Search Results (777)

The link between occupational exposure to Respirable Crystalline Silica (RCS) and silicosis, a potentially fatal respiratory disease, has been well-established, leading to global reductions in RCS Exposure Standards (ES). In Western Australia (WA), RCS data have been collected by the Department of Energy, Mining, Industry Regulation and Safety (DEMIRS) from 1986 to 2024 (n = 144,141). These results were analysed to assess the impacts of recent changes to the ES on compliance. Findings suggest that the WA mining sector, regardless of commodity type, is compliant with RCS exposures as assessed against the 0.05 mg/m³ ES (2019). Laboratory technicians, exploratory drilling, miscellaneous trades/utilities, trades assistant, sample preparation, and sampler/sample operator are SEGS that had the highest RCS exposures. Exposure assessment did not account for the protection provided by respiratory protective equipment (RPE). In the WA mining sector, a robust respiratory protection regime is enforced that includes respirator fit testing, and this is most likely the case throughout Australia. On the balance of epidemiological evidence, industry compliance over decades, reducing exposure profiles, and robust RPE programmes, it could be argued that further reductions to the RCS exposure standard are not justified. Regulators need to consider the protection provided by respirators in exposure assessment. Full article

Sediment erosion under turbulent wave action is a highly dynamic process shaped by the interaction between wave properties and sediment characteristics. Despite extensive empirical research, the underlying mechanisms of wave-induced erosion remain insufficiently understood, particularly regarding the threshold energy required for particle mobilization and the factors governing displacement patterns. This study employed a custom-built wave flume and a 3D-printed sampler to examine sediment behavior under controlled wave conditions. Rounded glass beads, chosen to eliminate the influence of particle shape, were used as sediment analogs with a similar specific gravity to natural sand. Ten experiments were conducted to systematically assess the effects of particle size, particle number, input voltage (wave power), and water depth on sediment response. The results revealed that (1) only a fraction of particles were mobilized, with the remainder forming stable interlocking structures; (2) the number of displaced particles increased with particle size, particle count, and water depth; (3) a threshold wave power is required to initiate erosion, though buoyancy under shallow conditions reduces this threshold; and (4) wave steepness, rather than voltage or wave height alone, provided the strongest predictor of sediment displacement. These findings highlight the central role of wave steepness in erosion modeling and call for its integration into predictive frameworks. The study concludes with methodological limitations and proposes future research directions, including expanded soil types, large-scale flume testing, and advanced flow field measurements. Full article

This study investigates the atmospheric concentrations, spatiotemporal distribution, the associated health risks and the ozone formation potential of benzene, toluene, ethylbenzene and xylenes (BTEX) across 33 monitoring sites of Greece over a one-year period. Samples were collected using passive diffusive samplers and analyzed by gas chromatography–mass spectrometry (GC-MS). The highest BTEX concentrations were detected during winter and autumn, particularly in urban and industrial areas such as in the Attica and Thessaloniki regions, likely due to enhanced emissions from combustion-related activities and reduced atmospheric dispersion. Health risk assessment revealed that hazard quotient (HQ) values for all compounds were within the acceptable limits. However, lifetime cancer risk (LTCR) for benzene exceeded the recommended limits in multiple regions during the colder seasons, indicating notable public health concern. Source apportionment using diagnostic ratios suggested varying seasonal emission sources, with vehicular emissions prevailing in winter and marine or industrial emissions in summer. Xylenes and toluene exhibited the highest ozone formation potential (OFP), underscoring their role in secondary pollutant formation. These findings demonstrate the need for seasonally adaptive air quality strategies, especially in Mediterranean urban and semi-urban environments. Full article

A fundamental limitation of maximum likelihood and Bayesian methods under model misspecification is that the asymptotic covariance matrix of the pseudo-true parameter vector ${\mathsf{\theta }}_{*}$ is not the inverse of the Fisher information, but rather the sandwich covariance matrix ${\displaystyle \frac{1}{n}}{\mathbf{A}}_{*}^{-1}{\mathbf{B}}_{*}^{\phantom{-1}}{\mathbf{A}}_{*}^{-1}$, where ${\mathbf{A}}_{*}$ and ${\mathbf{B}}_{*}$ are the sensitivity and variability matrices, respectively, evaluated at ${\mathsf{\theta }}_{*}$ for training data record ${\omega }_1,\dots ,{\omega }_n$. This paper makes three contributions. First, we review existing approaches to robust posterior sampling, including the open-faced sandwich adjustment and magnitude- and curvature-adjusted Markov chain Monte Carlo (MCMC) simulation. Second, we introduce a new sandwich-adjusted MCMC method. Unlike existing approaches that rely on arbitrary matrix square roots, eigendecompositions or a single scaling factor applied uniformly across the parameter space, our method employs a parameter-dependent learning rate $\lambda \left(\mathsf{\theta }\right)$ that enables direction-specific tempering of the likelihood. This allows the sampler to capture directional asymmetries in the sandwich distribution, particularly under model misspecification or in small-sample regimes, and yields credible regions that remain valid when standard Bayesian inference underestimates uncertainty. Third, we propose information-theoretic diagnostics for quantifying model misspecification, including a strictly proper divergence score and scalar summaries based on the Frobenius norm, Earth mover’s distance, and the Herfindahl index. These principled diagnostics complement residual-based metrics for model evaluation by directly assessing the degree of misalignment between the sensitivity and variability matrices, ${\mathbf{A}}_{*}$ and ${\mathbf{B}}_{*}$. Applications to two parametric distributions and a rainfall-runoff case study with the Xinanjiang watershed model show that conventional Bayesian methods systematically underestimate uncertainty, while the proposed method yields asymptotically valid and robust uncertainty estimates. Together, these findings advocate for sandwich-based adjustments in Bayesian practice and workflows. Full article

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

Using Bayesian inference to calibrate constitutive model parameters has recently seen a rise in interest. The Markov chain Monte Carlo (MCMC) algorithm is one of the most commonly used methods to sample from the posterior. However, the choice of which MCMC algorithm to apply is typically pragmatic and based on considerations such as software availability and experience. We compare three commonly used MCMC algorithms: Metropolis-Hastings (MH), Affine Invariant Stretch Move (AISM) and No-U-Turn sampler (NUTS). For the comparison, we use the Kullback-Leibler (KL) divergence as a convergence criterion, which measures the statistical distance between the sampled and the ‘true’ posterior. We apply the Bayesian framework to a Newtonian squeeze flow problem, for which there exists an analytical model. Furthermore, we have collected experimental data using a tailored setup. The ground truth for the posterior is obtained by evaluating it on a uniform reference grid. We conclude that, for the same number of samples, the NUTS results in the lowest KL divergence, followed by the AISM sampler and last the MH sampler. Full article

Rapid access to defibrillators, blood products, and time-critical medicines can improve survival, yet urban congestion and fragmented infrastructure delay deliveries. We present and evaluate an end-to-end framework for beyond-visual-line-of-sight (BVLOS) UAV logistics in Essex (UK), integrating (I) strategic depot placement, (II) a hybrid obstacle-aware route planner, and (III) a time-window-aware (TWA) Mixed-Integer Linear Programming (MILP) scheduler coupled to a battery/temperature feasibility model. Four global planners—Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Rapidly Exploring Random Tree* (RRT*)—are paired with lightweight local refiners, Simulated Annealing (SA) and Adaptive Large-Neighbourhood Search (ALNS). Benchmarks over 12 destinations used real Civil Aviation Authority no-fly zones and energy constraints. RRT*-based hybrids delivered the shortest mean paths: RRT* + SA and RRT* + ALNS tied for the best average length, while RRT* + SA also achieved the co-lowest runtime at $v=60\mathrm{km}{\mathrm{h}}^{-1}$. The TWA-MILP reached proven optimality in 0.11 s, showing that a minimum of seven UAVs are required to satisfy all 20–30 min delivery windows in a single wave; a rolling demand of one request every 15 min can be sustained with three UAVs if each sortie (including service/recharge) completes within 45 min. To validate against a state-of-the-art operations-research baseline, we also implemented a Vehicle Routing Problem with Time Windows (VRPTW) in Google OR-Tools, confirming that our hybrid planners generate competitive or shorter NFZ-aware routes in complex corridors. Digital-twin validation in AirborneSIM confirmed CAP 722-compliant, flyable trajectories under wind and sensor noise. By hybridising a fast, probabilistically complete sampler (RRT*) with a sub-second refiner (SA/ALNS) and embedding energy-aware scheduling, the framework offers an actionable blueprint for emergency medical UAV networks. Full article

The annual bluegrass weevil (ABW), Listronotus maculicollis (Kirby), is a significant pest of short-mown turfgrass in eastern North America. Proper monitoring of this pest may reduce insecticide applications. We investigated the extraction rate and labor involved in modifications of the two currently available methods for sampling ABW larvae: submersion of turf cores in saturated saline solution and heat extraction of cores on modified Berlese funnels. Using 5.7 cm diameter cores, submersion extracted 1.8× more larvae in saline solution than in water. Among the salt extraction variants, splitting the cores into four pieces before submersion was the best compromise between extraction rate and time requirement. Using intact cores extracted 40% fewer larvae while taking 18% less time, whereas destructive searching cores before submersion extracted 24% more larvae but required 64% more time. Using smaller cores (3.5 cm diam) took 18% less time and extracted 23% more larvae, but required more time sampling in the field. Larval stage averages did not differ significantly between salt extraction variants. Heat extraction, including destructively searching the desiccated core, extracted 60% more larvae but required 87% more time than four-piece salt extraction. Excluding the desiccated core, heat extracted as many larvae as four-piece salt extraction and required 16% less time. However, heat extraction requires three to four days and space that can be kept at around 32 °C. The method of choice for ABW larval extractions depends on whether the sampler prefers a high extraction rate, less labor, or quicker results, and whether space for heat extractions is available. Full article

Oral fluid sampling is a well-established, non-invasive method for disease surveillance in growing pigs; however, its application in group-housed gestating sows is under-researched. This study (1) characterized sow behaviors associated with oral fluid sampling and (2) documented the transfer of an environmental target into pen-based oral fluid samples. Field observations were conducted on a commercial sow farm in 12 pens of gestating sows sorted by parity (gilts, parity one, and multiparous sows). Sow oral fluid sampling behaviors were quantified by recording interactions with rope samplers using video cameras and then analyzing the recorded footage. All oral fluid sampling attempts were successful. Unlike growing pigs, experience with rope samplers (“training”) did not increase sow participation, but participation in oral fluid collection did increase as sampling time increased. The transfer of environmental components into oral fluid samples was demonstrated by introducing a fluorescent tracer into the pen and then detecting specific fluorescence in the samples (8 of 12 pens). These findings support the implementation of oral fluid sampling in group-housed sows and provide practical recommendations for optimizing surveillance protocols, including extended sampling times and use of at least two ropes per pen. Full article

This study presents a semi-quantitative characterization of volatile organic compound (VOC) concentrations and their emission sources in indoor and outdoor environments across four residential and laboratory sites in Milan, Italy, during the summer of 2024. Radiello^® passive samplers (Fondazione Salvatore Maugeri in Padova, Italy) were employed for VOC collection, followed by gas chromatography–mass spectrometry analysis. The semi-quantitative mean total VOC (TVOC) concentration was 220.8 ± 195.4 µg/m³ for the outdoor air and slightly higher at 243.6 ± 134.3 µg/m³ for the indoor air, resulting in an indoor-to-outdoor relative ratio of 1.10. The outdoor VOC profile was dominated by hydrocarbons, accounting for 80.3% ± 4.6% (173.2 ± 143.8 µg/m³) of TVOCs, followed by aromatic hydrocarbons at 13.3% ± 5.5% (37.2 ± 49.7 µg/m³). Indoors, hydrocarbons also predominated, representing 34.1% ± 15.2% (95.2 ± 80.1 µg/m³) of the TVOCs, followed by terpenes at 20.7% ± 15.5% (49.0 ± 46.4 µg/m³). Other VOC groups contributed smaller fractions in both environments. The emission profiles from cleaning and personal care products were assessed semi-quantitatively to determine their relative percentage contributions to the indoor VOCs. Source attribution was further supported by diagnostic relative ratios—benzene/toluene, toluene/benzene, and (m + p)-xylene/ethylbenzene—which provided insight into dominant emission sources and photochemical aging. Full article

The chemical composition of bottom sediments in the northern part of Lake Umbozero, located in close proximity to a closed rare metal mine in the Murmansk Region, was studied. This study is a continuation of our research into the impact of closed rare metal mines and tailings on the environment. Samples were collected using an open gravity sampler in two sections of the lake in three replicates. The content of rare earth elements was determined using inductively coupled plasma mass spectrometry. The total content of elements was determined both in the surface layers of bottom sediments and in the deep layers that were formed in the preindustrial period and, thus, characterize the geochemical background of the study area. The average ∑REE in the surface layers of bottom sediments of Lake Umbozero in the wastewater reception area (Site 1) reaches 774 mg/kg, while for the area located north of the discharge site (Site 2), ∑REE was 208 mg/kg. The enrichment factor (EF), the geoaccumulation index (I_geo), the coefficient of the index of potential ecological risk (E_ir) and the index of potential ecological hazard (RI) were calculated. Assessing the total pollution of bottom sediments of Lake Umbozero with rare earth elements, the value of potential ecological risk reaches values corresponding to the level of low and moderate ecological risk of pollution (RI_{Site 1} = 164; RI_{Site 2} = 104). The conducted correlation analysis allowed us to establish the main phases containing rare earth elements in the bottom sediments of Lake Umbozero—oxyhydroxide complex compounds with iron and manganese. Full article

The aim of this study was to assess the presence of heterogeneity of variance in milk yield in the first lactation of buffaloes and its subsequent influence on the genetic evaluation of Murrah breed sires. The analysis utilized a dataset comprising 2392 milk yield records of buffaloes involved in the Programa de Melhoramento de Búfalos do Brasil. The standard deviation classes were established by standardizing the averages of contemporary group levels, with positive values constituting the high standard deviation class and values equaling or less than zero comprising the low standard deviation class. The linear mixed model incorporated fixed effects of sire group, buffalo age at calving, and heterozygosity as covariates, along with additive genetic random effects. Variance components were estimated via Bayesian inference employing the Gibbs sampler to derive posterior means. The average posterior heritability obtained in analyses without considering heterogeneity of variances (i.e., the “general analysis”) was 0.21, while the averages 0.19 and 0.34 were obtained for the low and high standard deviation classes, respectively. The genetic correlation between standard deviation classes was 0.61. The genetic correlation estimates between the predictions of breeding values for milk yield were more closely aligned between the predictions obtained in the general analysis with the low standard deviation class, and more discrepant between the two standard deviation classes. In the animal genetic evaluation model, when heterogeneity of variance is disregarded, the variance components are substantially weighted towards the performance of individuals in the low phenotypic variability class. By disregarding the presence and heterogeneity of variance, the breeding values of the best sires were underestimated. 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

The rapid rise of the prices of cryptocurrencies has intensified the need for robust forecasting models that can capture the irregular and volatile patterns. This study aims to forecast Bitcoin prices over a 15-day horizon by evaluating and comparing two distant predictive modeling approaches: the Bayesian State-Space model and Long Short-Term Memory (LSTM) neural networks. Historical price data from January 2024 to April 2025 is used for model training and testing. The Bayesian model provided probabilistic insights by achieving a Mean Squared Error (MSE) of 0.0000 and a Mean Absolute Error (MAE) of 0.0026 for training data. For testing data, it provided 0.0013 for MSE and 0.0307 for MAE. On the other hand, the LSTM model provided temporal dependencies and performed strongly by achieving 0.0004 for MSE, 0.0160 for MAE, 0.0212 for RMSE, 0.9924 for R² in terms of training data and for testing data, and 0.0007 for MSE with an R² of 0.3505. From the result, it indicates that while the LSTM model excels in training performance, the Bayesian model provides better interpretability with lower error margins in testing by highlighting the trade-offs between model accuracy and probabilistic forecasting in the cryptocurrency markets. Full article