Search Results (5,309)

Quantum game theory merges principles from quantum mechanics with game theory, exploring how quantum phenomena such as superposition and entanglement can influence strategic decision making. It offers a novel approach to analyzing and optimizing complex systems where traditional game theory may fall short. Congestion of passengers, if considered as a network, may fall into the categories of optimization cases of quantum games. This paper explores the application of quantum potential games to minimize congestion in common areas at airports. The players/passengers of the airport have identical interests and they share the same utility function. A metric is introduced that considers a passenger’s visit to a common area by setting their preferences, in order to avoid congestion. Passengers can decide whether to visit a specific common area or choose an alternative. This study demonstrates that the proposed game is a quantum potential game for tackling congestion, with identical interests, ensuring the existence of a Nash equilibrium. We consider passengers to be players that want to ensure their interests. Quantum entanglement is utilized to validate the concept, and the results highlight the effectiveness of this approach. The objective is to ensure that not all passengers select the same common place of the airport to reduce getting crowded; hence, the airborne disease infection probability increases due to overcrowding. Our findings provide a promising framework for optimizing passenger flow and reducing congestion in airport common areas through quantum game theory. We showed that the proposed system is stable by encapsulating the Lyapunov stability. We compared it to a simulated annealing approach to show the efficacy of the quantum game approach. We acknowledge that this framework can be utilized in other disciplines as well. For our future work, we will research different strategies than binary ones to investigate the efficacy of the approach. Full article

The weather research and forecasting (WRF) model is frequently used to investigate the meteorological field around nuclear installations. The configuration of physical process parameterization schemes in the WRF model has a significant impact on the accuracy of the simulation results. Consequently, carrying out a pre-experiment to quickly obtain the optimal combination of parameterization schemes is essential before conducting meteorological parameter research. To obtain the optimal combination of physical process parameterization schemes from the planetary boundary layer (PBL), land surface (LSF), microphysical (MP), long-wave (LW), and short-wave (SW) radiation processes of the WRF model for simulating the near-surface meteorological variables near a nuclear power plant in Sanshan Town, Fuqing City, Fujian Province, China on 4 June 2019 were observed. Orthogonal experimental design (OED), a comprehensive evaluation method based on the CRiteria Import Through Intercriteria Correlation (CRITIC) weight analysis, and comprehensive balance method were employed for the first time to conduct the research. The sensitivity of meteorological variables to physical processes was first discussed. The findings revealed that the PBL scheme configuration had a profound impact on simulating wind fields. Furthermore, the LSF scheme configuration had a significant influence on simulating near-surface temperature and relative humidity, which was much greater than that of other physical processes. In addition, the choice of the radiation scheme had a significant impact on how the temperature was distributed close to the ground and how the wind field was simulated. Furthermore, the configuration of the MP scheme was found to exert a certain influence on the simulation of relative humidity; however, it demonstrated a weak influence on other meteorological variables. Secondly, The MYNN3 scheme for PBL process, the NoahMP scheme for LSF process, the WSM5 scheme for MP process, the RRTMG scheme for LW process, and the Dudhia scheme for SW process are found to be the comprehensive optimal physical process parameterization scheme combination for simulating meteorological variables in the research area selected in this study. As evident from the findings, the use of the OED method to obtain the combinations of the optimal physical process parameterization scheme could successfully reproduce the wind field, temperature, and relative humidity in the current study. Thus, this method appears to be highly reliable and effective for use in the WRF models to explore the optimal combinations of the physical process parameterization scheme, which could provide theoretical support to quickly analyzing accurate meteorological field data for longer periods and contribute to deeply investigating the migration and diffusion behavior of airborne pollutants in the atmosphere. Full article

Airborne microplastics are emerging pollutants originating from disposable tableware, packaging materials, textiles, and other consumer goods. Microplastics vary in shape and size and exposed to external factors break down into even smaller fractions. Airborne microplastics are abundant in both urban and natural environments, including water bodies and glaciers, as particles can travel long distances. The potential toxicity of airborne microplastics cannot be underestimated. Microparticles, especially those < 10 µm, entering the human body through inhalation or ingestion have been shown to cause serious adverse health effects, such as chronic inflammation, oxidation stress, physical damage to tissues, etc. Microplastics adsorb toxic chemicals and biopolymers, forming a polymer corona on their surface, affecting their overall toxicity. In addition, microplastics can also affect carbon dynamics in ecosystems and have a serious impact on biochemical cycles. The approaches to improve sampling techniques and develop standardized methods to assess airborne microplastics are still far from being perfect. The mechanisms of microplastic intracellular and tissue transport are still not clear, and the impact of airborne microplastics on human health is not understood well. Reduced consumption followed by collection, reuse, and recycling of microplastics can contribute to solving the microplastic problem. Combinations of different filtration techniques and membrane bioreactors can be used to optimize the removal of microplastic contaminants from wastewater. In this review we critically summarize the existing body of literature on airborne microplastics, including their distribution, identification, and safety assessment. Full article

A phase change materials (PCM)-based heat sink is an effective way to cool intermittent high-power electronic devices in aerospace applications such as airborne electronics and aircraft external carry. Optimizing the heat sink is significant in designing a compact and efficient system. This paper proposes an optimization procedure for the PCM/expanded graphite (EG)-based heat sink to minimize the temperature of the heat source. The numerical model is built to estimate the thermal response, and a surrogate model is fitted using the Kriging model. An optimization algorithm is constructed to predict the optimum parameters of the heat sink, and the effects of heat sink volume, heat flux, and working time on the optimal parameters of the heat sink are investigated. This shows that the numerical results agree well with the experimental data, and the proposed optimization method effectively obtains the optimal EG mass fraction and the geometric dimensions of the PCM enclosure. The optimal EG mass fraction increases with the rise in heat sink volume and decreases with the increase in heat flux and working time. The optimal ratio of the height to the length of the heat sink is 0.43. This study provides practical guidance for the optimal design of a PCM/EG-based heat sink. Full article

Accurate semantic segmentation in transmission corridor scenes is crucial for the maintenance and inspection of power infrastructure, facilitating the timely detection of potential hazards. In this study, we propose SA-KPConv, an advanced segmentation model specifically designed for transmission corridor scenarios. Traditional approaches, including Random Forest and point-based deep learning models such as PointNet++, demonstrate limitations in segmenting critical infrastructure components, particularly power lines and towers, primarily due to their inadequate capacity to capture complex spatial relationships and local geometric details. Our model effectively addresses these challenges by integrating a spatial attention module with kernel point convolution, enhancing both global context and local feature extraction. Experiments demonstrate that SA-KPConv outperforms state-of-the-art methods, achieving a mean Intersection over Union (mIoU) of 89.62%, particularly excelling in challenging terrains such as mountainous areas. Ablation studies further validate the significance of our model’s components in enhancing overall performance and effectively addressing class imbalance. This study presents a robust solution for semantic segmentation, with considerable potential for monitoring and maintaining power infrastructure. Full article

The airborne intelligent hydraulic system is an effective way to solve the ineffective power consumption and temperature rise of an aircraft hydraulic system. An intelligent pump that can work in a variety of modes according to the change of flight conditions is an inevitable requirement for the realization of airborne intelligent hydraulic system, and it is also the development trend of aviation pumps in the future. In this paper, key technologies for aviation intelligent pumps are reviewed. This paper briefly describes its development process and summarizes the research on aviation intelligent pumps from the aspects of the system scheme, working mode, structure form, and control strategy. Finally, the conclusions and trends of the research status of intelligent pumps are given, which can provide a reference for subsequent research on further improving the performance of aviation intelligent pumps. Full article

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of ${\mathrm{PM}}_1$ and ${\mathrm{PM}}_{2.5}$ as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam’s razor principle. For the ${\mathrm{PM}}_1$ calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the ${\mathrm{PM}}_{2.5}$ model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models. Full article

In this work, we introduce the first pipeline that combines a refraction-aware structure from motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the SfM optimization problem. This way, no refraction correction as post-processing is required. Experiments with simulated data that approach real-world capturing conditions demonstrate that SfM with refraction correction is extremely accurate, with submillimeter errors. We integrate our refraction-aware SfM within a deep learning framework that also takes into account radiometrical information, developing a combined spectral and geometry-based approach, with further improvements in accuracy and robustness to different seafloor types, both textured and textureless. We conducted experiments with real-world data at two locations in the southern Mediterranean Sea, with varying seafloor types, which demonstrate the benefits of refraction correction for the deep learning framework. We made our refraction-aware SfM open source, providing researchers in airborne bathymetry with a practical tool to apply SfM in shallow water areas. Full article

Characterizing Management Units (MUs) with tree-level data is instrumental for a comprehensive understanding of forest structure and for providing information needed to support forest management decision-making. Airborne Laser Scanning (ALS) data may enhance this characterization. While some studies rely on Individual Tree Detection (ITD) methods using ALS data to estimate tree diameters within stands, these methods often face challenges when the goal is to characterize MUs in dense forests. This study proposes a methodology that simulates diameter distributions from LiDAR data using an Area-Based Approach (ABA) to overcome these limitations. Focusing on maritime pine (Pinus pinaster Ait.) MUs within a forest intervention zone in northern Portugal, the research initially assesses the suitability of two highly flexible Probability Density Functions (PDFs), Johnson’s SB and Weibull, for simulating diameter distribution in maritime pine stands in Portugal using the PINASTER database. The selected PDF is then used in conjunction with ABA to derive the variables needed for parameter recovery, enabling the simulation of diameter distributions within each MU. Monte Carlo Simulation (MCS) is applied to generate a sample list of tree diameters from the simulated distributions. The results indicate that this methodology is appropriate to estimate diameter distributions within maritime pine MUs by using ABA combined with Johnson’s SB and Weibull PDFs. Full article

Polarization optics, which characterize the orientation of the electromagnetic field through Stokes vectors formalism, have been effectively used in lidar remote sensing to detect particles that differ in shape, such as mineral dust or pollen. In this study, for the first time, we explore the capability of polarization optics to distinguish the light-backscattering patterns of pollen and fungal spores, two complex-shaped particles that vary significantly in surface structure. A unique laboratory polarimeter operating at lidar backscattering at 180.0° was conducted to assess their light depolarization property in laboratory ambient air. If, at the precise lidar backscattering angle of 180.0°, the depolarization ratios of pollen and fungal spores were difficult to differentiate, slight deviations from 180.0° allowed us to reveal separate scattering matrices for pollen and fungal spores. This demonstrates that polarization optics can unambiguously differentiate these particles based on their light-(back)scattering properties. These findings are consistent at both 532 and 1064 nm. This non-invasive, real-time technique is valuable for environmental monitoring, where rapid identification of airborne allergens is essential, as well as in agricultural and health sectors. Polarization-based light scattering thus offers a valuable method for characterizing such atmospheric particles, aiding in managing airborne contaminants. Full article

The Dongzhongla deposit is a skarn-type lead–zinc ore deposit located in the eastern segment of the Gangdese metallogenic belt, situated in the Xizang province, China. The high-altitude mountainous terrain of the region poses significant challenges to ground-based exploration. To facilitate more accurate mineral exploration in the deposit and its surrounding area, a high-resolution airborne magnetic survey was conducted over the mining area and its periphery. The airborne magnetic data were processed using derivative and Euler deconvolution methods, yielding results that reflect the geological structural features of the study area. By integrating the geological characteristics of the ore deposit, we inferred that the areas of magnetic anomaly extensions and the peripheries of other magnetic anomalies are favorable zones for mineralization, providing positive leads for further mineral exploration. Full article

Soil piping erosion is an underground soil erosion process that is significantly underestimated or overlooked. It can lead to intense soil erosion and trigger surface processes such as landslides, collapses, and channel erosion. Conducting susceptibility mapping is a vital way to identify the potential for soil piping erosion, which is of enormous significance for soil and water conservation as well as geological disaster prevention. This study utilized airborne radar drones to survey and map 1194 sinkholes in Sunjiacha basin, Huining County, on the Loess Plateau in Northwest China. We identified seventeen key hydrogeomorphological factors that influence sinkhole susceptibility and used six machine learning models—support vector machine (SVM), logistic regression (LR), Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), random forest (RF), and gradient boosting decision tree (GBDT)—for the susceptibility assessment and mapping of loess sinkholes. We then evaluated and validated the prediction results of various models using the area under curve (AUC) of the Receiver Operating Characteristic Curve (ROC). The results showed that all six of these machine learning algorithms had an AUC of more than 0.85. The GBDT model had the best predictive accuracy (AUC = 0.94) and model migration performance (AUC = 0.93), and it could find sinkholes with high and very high susceptibility levels in loess areas. This suggests that the GBDT model is well suited for the fine-scale susceptibility mapping of sinkholes in loess regions. Full article

Livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) is widespread in European pig production and poses an occupational hazard to farm workers and their household members. Farm workers are exposed to LA-MRSA through direct contact with pigs and airborne transmission, enabling bacteria to be carried home in the nose or on the skin. Consequently, it is important to consider LA-MRSA in a one-health context, studying human exposure by examining LA-MRSA levels in pigs, air, and dust in the farm environment. In this investigation, LA-MRSA levels were quantified in nasal swab samples from the pigs, air samples, and dust at three different time points in a farm rearing pigs from approx. 30 kg until slaughter. Sampling was repeated across seven batches of pigs, resulting in the analysis of 504 nasal swab samples, alongside air and dust samples. LA-MRSA was cultured and quantified on MRSA2 agar plates. Findings revealed significant batch-to-batch variation and a significant 94.1% decrease in LA-MRSA levels during the rearing period. Despite this decline, all nasal swab samples tested positive, with the highest level reaching 353,000 cfu in a sample. Among the 42 air samples, LA-MRSA levels were low to moderate, with a maximum of 568 and an average of 63 cfu/m³. In the 28 dust samples collected during the second and third sampling periods, LA-MRSA counts were high, reaching up to 37,272 cfu/g, with an average of 17,185 cfu/g. The results suggest that while LA-MRSA levels in pigs decrease with age, reaching low levels before slaughter, the bacterium remains highly abundant in dust, posing an occupational hazard to farm workers. Full article

This study investigates the spatial and temporal distribution of the artificial radionuclides ¹²⁹I and ²³⁶U in the Western Mediterranean Sea, focusing on their connection to radionuclide sources and circulation dynamics. Taking advantage of unprecedented precision of accelerator mass spectrometry, both tracers were firstly investigated in 2013. Here, we examine tracer observations obtained along four stations (re-)visited during the TAlPro2022 expedition in May 2022. Distributions of both ¹²⁹I and ²³⁶U were related to water masses and clearly linked to local circulation patterns: a tracer-poor surface Atlantic inflow, a thining of the tracer minimum at intermediate depths, and a higher tracer signal in Western Mediterranean Deep Waters due to dense water formation in the Algero-Provençal basin. The comparison to 2013 tracer data indicated recent deep ventilation of the Tyrrhenian Sea, the mixing of deep waters and enhanced stratification in intermediate waters in the Algero-Provençal basin due to a temperature and salinity increase between 2013 and 2022. We estimate an overall ¹²⁹I increase of 20% at all depths between 0 and 500m with respect to 2013, which is not accompanied by ²³⁶U. This suggests either the lateral transport of ¹²⁹I from the Eastern Mediterranean Sea, or an additional source of this tracer. The inventories of ¹²⁹I calculated for each water mass at the four stations point to the deposition of airborne releases from the nuclear reprocessing plants (La Hague and Sellafield) on the surface Mediterranean waters as the more likely explanation for the ¹²⁹I increase. This work demonstrates the great potential of including measurements of anthropogenic radionuclides as tracers of ocean circulation. However, a refinement of the anthropogenic inputs is necessary to improve their use in understanding ventilation changes in the Mediterranean Sea. Full article

The exploration of porphyry deposits in Greenland has become increasingly important due to their significant economic potential. We utilized total magnetic intensity (TMI) and mobile magnetotelluric (MobileMT) airborne data to delineate potential porphyry mineralization zones. The TMI method was employed to map variations in the Earth’s magnetic field caused by subsurface geological features, including mineral deposits. By analyzing anomalies in TMI data, potential porphyry targets were identified based on characteristic magnetic signatures associated with mineralized zones. Complementing TMI data, MT airborne surveys provided valuable insights into the electrical conductivity structure of the subsurface. Porphyry deposits exhibited distinct conductivity signatures due to the presence of disseminated sulfide minerals, aiding in their identification and delineation. Integration of the TMI and MobileMT datasets allowed for a comprehensive assessment of porphyry exploration targets in Flammefjeld. The combined approach facilitates the identification of prospective areas with enhanced geological potential, optimizing resource allocation and exploration efforts. Overall, this study demonstrates the efficacy of integrating TMI and MobileMT airborne data for porphyry exploration in Greenland, offering valuable insights for mineral exploration and resource development in the region. Full article