Search Results (510)

Drosophila suzukii is an invasive pest of many fruit crops worldwide. Employing the Sterile Insect Technique (SIT) could mitigate D. suzukii population growth and crop damage. This study evaluated the efficacy of SIT on commercial fruit, by (1) validating the quality of irradiated sterile males (male mating competitiveness, courtship, and flight performance) in the laboratory, and (2) assessing population suppression and fruit damage reduction in commercial raspberry fields. Treatment with SIT was compared to the grower’s standard chemical insecticide program throughout the season. The principal metrics of efficacy were trap counts of wild adult female D. suzukii in crops and larvae per fruit during harvesting. These metrics together with monitoring of border areas allowed targeting of high-pressure areas with higher releases of sterile males, to maximise efficacy for a given release number. The sterile male D. suzukii were as competitive as their fertile non-irradiated counterparts in laboratory mating competitiveness and flight performance studies while fertility egg-to-pupae recovery was reduced by 99%. In commercial raspberry crops, season-long releases of sterile males significantly suppressed the wild D. suzukii population, compared to the grower standard control strategy; with up to 89% reduction in wild female D. suzukii and 80% decrease in numbers of larvae per harvested fruit. Additionally, relative fruit waste (i.e., percentage of harvested fruits rejected for sale) at harvest was reduced for early, mid and late harvest crops, by up to 58% compared to the grower standard control. SIT has the potential to provide an effective and sustainable strategy for managing D. suzukii in raspberries, increasing marketable yield by reducing adult populations, fruit damage and waste fruit. SIT could therefore serve as a valuable tool for integrated pest management practices in berry production systems. Full article

The Ministry of Land, Infrastructure, and Transport conducted a demonstration project targeting pilot areas to commercialize drone delivery services in urban areas and to present a standard model. In this study, flight data on drone delivery routes in Ulju and drone hovering in Yeosu were collected and analyzed for flight safety. Since there are no domestic or international regulations on the stability of drone flight, we were given the task of analyzing whether drone path flight should be maintained within a 10 m error range from the planned path line by the Korea Transportation Safety Authority and whether hovering works while satisfying the left and right radius errors and altitude errors within 3 m. Accordingly, the drone flight path data analyzed in Ulju met the criteria of up to 1.07%, and the hovering data analyzed in Yeosu met the criteria of less than 3% for the entire section data. Therefore, the drone flight stability evaluation analyzed in this paper is considered to have been passed. Based on the results of this study, is the data are expected to serve as a cornerstone for establishing guidelines for drone delivery flight data analysis regulations. Full article

Real-time positioning is a technological field with a multitude of applications, which expand across many scopes: from positioning within a large area to localization within smaller spaces; from locating people to locating equipment; from large-scale industrial or military applications to commercially available solutions. There are at least as many implementations of real-time positioning as there are applications and challenges. Within the domain of Radio Frequency (RF) systems, positioning has been approached from multiple angles. Some of the more common solutions involve using Time of Flight (ToF) and time difference of arrival (TDoA) technologies. Within TDoA-based systems, one common limitation stems from the computational power necessary to run the multi-lateration algorithms at a high enough speed to provide high-frequency refresh rates on the tag positions. The system presented in this study implements a complete hardware and software TDoA-based real-time positioning system, using wireless Ultra-Wideband (UWB) technology. This system demonstrates improvements in the state of the art by addressing the above limitations through the use of a hybrid Machine Learning solution combined with algorithmic fine tuning in order to reduce computational power while achieving the desired positioning accuracy. This study presents the design, implementation, verification and validation of the aforementioned system, as well as an overview of similar solutions. Full article

This paper presents a novel design for precise vertical landing of drones based on the detection of three phase shifts in the range of ±180°. The design has three inputs to which the signal transmitted from an oscillator located at the landing point arrives with different delays. The circuit increases the aerial tracking volume relative to that achieved by detectors with theoretical unambiguous detection ranges of ±90°. The phase shift measurement circuit uses an analog phase detector (mixer), detecting a maximum range of ±90°and a double multiplication of the input signals, in phase and phase-shifted, without the need to fulfill the quadrature condition. The calibration procedure, phase detector curve modeling, and calculation of the input signal phase shift are significantly simplified by the use of an automatic gain control on each branch, dwhich keeps input amplitudes to the analog phase detectors constant. A simple program to determine phase shifts and guidance instructions is proposed, which could be integrated into the same flight control platform, thus avoiding the need to add additional processing components. A prototype has been manufactured in C band to explain the details of the procedure design. The circuit uses commercial circuits and microstrip technology, avoiding the crossing of lines by means of switches, which allows the design topology to be extrapolated to much higher frequencies. Calibration and measurements at 5.3 GHz show a dynamic range greater than 50 dB and a non-ambiguous detection range of ±180°. These specifications would allow one to track the drone during the landing maneuver in an inverted cone formed by a surface with an 11 m radius at 10 m high and the landing point, when 4 cm between RF inputs is considered. The errors of the phase shifts used in the landing maneuver are less than ±3°, which translates into 1.7% losses over the detector theoretical range in the worst case. The circuit has a frequency bandwidth of 4.8 GHz to 5.6 GHz, considering a 3 dB variation in the input power when the AGC is limiting the output signal to 0 dBm at the circuit reference point of each branch. In addition, the evolution of phases in the landing maneuver is shown by means of a small simulation program in which the drone trajectory is inside and outside the tracking range of ±180°. Full article

Modern fly-by-wire (FBW) aircraft demand high-fidelity simulation systems for research and training, yet existing force-sensing solutions are often prohibitively expensive. This study presents the design, development, and validation of a low-cost, reconfigurable force-sensing sidestick. The system utilizes four strain-gauge load cells to capture pure pilot force inputs, integrated with a 6-DoF non-linear flight model. To evaluate its performance, a pitch-angle tracking task was conducted with 16 participants (pilots and non-pilots). Objective metrics revealed that the control strategy was a primary determinant of performance. Participants employing a proactive feedforward control strategy exhibited roughly an order of magnitude lower tracking-error variance than those relying on reactive corrections. Subjective assessments using the Cooper-Harper scale and NASA-TLX corroborated the objective data, confirming the sidestick’s ability to differentiate control techniques. This work demonstrates an open-source platform that makes high-fidelity FBW simulation accessible for academic research, pilot training, and human factors analysis at a fraction of the cost of commercial systems. Full article

Water status is a key determinant of physiological performance and vineyard productivity. However, its assessment through field measurements is time-consuming and labour-intensive. Remote sensing offers a fast and reliable alternative to traditional in situ methods for the monitoring of the water status in vineyards. This study aimed to assess the potential of high-resolution multispectral imagery acquired by UAVs to estimate the vine water status. The research was conducted over two growing seasons (2021 and 2022) in a commercial Merlot vineyard in Yepes (Toledo, Central Spain), under five irrigation regimes designed to generate a range of water statuses. UAV flights were performed at two times of day (09:00 and 12:00 solar time), coinciding with in-field measurements of physiological parameters. Stem water potential (SWP), chlorophyll content, and photosynthesis data were collected. The SWP consistently showed the strongest and most stable associations with vegetation indices (VIs) and the red spectral band at 12:00. A simple linear regression model using the NDVI explained up to 58% of the SWP variability regardless of the time of day or year. Multiple linear regression models incorporating the red and NIR bands yielded even higher predictive power (R² = 0.62). Stronger correlations were observed at 12:00, especially when combining data from both years, highlighting the importance of midday measurements in capturing water stress effects. These findings demonstrate the potential of UAV-based multispectral imagery as a non-destructive and scalable tool for the monitoring of the vine water status under field conditions. Full article

In recent years, the use of unmanned aerial vehicles (UAVs), commonly known as drones, has significantly surged across civil, military, and commercial sectors. Ensuring reliable and efficient communication between UAVs and between UAVs and base stations is challenging due to dynamic factors such as altitude, mobility, environmental obstacles, and atmospheric conditions, which existing communication models fail to address fully. This paper presents a multi-ray channel model that captures the complexities of the airspace network, applicable to both ground-to-air (G2A) and air-to-air (A2A) communications to ensure reliability and efficiency within the network. The model outperforms conventional line-of-sight assumptions by integrating multiple rays to reflect the multipath transmission of UAVs. The multi-ray channel model considers UAV flights’ dynamic and 3-D nature and the conditions in which UAVs typically operate, including urban, suburban, and rural environments. A technique that calculates the received power at a target UAV within a networked airspace is also proposed, utilizing the reflective characteristics of UAV surfaces along with the multi-ray channel model. The developed multi-ray channel model further facilitates the characterization and performance evaluation of G2A and A2A communications. Additionally, this paper explores the effects of various factors, such as altitude, the number of UAVs, and the spatial separation between them on the power received by the target UAV. The simulation outcomes are validated by empirical data and existing theoretical models, providing comprehensive insight into the proposed channel modelling technique. Full article

Three different vegetable oils, namely coconut oil, palm olein and olive oil, were analyzed for mineral oil hydrocarbons (MOHs) in our laboratory and in five commercial laboratories well recognized for their expertise in this field. The analysis consisted of a preliminary quantitative estimation of MOH content by hyphenated liquid chromatography–gas chromatography with flame ionization detection (LC-GC-FID), followed by a confirmatory analysis of MOH components by two-dimensional gas chromatography with time-of-flight mass spectrometry (GCxGC-ToF). The results provided by the six laboratories were compared to check their consistency, which would have led to a hypothetical commercial agreement or dispute scenarios, for instance. The comparison was based merely on information provided by the laboratories in their analytical reports (i.e., the methodology was not challenged, and chromatograms were not reviewed). Additionally, some of the laboratories were willing to provide some more information or details of the analysis. Similar quantitative results were provided by all six laboratories, emphasizing the utility of the current available harmonized guidelines and official standards for this method. However, as regards confirmatory results, discrepancies were observed among some laboratories in terms of the detection of MOH markers at low levels and the interpretation of GCxGC-ToF information. Even taking into account the limitation of this study as regards the reduced number of laboratories included, it highlights the need for harmonizing the GCxGC-ToF confirmatory method for MOHs in order to increase the alignment of results between laboratories for this kind of analysis. Full article

As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. Full article

To commercialize supersonic and hypersonic passenger aircraft and reusable spaceplanes, we are developing a small-scale supersonic flight experiment vehicle as a flying testbed for technical demonstrations in high-speed flight environments. This experiment vehicle is equipped with a fuel tank and an oxidizer tank, and the propellants inside the tanks slosh due to changes in acceleration during flight. In this situation, there is a risk of gas entrainment during liquid discharge, which could potentially cause an engine malfunction. To avoid such a situation, we considered installing a propellant management device (PMD) inside the tank to suppress the gas entrainment. In this study, a capillary type PMD with a screen channel structure, commonly used in satellites featuring no moving parts, was adopted due to its applicability to a wide acceleration range. The PMD was designed with a structure featuring cylindrical mesh screen nozzles installed at the top and bottom of a cylindrical tank. A one-dimensional flow analysis model was developed taking into account factors such as the pressure loss across the mesh screens and the flow loss within the mesh screen nozzles, which enabled the identification of conditions under which gas entrainment occurred. In this analytical model, separate formulations were developed using Hartwig’s and Ingmanson’s formulas for evaluating the flow losses through the mesh screens. Furthermore, by applying the flow analysis model, the specifications of the mesh screens as key parameters of the PMD, together with the nozzle diameter and nozzle length, were selected. Moreover, we fabricated prototype PMDs with each nozzle and conducted visualization tests using a transparent tank. The tests were conducted under static conditions, where a gravitational acceleration acted downward, and the effects of the cylindrical mesh screen length and discharge flow rate on the free surface height at which gas entrainment occurred were investigated. This experiment demonstrated the effectiveness of the propellant acquisition mechanism of the present PMD. The height of the free surface was also compared with the experimental and analytical results, and it was shown that the results obtained by using Ingmanson’s formula for pressure loss through the screen mesh were closer to the experimental results. These findings demonstrated the validity of the one-dimensional flow analysis model. Full article

Background: Aneurysm-related subarachnoid hemorrhage is a life-threatening form of stroke. While medical image acquisition for aneurysm screening is limited to high-risk patients, advances in artificial intelligence (AI)-based image analysis suggest that AI-driven routine screening of imaging studies acquired for other clinical reasons could be valuable. Methods: A representative cohort of 1761 routine cranial magnetic resonance imaging scans [cMRIs] (with time-of-flight angiographies) from patients without previously known intracranial aneurysms was established by combining 854 general radiology 1.5T and 907 neuroradiology 3.0T cMRIs. TOF-MRAs were analyzed with a commercial AI algorithm for aneurysm detection. Neuroradiology consultants re-assessed cMRIs with AI results, providing Likert-based confidence scores (0–3) and work-up recommendations for suspicious findings. Original cMRI reports from more than 90 radiologists and neuroradiologists were reviewed, and patients with new findings were contacted for consultations including follow-up imaging (cMRI / catheter angiography [DSA]). Statistical analysis was conducted based on descriptive statistics, common diagnostic metrics, and the number needed to screen (NNS), defined as the number of cMRIs that must be analyzed with AI to achieve specific clinical endpoints. Results: Initial cMRI reporting by radiologists/neuroradiologists demonstrated a high risk of incidental aneurysm non-reporting (94.4% / 86.4%). A finding-based analysis revealed high AI algorithm sensitivities (100% [3T] / 94.1% [1.5T] for certain aneurysms of any size, well above 90% for any suspicious findings > 2 mm), associated with AI alerts triggered in 22% of cMRIs with PPVs of 7.5–25.2% (depending on the inclusion of inconclusive findings). The NNS to prompt further imaging work-/follow-up was 22, while the NNS to detect an aneurysm with a possible therapeutic impact was 221. Reference readings and patient consultations suggest that routine AI-driven cMRI screening would lead to additional imaging for 4–5% of patients, with 0.45% to 0.74% found to have previously undetected aneurysms with possibly therapeutic implications. Conclusions: AI-based second-reader screening substantially reduces incidental aneurysm non-reporting but may disproportionally increase follow-/work-up imaging demands also for minor or inconclusive findings with associated patient concern. Future research should focus on (subgroup-specific) AI optimization and cost-effectiveness analyses. Full article

Developing control systems for high aspect ratio aircraft can be challenging due to the flexibility of the structure involved in the control loop design. A model-based approach can be straightforward to tune the control system parameters and, to this aim, a reliable aircraft flexible model is mandatory. This paper aims to present the approach pursued to design a control strategy considering the flexible aircraft simulator in the loop. Once the elastic model for the longitudinal dynamics has been set up, genetic algorithms are used to determine-together with a Linear Quadratic Regulator controller—a logic to improve the dynamic behaviour whilst encountering a gust. A relatively low order elastic model is developed for the dynamics in the longitudinal plane, including both rigid body and elastic degrees of freedom defined in a vehicle-fixed reference frame. The rigid body degrees of freedom and the associated states are the same as those of the rigid vehicle, whilst the additional states represent the elastic degrees of freedom. Modal characteristics are calculated from a finite element model of the aircraft using a commercial code, with the weight distribution added as lumped masses on grid points, while the aerodynamic rigid properties are described with a nonlinear database. Using the 2-D strip theory and neglecting the unsteady effects, the aeroelastic stability derivatives, i.e., elastic influence coefficients, are computed to superimpose the elastic effects on the rigid body degrees of freedom and vice versa. The flexible dynamics is compared to the rigid one in order to highlight the relevant changes in the aircraft modes. Following is herein proposed a control strategy combining genetic algorithms and Linear Quadratic Regulator controller to reduce the load factor, also considering the oscillation amplitude due to a deterministic gust encountered in a predefined flight condition. Full article

Ice accumulation poses a significant hazard to aviation safety, particularly in cold weather conditions, as it can compromise aerodynamic performance, increase structural weight, and diminish lift, occasionally resulting in severe stall incidents. At present, such risks are managed through the use of energy-demanding active ice protection systems (IPSs), which operate either by inhibiting ice formation (anti-icing) or by removing existing ice (de-icing). Nonetheless, in the context of future sustainable aviation, there is a pressing need to develop IPSs with lower energy requirements. A promising approach involves hybrid IPSs that integrate conventional active systems with passive superhydrophobic or icephobic surface treatments, which are capable of preventing, delaying, or minimizing ice buildup. These systems offer the potential to substantially decrease the energy consumption and consequently the CO₂ emissions. Furthermore, in accordance with FAA regulations, active IPSs are not permitted to operate during takeoff and initial flight stages to prevent any reduction in engine thrust. These two reasons emphasize the critical importance of developing efficient coatings that, on the one hand, promote the mobility of water droplets, hereby preventing ice formation, as achieved by superhydrophobic surfaces, and on the other hand, facilitate ice detachment, as required for icephobic performance. In this context, the primary objective of the present work is to emphasize the icephobic properties of two superhydrophobic coatings. To achieve this, an extensive characterization is first conducted, including wettability, Surface Free Energy (SFE), and surface roughness, to confirm their superhydrophobic nature. This is followed by an assessment of their icephobic performance, specifically in terms of ice adhesion strength, with comparisons made against a commercial aeronautical coating. The results revealed a significant reduction in both the wettability and SFE of the developed coatings compared to the reference, along with a marked decrease in ice adhesion strength, thereby demonstrating their icephobic properties. Future activities will focus on the combination of coatings with active IPS in order to assess the energy efficiency under extensive icing conditions where both superhydrophobic and icephobic properties are required. Full article

Hydrogen-powered aviation is gaining momentum as a sustainable alternative to fossil-fueled flight, yet the field faces complex technological and operational challenges. To better understand commercial innovation pathways, this study analyzes the claims sections of 166 hydrogen aviation patents issued between 2018 and 2024. Unlike prior studies that focused on patent titles or abstracts, this approach reveals the protected technical content driving commercialization. The study classifies innovations into seven domains: fuel storage, fuel delivery, fuel management, turbine enhancement, fuel cell integration, hybrid propulsion, and safety enhancement. Thematic word clouds and term co-occurrence networks based on natural language processing techniques validate these classifications and highlight core technical themes. Scientometric analyses uncover rapid patent growth, rising international participation, and strong engagement from both established aerospace firms and young companies. The findings provide stakeholders with a structured view of the innovation landscape, helping to identify technological gaps, emerging trends, and areas for strategic investment and policymaking. This claims-based method offers a scalable framework to track progress in hydrogen aviation and is adaptable to other emerging technologies. Full article

The identification of filamentous fungi by matrix-assisted laser desorption/ionization–time of flight mass spectrometry (MALDI–TOF MS) represents a challenge due to their complex taxonomy and the lack of comprehensive databases. The aim of this study was to evaluate the current status of available MALDI–TOF MS databases for the identification of dermatophytes, including commercial, in-house, and web-based databases. We collected 289 dermatophyte strains from different centers and analyzed them using four databases and a combination of them. The combination of commercial and in-house databases was shown to improve the identification rate and accuracy at the species level. For Trichophyton rubrum, the concordance among all databases was above 90.0%. For the T. mentagrophytes group, correct identification at the species level ranged from 30.0 to 78.9%, depending on the database, and showed very low agreement among them. The addition of the novel species T. japonicum to our in-house database resulted in the successful identification of this species. On the other hand, T. interdigitale and T. tonsurans were the species most frequently misidentified by MALDI–TOF MS. Through deep spectra analysis of both species, up to 29 protein peaks were found to be suitable for their differentiation, demonstrating the potential of peak analysis in differentiating closely related species. In conclusion, improvements of the databases with new strains resulted in increased identification accuracy at the species level. This, combined with peak analysis, could improve the overall identification of dermatophytes by MALDI–TOF MS in clinical laboratories. Full article