Search Results (5,244)

Advanced airborne power generation technology represents one of the most effective solutions for meeting the electricity requirements of hypersonic vehicles during long-endurance flights. This paper proposes a power generation system that integrates a high-temperature fuel cell to tackle the challenges associated with power generation in the hypersonic field, utilizing techniques such as inlet pressurization, autothermal reforming, and anode recirculation. Firstly, the power generation system is modeled modularly. Secondly, the influence of key parameters on the system’s performance is analyzed. Thirdly, the performance of the power generation system under the design conditions is simulated and evaluated. Finally, the weight distribution and exergy loss of the system’s components under the design conditions are calculated. The results indicate that the system’s electrical efficiency increases with fuel utilization, decreases with rising current density and steam-to-carbon ratio (SCR), and initially increases before declining with increasing fuel cell operating temperature. Under the design conditions, the system’s power output is 48.08 kW, with an electrical efficiency of 51.77%. The total weight of the power generation system is 77.09 kg, with the fuel cell comprising 69.60% of this weight, resulting in a power density of 0.62 kW/kg. The exergy efficiency of the system is 55.86%, with the solid oxide fuel cell (SOFC) exhibiting the highest exergy loss, while the mixer demonstrates the greatest exergy efficiency. This study supports the application of high-temperature fuel cells in the hypersonic field. Full article

This paper addresses the study of synoptic-scale meteorological conditions that favor long-range pollen transport in southern South America combining airborne pollen counts, modeled three-dimensional backward trajectories, and synoptic and surface meteorological data. Alnus pollen transport trajectories indicate origins predominantly in montane forests of the Yungas between 1500 and 2800 m altitude. The South American Low-Level Jet is the main meteorological feature that explains 64% of the detected pollen arrival at the target site. Podocarpus and Nothofagus pollen instead are linked primarily to the widespread Subantartic forests in southern Patagonia. Their transport patterns are consistent with previous studies, which show an association with synoptic patterns related to cold front passages carrying pollen in the free atmosphere (27% for Nothofagus and 25% for Podocarpus). These results show the significance of understanding long-distance pollen transport for disciplines such as climate change reconstruction and agriculture, emphasizing the need for further research to refine atmospheric circulation models and refine interpretations of past vegetation and climate dynamics. Full article

Mapping forest canopy height is critical for climate modeling and forest management, and tropical forests present unique challenges for remote sensing due to their dense vegetation and complex structure. The advent of ICESat-2 and GEDI, two advanced lidar datasets, offers new opportunities for improving canopy height estimation. In this study, we used footprint-level canopy height products from ICESat-2 and GEDI, combined with features extracted from Landsat-8, PALSAR-2, and FABDEM products. The AutoGluon stacking ensemble learning algorithm was employed to construct inversion models, generating 30 m resolution continuous canopy height maps for the tropical forests of Puerto Rico. Accuracy validation was performed using the high-resolution G-LiHT airborne lidar products. Results show that tropical forest canopy height inversion remains challenging, with all models yielding relative root mean square errors (rRMSE) exceeding 0.30. The stacking ensemble model outperformed all base learners, and the GEDI-based map had slightly higher accuracy than the ICESat-2-based map, with RMSE values of 4.81 and 4.99 m, respectively. Both models showed systematic biases, but the GEDI-based model exhibited less underestimation for taller canopies, making it more suitable for biomass estimation. The proposed approach can be applied to other forest ecosystems, enabling fine-resolution canopy height mapping and enhancing forest conservation efforts. Full article

Estimating forest growing stock volume (GSV) is crucial for forest growth and resource management, as it reflects forest productivity. National measurements are laborious and costly; however, integrating satellite data such as optical, Synthetic Aperture Radar (SAR), and airborne laser scanning (ALS) with National Forest Inventory (NFI) data and machine learning (ML) methods has transformed forest management. In this study, random forest (RF), support vector regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point cloud data. Four variable combinations were tested: CO1 (vegetation indices and LiDAR), CO2 (vegetation indices and individual band reflectance), CO3 (LiDAR and individual band reflectance), and CO4 (a combination of vegetation indices, individual band reflectance, and LiDAR). Across Estonia’s geographical regions, RF consistently delivered the best performance. In the northwest (NW), the RF model achieved the best performance with the CO3 combination, having an R² of 0.63 and an RMSE of 125.39 m³/plot. In the southwest (SW), the RF model also performed exceptionally well, achieving an R² of 0.73 and an RMSE of 128.86 m³/plot with the CO4 variable combination. In the northeast (NE), the RF model outperformed other ML models, achieving an R² of 0.64 and an RMSE of 133.77 m³/plot under the CO4 combination. Finally, in the southeast (SE) region, the best performance was achieved with the CO4 combination, yielding an R² of 0.70 and an RMSE of 21,120.72 m³/plot. These results underscore RF’s precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy. Full article

The Italian sporting event ‘XIV Convittiadi’ involving students at boarding schools took place in Molise region, central Italy, in April 2022. The study describes the public health protocol with specific countermeasures developed for the event, including testing, isolation, and contact tracing during the COVID-19 pandemic, and reports the main related findings. There were 590 Italian white participants, with 514 athletes (mean age 13.6 ± 1.6 years) and 76 accompanying teachers/guardians (50 ± 8.7 years) of 21 boarding schools from different Italian regions. During the event, 1281 antigenic swabs were performed, and twelve COVID-19 cases were promptly identified due to active screening, involving ten (83.3%) athletes and two (16.7%) accompanying teachers. Among the infected athletes, 83.3% complained mild symptoms, either before or after the ascertained positivity, and 40% had received primary cycle vaccination and booster, or only completed the primary course. The enhanced surveillance and contact tracing activities allowed identifying 34 participants as close contacts who were subjected to a daily follow-up that revealed only four (11.8%) as infected. Since in mass gathering events public health risk is not clear and could not be available through the traditional surveillance systems, increased monitoring activities are necessary. The practical approach implemented for this event was valuable for SARS-CoV-2 control and case management either among participants, or the host country population, suggesting its application to other airborne communicable diseases. Full article

Air pollution is recognized by the World Health Organization as the major environmental threat; therefore, air quality is constantly being monitored by monitoring stations. However, the most common atmospheric pollutants being monitored do not include pollen. Among the reasons for the lack of pollen control is that there are different types and sizes of pollen. The largest particles commonly being monitored by air monitoring stations have a maximum aerodynamic diameter of 10 microns, while the aerodynamic diameter of most pollen grains is known to range from 10 to 100 microns. For this reason, most pollen is not being detected by air monitoring stations. For the patents found in a literature review, monitoring pollen concentration in the air requires the discrimination of pollen grains from particulate matter of a similar size, as well as the identification of the type of pollen grains detected, since different pollen types may produce different effects, such as allergic reactions, asthma, and lung cancer, in exposed people. In this work, 15 patent documents regarding pollen monitoring were identified and reviewed using three search engines: Google Patents, WIPO’s PatentScope, and the United States Patent and Trademark Office (USPTO) database. The extracted data from the patents included whether they differentiate pollen type, pollen size, and sensor type and whether they provide real-time data. The results show that 93.33% of the patents identify pollen type, while 80% of the patents identify pollen size. Most of the patents use light-scattering and image sensors and use image processing techniques to analyze particles. Furthermore, 40% of the patents were found to implement artificial intelligence. Further, it was found that only nine patents provide real-time data, which is an important feature of an air monitoring system. Full article

The Cerro Prieto basin, a tectonically active pull-apart basin, hosts significant geothermal resources currently being exploited in the Cerro Prieto Geothermal Field (CPGF). Consequently, natural tectonic processes and anthropogenic activities contribute to three-dimensional surface displacements in this pull-apart basin. Here, we obtained the Cerro Prieto Step-Over 3D surface velocity field (3DSVF) by accomplishing a weighted least square algorithm inversion from geometrically quasi-orthogonal airborne UAVSAR and RADARSAT-2, Sentinel 1A satellite Synthetic Aperture-Radar (SAR) imagery collected from 2012 to 2016. The 3DSVF results show a vertical rate of 150 mm/yr and 40 mm/yr for the horizontal rate, where for the first time, the north component displacement is achieved by using only the Interferometric SAR time series in the CPGF. Data integration and validation between the 3DSVF and ground-based measurements such as continuous GPS time series and precise leveling data were achieved. Correlating the findings with recent geothermal energy production revealed a subsidence rate slowdown that aligns with the CPGF’s annual vapor production. Full article

Wake vortices generated by aircraft during near-ground operations have a significant impact on airport safety during takeoffs and landings. Identifying wake vortices in complex airspaces assists air traffic controllers in making informed decisions, ensuring the safety of aircraft operations at airports, and enhancing the intelligence level of air traffic control. Unlike traditional image recognition, identifying wake vortices using airborne LiDAR data demands a higher level of accuracy. This study proposes the IRSN-WAKE network by optimizing the Inception-ResNet-v2 network. To improve the model’s feature representation capability, we introduce the SE module into the Inception-ResNet-v2 network, which adaptively weights feature channels to enhance the network’s focus on key features. Additionally, we design and incorporate a noise suppression module to mitigate noise and enhance the robustness of feature extraction. Ablation experiments demonstrate that the introduction of the noise suppression module and the SE module significantly improves the performance of the IRSN-WAKE network in wake vortex identification tasks, achieving an accuracy rate of 98.60%. Comparative experimental results indicate that the IRSN-WAKE network has higher recognition accuracy and robustness compared to common recognition networks, achieving high-accuracy aircraft wake vortex identification and providing technical support for the safe operation of flights. Full article

The abuse of UAVs poses a potential risk to social security, necessitating the investigation of anti-UAV methods to safeguard critical areas. However, the existing UAV countermeasures face challenges such as high environmental impact, restricted spatial deployment, and low cost-effectiveness. To address these limitations, we developed a novel anti-UAV system known as UAV Hunter, which adopts an airborne tether-net capture device with visual aids to counter unauthorized UAVs. This system employs an “Anti-UAV with UAV” scheme, comprising a ground control station and a net-capturing UAV. The operator utilizes the ground control station to determine the mission area and flight path and then controls the flight of the net-capturing UAV. During flight, the net-capturing UAV leverages its dual-mode sensor to continuously monitor the target area. Simultaneously, the onboard computer executes a UAV detection and tracking algorithm to search for unauthorized UAVs in real time. The results are relayed to the operator in real time, facilitating precise adjustments for the net-capturing UAV to launch the rope net accurately. The system successfully realizes the functions of dual-mode real-time detection and tracking, precise net capture, and efficient integrated control. Compared with existing methods, the developed system exhibits accurate recognition, rapid action, diverse application scenarios, and an enhanced human–machine interaction experience. Test results in the open environment further validate the feasibility and functional integrity of the system, demonstrating its capability to effectively capture low-altitude unauthorized UAVs. Full article

Coffee production worldwide is affected by the pathogen Hemileia vastatrix, which causes the “coffee rust” disease and may be associated with other fungi. Ecuador lacks studies on fungal diversity associated with coffee rust, which could potentially control or escalate pathogen activity. Using the ITS-5.8S nrDNA region, we randomly detected a small preliminary fungi diversity related to coffee rust in Ecuador, which we report here for the first time. Ten coffee farms (four in Loja, three in Calvas, and three in Quilanga) from the Loja Province were sampled to analyze the genetic diversity of the pathogen Hemileia vastatrix in rust lesions on coffee leaves. A high number of selected sequences (Sanger sequencing) showed the presence of 48 OTUs (Operational Taxonomic Units) or “hypothetical species” of Ascomycetes and Basidiomycetes distributed across all the sampled farms. The genera Akanthomyces, Ceramothyrium, Cladosporium, Didymella, Fusarium, Mycosphaerella, Neoceratosperma, and Trichothecium of Ascomycetes, as well as Bulleribasidium, Hannaella, and Meira of Basidiomycetes, were the most abundant. To avoid taxonomic conflict, some sequences were placed into Capnodiales (Ascomycetes) and Tremelalles (Basidiomycetes) without a genus definition. A new phylogenetic group of sequences is considered Incertae Sedis from Basidiomycetes. Additionally, morphospecies of Akanthomyces (synonymous with some Lecanicillium species) and Colletotrichum were observed macroscopically and microscopically growing closely with rust. Most of the OTUs probably correspond to rust mycoparasites, as previously reported in the literature. However, this study is limited by the number of sequences analyzed phylogenetically, which may hinder the discovery of significant insights. Future studies are needed to determine whether this preliminary fungal diversity is associated with the rust fungus or corresponds to ubiquitous airborne fungi. Furthermore, research into the function of these species may reveal whether they promote rust pathogenicity or enhance plant responses by activating resistance mechanisms. Full article

Unmanned aerial vehicles (UAVs) can be utilized as airborne base stations to deliver wireless communication and federated learning (FL) training services for ground vehicles. However, most existing studies assume that vehicles (clients) and UAVs (model owners) offer services voluntarily. In reality, participants (FL clients and model owners) are selfish and will not engage in training without compensation. Meanwhile, due to the heterogeneity of participants and the presence of free-riders and Byzantine behaviors, the quality of vehicles’ model updates can vary significantly. To incentivize participants to engage in model training and ensure reliable outcomes, this paper designs a reliable incentive mechanism (FedBeam) based on game theory. Specifically, we model the cooperation problem between model owners and clients as a two-layer Stackelberg game and prove the existence and uniqueness of the Stackelberg equilibrium (SE). For the cooperation among model owners, we formulate the problem as a coalition game and based on this, analyze and design a coalition formation algorithm to derive the Pareto optimal social utility. Additionally, to achieve reliable FL model updates, we design a weighted-beta (Wbeta) reputation update mechanism to incentivize FL clients to provide high-quality model updates. The experimental results show that compared to the baselines, the proposed incentive mechanism improves social welfare by 17.6% and test accuracy by 5.5% on simulated and real datasets, respectively. Full article

Airborne dust easily accumulates on the top of solar panel surfaces and reduces the output power in arid regions. A commonly used mitigation solution for dust deposition issues is cleaning PV panels periodically. However, cleaning frequency affects the economic viability of solar PV systems, resulting in a trade-off between cleaning costs and energy loss costs. To address this issue, this study relates several metrics and develops a generic framework based on simulation and optimization to determine the optimal cleaning interval. Based on the computational tests, the optimal cleaning interval in Abu Dhabi is determined to be 34 days, which is longer than the currently recommended cleaning interval of 28 days. This study also identifies that energy recovery is responsive to decreases in unit cleaning costs in the presence of high electricity tariffs, whereas total cost savings show sensitivity when electricity tariffs are low. Finally, this study discusses energy policy implications by presenting an innovative concept involving the introduction of a cleaning subsidy which could reshape energy system cost dynamics, making PV systems economically competitive beyond the conventional levelized cost of electricity. Full article

Plasmopara viticola (Berk.et Curtis) Berl. Et de Toni is the pathogen that causes grape downy mildew, which is an airborne disease that severely affects grape yield and causes huge economic losses. The usage of effective control methods can reduce the damage to plants induced by grape downy mildew. Biocontrol has been widely used to control plant diseases due to its advantages of environmental friendliness and sustainability. However, to date, only a few comprehensive reviews on the biocontrol of grape downy mildew have been reported. In this review, we summarize the biological characteristics of P. viticola and its infection cycle, followed by a detailed overview of current biocontrol agents, including bacteria and fungi that could be used to control grape downy mildew, and their control effects. Furthermore, potential control mechanisms of biocontrol agents against grape downy mildew are discussed. Lastly, suggestions for future research on the biocontrol of grape downy mildew are provided. This review provides the basis for the application of grape downy mildew biocontrol. Full article

Infections in confined environments can spread by direct contact, contaminated surfaces, and airborne transmission. This is critical in prison facilities, where cleaning and sanitary conditions are inadequate. An alternative is the development of antimicrobial surfaces. A new antimicrobial coating was developed by incorporating copper microparticles into a standard commercial paint, aiming to reduce the concentration of bacteria on surfaces by granting antimicrobial properties to surfaces. The copper additive comprised Cu₂Cl(OH)₃ deposited on polyhedral zeolite. The efficacy of this coating was evaluated in detention cells in a police station, which are temporary prisons and inherently dirty environments. The experiment compared a cell painted with the copper additive coating and a control cell with the standard paint. Viable coliforms were measured on different surfaces and in the air for five months under normal usage. Bacterial load was reduced by ca. 68% by the copper-amended paint on cement surfaces. Surprisingly, airborne viable coliforms were reduced by ca. 87% in the detention cell treated with the copper coating. This research highlights the potential of antimicrobial coatings in controlling the spread of infections through contact with contaminated surfaces and emphasizes the significant reduction in airborne bacterial load. It is especially relevant for controlling infections where sanitization is limited but can be extended to other built environments, such as healthcare facilities. 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