Search Results (179)

Dry bean, the fourth-largest pulse crop in Canada is increasingly impacted by climate variability, needing efficient methods to support cultivar development. This study investigates the potential of unmanned aerial vehicle (UAV)-based Light Detection and Ranging (LiDAR) and multispectral imaging (MSI) for high-throughput phenotyping of dry bean traits. Image data were collected across two dry bean field trials to assess plant height, lodging and seed yield. Multiple LiDAR-derived features accessing canopy height, crop lodging and digital biomass were evaluated against manual height measurements, visually rated lodging scale and seed yield, respectively. At the same time, three MSI-derived data were used to estimate seed yield. Classification- and regression-based machine learning models were used to estimate key agronomic traits using both LiDAR and MSI-based crop features. The canopy height derived from LiDAR showed a good correlation (R² = 0.86) with measured plant height at the mid-pod filling (R6) stage. Lodging classification was most effective using Gradient Boosting, Random Forest and Logistic Regression, with R8 (physiological maturity stage) canopy height being the dominant predictor. For seed yield prediction, models integrating LiDAR and MSI outperformed individual datasets, with Gradient Boosting Regression Trees yielding the highest accuracy (R² = 0.64, RMSE = 687.2 kg/ha and MAE = 521.6 kg/ha). Normalized Difference Vegetation Index (NDVI) at the R6 stage was identified as the most informative spectral feature. Overall, this study demonstrates the importance of integrating UAV-based LiDAR and MSI for accurate, non-destructive phenotyping in dry bean breeding programs. Full article

ASTER GDEM provides the fundamental data for remote sensing identification of snow cover in mountainous areas. Due to its elevation accuracy being easily affected by optical stereo images and local terrain, many studies have utilized machine learning (ML) models for correction. However, most correction methods rely on a single ML model, which limits the improvement of DEM accuracy. Stacked ensemble learning (SEL) is a newly developed method of improving model performance by combining multiple ML models. This study proposes a DEM correction method based on SEL and ICESatand affiliations. -2/ATL08 products. Taking the Babao River Basin in Qilian Mountains as the study area, five ML models with good DEM correction effects (XGBoost, AdaBoost, LightGBM, BPNN, and CatBoost) were selected and trained using land cover and various terrain factors to obtain DEM errors, respectively. Then, the SEL algorithm was used to integrate the DEM errors of the five ML models and correct GDEM. Using 740 CORS measurements and 48,000 ATL08 points for accuracy validation, the results showed that the SEL achieved higher DEM accuracy than any single ML model. The root mean square error (RMSE) of the corrected GDEM decreased from 7.15 m to 4.13 m, while the mean absolute error (MAE) and mean bias error (MBE) values both decreased about by 38%. Furthermore, unmanned aerial vehicle (UAV) DEM data from five sample areas were selected for profile analysis, and it was found that the corrected GDEM was closer to the real surface. Further analysis revealed that the influence of slope, aspect, and land cover types on corrected DEM was weakened, with the most significant improvement in DEM accuracy observed in areas with slope ≥5°, north orientation, and bare land. This study can provide high-precision DEM scientific data for quantitative remote sensing, flood prediction, and other research. Full article

Background: Methicillin-resistant Staphylococcus aureus (MRSA) considered under the category of serious threats by the Centers for Disease Control and Prevention (CDC), urges for new antibiotics or alternate strategies to control MRSA. Methods: Ethosome-like liposomes have been developed and characterized using dynamic light scattering (DLS), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Liposomes were confirmed for antibiotics infusion by encapsulation efficiency and release kinetics as well. Further, the antimicrobial potential of liposomes was checked by determination of minimum inhibitory concentrations (MICs), crystal violet assay, and live/dead biofilm eradication assay. Results: The specially designed liposomes consist of amphiphilic molecules, tocopherol, conjugated with ampicillin and, another antibiotic amikacin, loaded in the core. The developed liposomes exhibited good encapsulation efficiency, and sustained release while serving as ideal antibiotic carriers for advanced efficacy along with anti-inflammatory benefits from tocopherol. Conclusively, newly designed liposomes displayed potential antimicrobial activity against MRSA and its complex biofilms. Conclusions: Overall, dual antibiotic-encapsulated liposomes demonstrate the potential to eradicate MRSA and its mature biofilms by dual-targeted action. This could be developed as an efficient anti-infective agent and delivery vehicle for conventional antibiotics to combat MRSA. Full article

The hydrogen fuel quality is critical to the efficiency and longevity of fuel cell electric vehicles (FCEVs), with ISO 14687:2019 grade D establishing stringent impurity limits. This study compared two different sampling techniques for assessing the hydrogen fuel quality, focusing on the National Physical Laboratory hydrogen direct sampling apparatus (NPL DirSAM) from a 35 MPa heavy-duty (HD) dispenser and qualitizer sampling from a 70 MPa light-duty (LD) nozzle, both of which were deployed on the same day at a local hydrogen refuelling station (HRS). The collected samples were analysed as per the ISO 14687:2019 contaminants using the NPL H₂-quality laboratory. The NPL DirSAM was able to sample an HD HRS, demonstrating the ability to realise such sampling on an HD nozzle. The comparison of the LD (H₂ Qualitizer sampling) and HD (NPL DirSAM) devices showed good agreement but significant variation, especially for sulphur compounds, non-methane hydrocarbons and carbon dioxide. These variations may be related to the HRS difference between the LD and HD devices (e.g., flow path, refuelling conditions and precooling for light duty versus no precooling for heavy duty). Further study of HD and LD H₂ fuel at HRSs is needed for a better understanding. Full article

Tree crown detection of high-resolution UAV forest remote sensing images using computer technology has been widely performed in the last ten years. In forest resource inventory management based on remote sensing data, crown detection is the most important and essential part. Deep learning technology has achieved good results in tree crown segmentation and species classification, but relying on high-performance computing platforms, edge calculation, and real-time processing cannot be realized. In this thesis, the UAV images of coniferous Pinus tabuliformis and broad-leaved Salix matsudana collected by Jingyue Ecological Forest Farm in Changping District, Beijing, are used as datasets, and a lightweight neural network U-Net-Light based on U-Net and VGG16 is designed and trained. At the same time, the IP core and SoC architecture of the neural network accelerator are designed and implemented on the Xilinx ZYNQ 7100 SoC platform. The results show that U-Net-light only uses 1.56 MB parameters to classify and segment the crown images of double tree species, and the accuracy rate reaches 85%. The designed SoC architecture and accelerator IP core achieved 31 times the speedup of the ZYNQ hard core, and 1.3 times the speedup compared with the high-end CPU (Intel CoreTM i9-10900K). The hardware resource overhead is less than 20% of the total deployment platform, and the total on-chip power consumption is 2.127 W. Shorter prediction time and higher energy consumption ratio prove the effectiveness and rationality of architecture design and IP development. This work departs from conventional canopy segmentation methods that rely heavily on ground-based high-performance computing. Instead, it proposes a lightweight neural network model deployed on FPGA for real-time inference on unmanned aerial vehicles (UAVs), thereby significantly lowering both latency and system resource consumption. The proposed approach demonstrates a certain degree of innovation and provides meaningful references for the automation and intelligent development of forest resource monitoring and precision agriculture. Full article

Material selection is a crucial aspect of product design, often determining the success or failure of a product in the market. It involves an exploration of the main criteria for the application of the product, according to the properties required by the component to be designed. The present study aims to evaluate the material selection of a brake disc in light SUV-type vehicles. The material selection is based on multi-criteria decision making (MCDM) methods. Five different MCDM methods were used to select the best material alternatives and the ENTROPY method was used for weighting the criteria. In addition, a simulation is carried out to validate the results of the MCDM analysis and to show that the selected material can be used due to its strength and temperature conditions. Due to its low density, high yield strength, and good compressive strength, the best alternative is ASTM A536 material for three MCDM methods and the second option is ASTM A48 according to two methods. Full article

Asphalt pavements are subjected to both repeated vehicle loads and erosive deterioration from complicated environments in service. Salt erosion exerts a serious negative impact on the service performance of asphalt pavements in salt-rich areas such as seasonal frozen areas with snow melting and deicing, coastal areas, and saline soils areas. In recent years, the performance evolution of asphalt materials under salt erosion environments has been widely investigated. However, there is a lack of a systematic summary of salt erosion damage for asphalt materials from a multi-scale perspective. The objective in this paper is to review the performance evolution and the damage mechanism of asphalt mixtures and binders under salt erosion environments from a multi-scale perspective. The salt erosion damage and damage mechanism of asphalt mixtures is discussed. The influence of salt categories and erosion modes on the asphalt binder is classified. The salt erosion resistance of different asphalt binders is determined. In addition, the application of microscopic test methods to investigate the salt damage mechanism of asphalt binders is generalized. This review finds that the pavement performance of asphalt mixtures decreased significantly after salt erosion. A good explanation for the salt erosion mechanism of asphalt mixtures can be provided from the perspective of pores, interface adhesion, and asphalt mortar. Salt categories and erosion modes exerted great influences on the rheological performance of asphalt binders. The performance of different asphalt binders showed a remarkable diversity under salt erosion environments. In addition, the evolution of the chemical composition and microscopic morphology of asphalt binders under salt erosion environments can be well characterized by Fourier Infrared Spectroscopy (FTIR), Gel Permeation Chromatography (GPC), and microscopic tests. Finally, the major focus of future research and the challenges that may be encountered are discussed. From this literature review, pore expansion mechanisms differ fundamentally between conventional and salt storage asphalt mixtures. Sulfate ions exhibit stronger erosive effects than chlorides due to their chemical reactivity with asphalt components. Molecular-scale analyses confirm that salt solutions accelerate asphalt aging through light-component depletion and heavy-component accumulation. These collective findings from prior studies establish critical theoretical foundations for designing durable pavements in saline environments. Full article

In this work, an experiment was designed in which a defined route consisting of country roads, highways, and urban roads was driven by 20 subjects during the day and at night. The test vehicle was equipped with GPS and a camera, and the subject wore head-mounted eye-tracking glasses to record gaze. Gaze distributions for country roads, highways, urban roads, and specific urban roads were then calculated and compared. The day/night comparisons showed that the horizontal fixation distribution of the subjects was wider during the day than at night over the whole test distance. When the distributions were divided into urban roads, country roads, and motorways, the difference was also seen in each road environment. For the vertical distribution, no clear differences between day and night can be seen for country roads or urban roads. In the case of the highway, the vertical dispersion is significantly lower, so the gaze is more focused. On highways and urban roads there is a tendency for the gaze to be lowered. The differentiation between a residential road and a main road in the city made it clear that gaze behavior differs significantly depending on the urban area. For example, the residential road led to a broader gaze behavior, as the sides of the street were scanned much more often in order to detect potential hazards lurking between parked cars at an early stage. This paper highlights the contradictory results of eye-tracking research and shows that it is not advisable to define a holy grail of gaze distribution for all environments. Gaze is highly situational and context-dependent, and generalized gaze distributions should not be used to design lighting functions. The research highlights the importance of an adaptive light distribution that adapts to the traffic situation and the environment, always providing good visibility for the driver and allowing a natural gaze behavior. Full article

The optimization of passenger car efficiency is an important contribution to GHG emissions mitigation. This global warming concern is pushing technological solutions to reduce vehicle fuel consumption and consequently CO₂ emissions. In this work, the impacts of engine lubricants with lower viscosity and friction modifier additive in a light-vehicle with a spark ignition engine were numerically simulated and experimentally validated. The substitution of a baseline 5W40 lubricant by a lower viscosity 5W20 proposal resulted in 2.9% lower fuel consumption in a combined cycle. This fuel consumption improvement is enhanced to 6.1% with a 0W16 lubricant with friction modifier. A 1D simulation model based on lubricant temperature and viscosity impact on engine friction was developed and presented good experimental correlation in combined cycle for 5W20, showing a 7% lower fuel consumption advantage than the experimental results. The numerical simulation advantage was 38% lower than experimental results for 0W16 that contains friction modifier, as the additive impact was not considered in this mathematical model. Full article

To address the current issues with diesel vehicle exhaust after-treatment system particulate sensors—such as low accuracy and inability to perform continuous measurements of particulate mass concentration—a new sensor based on the light scattering method is proposed. During the research, it was found that the light scattering method can be affected by soot particles in the exhaust, which contaminate the optical components and reduces measurement accuracy. To solve this issue, a structure with alumina ceramic embedded lenses and optical fibers was designed, effectively improving the sensor’s resistance to contamination. The detection device is based on the principle of light scattering, and a particulate concentration measurement system with a 90° scattering angle was built. Calibration experiments were conducted using the dust particles generated by the device. The experimental results show that this sensor can measure particulate concentrations accurately, in real time, and with good stability, achieving a calibration error of less than ±5%. Full article

Noise coming from road traffic represents a major contributor to the high levels of noise to which people are continuously exposed—especially in urban areas—throughout all of Europe. Since it represents a very detrimental pollutant, the assessment of such noise is an important procedure. Noise levels can be measured or simulated, and, in this second case, for the building of a valid model, a proper collection of input data cannot be left out of consideration. In this paper, the authors present the development of a methodology for the collection of the main inputs for a road traffic noise model, i.e., vehicle number, category, and speed, from a video recording of traffic on an Italian highway. Starting from a counting and recognition tool already available in the literature, a self-written Python routine based on image inference has been developed for the instantaneous detection of the position and speed of vehicles, together with the categorization of vehicles (light or heavy). The obtained data are coupled with the CNOSSOS-EU model to estimate the noise power level of a single vehicle and, ultimately, the noise impact of traffic on the selected road. The results indicate good performance from the proposed model, with a mean error of −1.0 dBA and a mean absolute error (MAE) of 3.6 dBA. Full article

In recent years, the field of deep learning and computer vision has increasingly focused on the problem of vehicle target detection, becoming the forefront of many technological innovations. YOLOv8, as an efficient vehicle target detection model, has achieved good results in many scenarios. However, when faced with complex traffic scenarios, such as occluded targets, small target detection, changes in lighting, and variable weather conditions, YOLOv8 still has insufficient detection accuracy and robustness. To address these issues, this paper delves into the optimization strategies of YOLOv8 in the field of vehicle target detection, focusing on the EMA module in the backbone part and replacing the original SPPF module with focal modulation technology, all of which effectively improved the model’s performance. At the same time, modifications to the head part were approached with caution to avoid unnecessary interference with the original design. The experiment used the UA-DETRAC dataset, which contains a variety of traffic scenarios, a rich variety of vehicle types, and complex dynamic environments, making it suitable for evaluating and validating the performance of traffic monitoring systems. The 5-fold cross-validation method was used to ensure the reliability and comprehensiveness of the evaluation results. The final results showed that the improved model’s precision rate increased from 0.859 to 0.961, the recall rate from 0.83 to 0.908, and the mAP50 from 0.881 to 0.962. Meanwhile, the optimized YOLOv8 model demonstrated strong robustness in terms of detection accuracy and the ability to adapt to complex environments. Full article

Individual road mobility comes with two major challenges: greenhouse gas emissions related to global warming and chemical pollution. For the pollution reduction in the spark ignition engine vehicle, the standard and reliable aftertreatment technology is the three-way catalytic converter (TWC). However, the TWC starts to convert once an optimal temperature, usually known as the light-off temperature, is reached. There are many methods to reduce the warm-up period of the TWC, among which is using a burner. The initial question underlying this study was to see if the use of a relatively straightforward extra-combustion device mounted upstream the TWC, without complex elements, was able to serve the purpose of reducing the light-off time. Consequently, an original burner was designed and investigated numerically via the CFD method and experimentally via measurements of the temperature evolution within a TWC, along with the emissions specific to the burner’s operation. The main findings of this study are: (1) the CFD-based examination is a good way to decide on how to achieve the so-called fit-for-purpose internal aerodynamics of the burner (i.e., to obtain a homogeneous mixture) and (2) to reach the light-off temperature, conventionally taken as 500 K, the burner was operated for 5.2 s, i.e., 3.6 g of gasoline injected, 2.7 g of CO₂ and 1.351 g of CO, respectively, emitted. Moreover, this study identified measures for improving the burner’s design as well as an enhanced procedure for the burner’s operating control both aiming to produce a cleaner combustion during the TWC pre-heating. Full article

With the advancing development of Advanced Air Mobility (AAM), there is a collaborative effort to increase safety in the airspace. AAM is an advancing field of aviation that aims to contribute to the safe transportation of goods and people using aerial vehicles. When aerial vehicles are operating in high-density locations such as urban areas, it can become crucial to incorporate collision avoidance systems. Currently, there are available pilot advisory systems such as Traffic Collision and Avoidance Systems (TCAS) providing assistance to manned aircraft, although there are currently no collision avoidance systems for autonomous flights. Standards Organizations such as the Institute of Electrical and Electronics Engineers (IEEE), Radio Technical Commission for Aeronautics (RTCA), and General Aviation Manufacturers Association (GAMA) are working to develop cooperative autonomous flights using UAS-to-UAS Communication in structured and unstructured airspaces. This paper presents a new approach for collision avoidance strategies within structured airspace known as “digital traffic lights”. The digital traffic lights are deployed over an area of land, controlling all UAVs that enter a potential collision zone and providing specific directions to mitigate a collision in the airspace. This strategy is proven through the results demonstrated through simulation in a Cesium Environment. With the deployment of the system, collision avoidance can be achieved for autonomous flights in all airspaces. Full article

Uncooled and shutterless microbolometer cameras are good candidates for infrared imaging systems installed on small satellites or small unmanned aerial vehicles: they are light and passive since no cooling system or mechanical shutter is required and they can be operated at ambient temperatures. However, the radiometric compensation has to be carefully performed to make the system compatible with applications where the radiometric accuracy of the images is mandatory. In this paper, we study the impact of the camera environment to the radiometric accuracy of the images. We propose and test hardware and software solutions to improve this accuracy and the quality of the radiometric images. We show that the radiometric calibration of the camera with our model is valid over a long time period— about 3 years—using in-door experiments. Full article