of this study was to quantitatively assess soil loss and dust deposition using three different dust collection methods across 20 sites during the summer of 2022. The methods include Big Spring Number Eight (BSNE), which measures airborne dust particles using passive samplers; Surface Dust Collector (SDC), designed to collect dust settling on the ground surface; and Marble Dust Collector (MDCO), which utilizes marble-coated surfaces to trap and measure dust deposition. These methods collectively provide a comprehensive evaluation of dust dynamics in the study area. The objective was to evaluate the effects of wind erosion and dust deposition on soil properties, offering insights into the mechanisms of soil loss in arid environments. The study revealed significant variations in soil characteristics, including low organic matter content (<1%), high calcite (up to 19.62%), and increased salinity levels, with notable quantities of Cl⁻ (211.58 meq kg⁻¹) and Na^⁺ (165.98 meq kg⁻¹). July showed the highest dust deposition (0.0133 ton ha⁻¹), particularly at site S11, while soil loss was lowest at site S5. This research offers novel insights into the nonlinear relationship between soil loss and time, contributing to sustainable soil management strategies. By aligning with Saudi Arabia’s Vision 2030 and the Sustainable Development Goals (SDGs), the findings underscore the need to mitigate soil loss to enhance environmental sustainability, prevent desertification, and promote long-term resilience in arid regions. Full article

This research describes a vision-based control strategy that employs deep learning for an aerial manipulation system developed for vegetation sampling in remote, dangerous environments. Vegetation sampling in such places presents considerable technical challenges such as equipment failures and exposure to hazardous elements. Controlling aerial manipulation in unstructured areas such as forests remains a significant challenge because of uncertainty, complex dynamics, and the possibility of collisions. To overcome these issues, we offer a new image-based visual servoing (IBVS) method that uses knowledge distillation to provide robust, accurate, and adaptive control of the aerial vegetation sampler. A convolutional neural network (CNN) from a previous study is used to detect the grasp point, giving critical feedback for the visual servoing process. The suggested method improves the precision of visual servoing for sampling by using a learning-based approach to grip point selection and camera calibration error handling. Simulation results indicate the system can track and sample tree branches with minimum error, demonstrating that it has the potential to improve the safety and efficiency of aerial vegetation sampling. Full article

The Poaceae family, one of the most diverse and widespread angiosperms, is prevalent in various natural and urban environments and is a major cause of allergies, affecting over 20% of the population in Europe, specifically in Ireland. With extensive grasslands, Ireland supports numerous grass species, though pollen release varies due to the family’s complexity. The Hirst spore-trap is commonly used to sample airborne pollen, but the area of influence is debated and may differ by pollen type. This study compares grass pollen seasons between rural Carlow and urban Dublin, aiming to create forecast models for airborne pollen and identify key grass areas influencing the main pollen season (MPS). Two Hirst samplers were analyzed, using data up to 2020, and two threshold models (based on Swedish and Danish studies) were tested to find the best fit for Ireland. Airmass footprints were calculated using Hysplit and combined with grassland data to pinpoint major pollen sources. The results showed that Carlow had higher pollen concentrations but shorter seasons than Dublin. The Swedish threshold method was the most accurate for Ireland, with the Wicklow Mountains identified as a significant pollen source. These findings improve the understanding of pollen dynamics and support better public health and allergy management. Full article

A fast and sample cleanup approach for fluoxetine in human plasma was developed using protein precipitation coupled with LC–MS-MS. Samples were treated with methanol prior to LC–MS-MS analysis. Chromatographic separation was performed on a reverse phase column with an isocratic mobile phase of methanol and 10 mM ammonium formate pH acidified with formic acid (80:20, v/v) at a flow rate of 0.2 mL/min. The run time was 4 min. Mass parameters were optimized to monitor transitions at m/z [M + H]⁺ 310 > > 148 for fluoxetine and m/z [M + H]⁺ 315.1 > > 153 for fluoxetine-d5 as an internal standard. The lower limit of quantification and the dynamic range were 0.25 and 0.25–50 ng/mL, respectively. Linearity was good for intra-day and inter-day validations (R² = 0.999). The matrix effect was acceptable with CV% < 15 and accuracy% < 15. The hemolytic effect was negligible. Fluoxetine was stable in human plasma for 48 h at room temperature (25 °C), for 12 months frozen at −25 °C, for 48 h in an auto-sampler at 6 °C, and for three freeze/thaw cycles. The validated method was applied in a pharmacokinetic study to determine the concentration of fluoxetine in plasma samples. The study provides a fast and simple bioanalytical method for routine analysis and may be particularly useful for bioequivalence studies. The method was successfully applied to a pharmacokinetic study of fixed-dose fluoxetine in nine healthy volunteers. Full article