Search Results (5,363)

Contrails form as a result of water vapor bonding with soot emitted from jet engines at cruise altitudes, leading to contrail formation in Ice Supersaturated Regions (ISSRs). Contrails are estimated to contribute approximately 2% to total anthropogenic global warming. Some researchers have developed simulation models to estimate the frequency, duration, and spatial distribution of contrails. Other researchers have identified issues with the accuracy of the data for predicting the timing and precise geographic positioning of ISSRs. This study presents a systematic review of 22 peer-reviewed articles that included detailed models of ISSR identification, identifying three atmospheric data sources, four parameters, and two equations for calculating the parameters derived. A further analysis revealed differences in the temperature and RH_W readings across the three databases, resulting in differences in the RH_I calculations and the identification of ISSRs. Over an 18-month period in Sterling, Virginia, USA, the radiosonde data and two atmospheric forecast databases identified the ISSR conditions on 44%, 47%, and 77% of days, respectively. Broken down by a flight level between 30,000 and 39,999 feet in altitude, these differences are highlighted further. The forecast databases overestimated the presence of ISSRs compared to the radiosonde data. These findings underscore the variability inherent in atmospheric datasets and the conversion methods, highlighting potential areas for refinement in ISSR prediction, notably in the development of ensemble forecasts based on several atmospheric databases. The implications of these results, the limitations of this study, and future work are discussed. Full article

Improving decision-making in the autonomous maneuvering of unmanned aerial vehicles (UAVs) is of great significance to improving flight safety, the mission execution rate, and environmental adaptability. The method of deep reinforcement learning makes the autonomous maneuvering decision of UAVs possible. However, the current algorithm is prone to low training efficiency and poor performance when dealing with complex continuous maneuvering problems. In order to further improve the autonomous maneuvering level of UAVs and explore safe and efficient maneuvering methods in complex environments, a maneuvering decision-making method based on hierarchical reinforcement learning and ‌Proximal Policy Optimization (PPO) is proposed in this paper. By introducing the idea of hierarchical reinforcement learning into the PPO algorithm, the complex problem of UAV maneuvering and obstacle avoidance is separated into high-level macro-maneuver guidance and low-level micro-action execution, greatly simplifying the task of addressing complex maneuvering decisions using a single-layer PPO. In addition, by designing static/dynamic threat zones and varying their quantity, size, and location, the complexity of the environment is enhanced, thereby improving the algorithm’s adaptability and robustness to different conditions. The experimental results indicate that when the number of threat targets is five, the success rate of the H-PPO algorithm for maneuvering to the designated target point is 80%, which is significantly higher than the 58% rate achieved by the original PPO algorithm. Additionally, both the average maneuvering distance and time are lower than those of the PPO, and the network computation time is only 1.64 s, which is shorter than the 2.46 s computation time of the PPO. Additionally, as the complexity of the environment increases, the H-PPO algorithm outperforms other compared networks, demonstrating the effectiveness of the algorithm constructed in this paper for guiding intelligent agents to autonomously maneuver and avoid obstacles in complex and time-varying environments. This provides a feasible technical approach and theoretical support for realizing autonomous maneuvering decisions in UAVs. Full article

The development of a correlation-assisted direct time-of-flight (CA-dToF) pixel provides a novel solution for time-of-flight applications that combines low power consumption, robust ambient shot noise suppression, and a compact design. However, the pixel’s implementation introduces systematic errors, affecting its performance. We investigate the pixel’s robustness against various noise sources, including timing jitter, kTC noise, switching noise, and photon shot noise. Additionally, we address limitations such as the SPAD deadtime, and source follower gain mismatch and offset, identifying their impact on performance. The paper also proposes solutions to enhance the pixel’s overall reliability and to improve the pixel’s implementation. Full article

Nighttime semantic segmentation represents a challenging frontier in computer vision, made particularly difficult by severe low-light conditions, pronounced noise, and complex illumination patterns. These challenges intensify when dealing with Unmanned Aerial Vehicle (UAV) imagery, where varying camera angles and altitudes compound the difficulty. In this paper, we introduce NoctuDroneNet (Nocturnal UAV Drone Network, hereinafter referred to as NoctuDroneNet), a real-time segmentation model tailored specifically for nighttime UAV scenarios. Our approach integrates convolution-based global reasoning with training-only semantic alignment modules to effectively handle diverse and extreme nighttime conditions. We construct a new dataset, NUI-Night, focusing on low-illumination UAV scenes to rigorously evaluate performance under conditions rarely represented in standard benchmarks. Beyond NUI-Night, we assess NoctuDroneNet on the Varied Drone Dataset (VDD), a normal-illumination UAV dataset, demonstrating the model’s robustness and adaptability to varying flight domains despite the lack of large-scale low-light UAV benchmarks. Furthermore, evaluations on the Night-City dataset confirm its scalability and applicability to complex nighttime urban environments. NoctuDroneNet achieves state-of-the-art performance on NUI-Night, surpassing strong real-time baselines in both segmentation accuracy and speed. Qualitative analyses highlight its resilience to under-/over-exposure and small-object detection, underscoring its potential for real-world applications like UAV emergency landings under minimal illumination. Full article

Objective: During pregnancy, vulvovaginal infections (VVIs), including abnormal vaginal flora (AVF), bacterial vaginosis (BV), and vulvovaginal candidiasis (VVC), are associated with serious complications and discomfort. We aimed to elucidate the effectiveness of oral probiotics in secondary prevention of VVIs in pregnant women. Study design: A multicenter prospective randomized, double-blind, placebo-controlled trial was conducted at three medical centers between 2016 and 2021. Women who complained of vaginal symptoms with positive smear for AVF/BV and/or candida were treated with antibiotics or an antimycotic agent, respectively. After confirmation of VVI eradiation by repeated vaginal smear, the women were divided into a research group, receiving two capsules/day of oral probiotic formula containing Bifidobacterium bifidum, Bifidobacterium lactis, Lactobacillus (L.) acidophilus, L. paracasei, L. rhamnosus and Streptococcus thermophilus (>6 × 10⁹ CFU/capsule), and a control group, receiving a placebo (two capsules/day) until delivery. At least once a month or following complaints, a vaginal smear was taken to assess vaginal microbiota. If VVIs were found, they were treated with antibiotics/antimycotics, and eradication was assessed by a repeated vaginal smear. Lactobacilli vaginal colonization, including the specific strains from the probiotic capsules, were detected using the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI TOF-MS). The primary outcome was the rate of women who developed VVI during the study period until delivery. Results: Twenty-three and twenty-four women were analyzed in the probiotic and placebo cohorts, respectively. There was no difference in the rate of any VVI between the probiotic and placebo cohorts (16 (67%) versus 11 (48%), respectively; p = 0.19), time until first infection or pregnancy outcomes. The lactobacilli strains that colonized the vagina were similar at baseline and following probiotic or placebo administration. No woman was detected with vaginal colonization of the strains from the capsule, although the probiotics were taken for about 4 months. Conclusions: The oral probiotic product tested in this study did not reduce the recurrence rate of VVIs in pregnant women following eradication. Full article

Zinc oxide (ZnO) particles were successfully synthesized through the green method using aloe vera extract and zinc nitrate (1:1). The structure, morphology and properties of the biosynthesized ZnO (bioZnO) particles were analyzed by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), time of flight secondary ion mass spectrometry (TOF-SIMS) and thermogravimetry (TG). The morphology and the size of ZnO particles were elucidated by scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS). Then, the ability of bioZnO to activate sulfur curing of natural rubber (NR) was tested and compared to commercial ZnO traditionally used as vulcanization activator. The bioZnO showed similar activity in the vulcanization process to commercial ZnO. NR composites containing bioZnO were pro-ecological in nature and exhibited better mechanical characteristics and durability against thermo-oxidative aging than NR with commonly used micrometric ZnO. Moreover, NR vulcanizates containing bioZnO showed good mechanical properties in dynamic conditions and satisfactory thermal stability. The present research is new and in addition to the analysis of biosynthesized ZnO particles, the effect of the activator in the vulcanization process of the NR elastomer and its influence on the properties of the final products were additionally discussed. Full article

This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The system leverages advanced computer vision algorithms and an extended Kalman filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive maneuver computation. A novel hardware-in-the-loop (HIL) simulation environment was developed and used for validation, enabling the evaluation of closed-loop real-time interaction between the “Sense” and “Avoid” subsystems. Extensive numerical simulations and a flight test campaign demonstrate the system’s effectiveness in real-time detection and the avoidance of non-cooperative obstacles, ensuring compliance with UAV aero mechanical and safety constraints in terms of minimum separation requirements. The novelty of this research lies in (1) the design of an innovative and efficient visual processing pipeline tailored for SWaP-constrained mini-UAVs, (2) the formulation an EKF-based data fusion strategy integrating optical data with a custom-built Doppler radar, and (3) the development of a unique HIL simulation environment with realistic scenery generation for comprehensive system evaluation. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, significantly contributing to the safety of flight in non-segregated airspaces Full article

Real-time dynamic capture of a single moving target is one of the most crucial and representative tasks in UAV capture problems. This paper proposes a multi-UAV real-time dynamic capture strategy based on a differential game model to address this challenge. In this paper, the dynamic capture problem is divided into two parts: pursuit and capture. First, in the pursuit–evasion problem based on differential games, the capture UAVs and the target UAV are treated as adversarial parties engaged in a game. The current pursuit–evasion state is modeled and analyzed according to varying environmental information, allowing the capture UAVs to quickly track the target UAV. The Nash equilibrium solution in the differential game is optimal for both parties in the pursuit–evasion process. Then, a collaborative multi-UAV closed circular pipeline control method is proposed to ensure an even distribution of capture UAVs around the target, preventing excessive clustering and thereby significantly improving capture efficiency. Finally, simulations and real-flight experiments are conducted on the RflySim platform in typical scenarios to analyze the computational process and verify the effectiveness of the proposed method. Results indicate that this approach effectively provides a solution for multi-UAV dynamic capture and achieves desirable capture outcomes. Full article

Background/Objectives: Infection poses a significant threat of mortality in patients with chronic kidney disease (CKD) undergoing hemodialysis. Staphylococcus aureus (S. aureus) is a common etiological agent, with prior nasal colonization identified as a risk factor for infection. The aim of the present cross-sectional study was to determine the prevalence of nasal S. aureus carriage among patients with CKD undergoing dialysis at a university hospital and identify potential factors associated with colonization. Methods: Nasal swabs were obtained, and bacterial isolates were identified using matrix-assisted laser desorption/ionization time-of-flight spectrometry and antibiogram testing with the Vitek 2 system. Demographic and clinical data were collected for the investigation of risk factors associated with colonization. Results: Among the 96 patients analyzed, 34 were carriers of S. aureus. Among these carriers, three (8.8%) harbored oxacillin-resistant strains. More than half of the S. aureus strains exhibited resistance to clindamycin but susceptibility to oxacillin. Colonization was associated with age and the use of corticosteroids/immunosuppressants. Conclusions: The prevalence of nasal S. aureus carriage was high among patients undergoing dialysis at the university hospital, exceeding that found in the general population. Nasal colonization by S. aureus was linked to corticosteroid use and age. Understanding factors associated with S. aureus nasal colonization in patients on dialysis can assist healthcare providers in preventing the spread of infection and cross-contamination, while reducing risk in this population. Full article

As an emerging edge device aimed at consumers, Unmanned Aerial Vehicles (UAVs) have attracted significant attention in the consumer electronics market, particularly for intelligent imaging applications. However, aerial image detection tasks face two major challenges: first, there are numerous small and overlapping objects that are difficult to identify from an aerial perspective, and second, if the detection frame rate is not high enough, missed detections may occur when the UAV is moving quickly, which can negatively impact the user experience by reducing detection accuracy, increasing the likelihood of collision-avoidance failures, and potentially causing unsafe flight behavior. To address these challenges, this paper proposes a novel YOLO (you only look once) framework, named YOLOFLY, which includes a C4f feature extraction module and a DWcDetect head to make the model lightweight, as well as an MPSA attention mechanism and an ACIoU loss function, aimed at improving detection accuracy and performance for consumer-grade UAVs. Extensive experiments on the public VisDrone2019 dataset demonstrate that YOLOFLY outperforms the latest state-of-the-art model, YOLOv11n, by 3.2% in mAP_50-95, reduces detection time by 27.2 ms, decreases the number of parameters by 0.6 M, and cuts floating-point operations by 1.8 B. Finally, testing YOLOFLY in real-world environments also yielded the best results, including a 3.75% reduction in missed detections at high speeds. These findings validate the superiority and effectiveness of YOLOFLY. Full article

Predictions of the vertical location of a pitched ball (termed the passing height) when the ball arrives at an observer may be based on internal models of ball trajectory and situational cues, kinematic cues from the pitcher’s motion, and visual ball-flight cues. The informational content of ball-flight cues for judgments of vertical passing height when the ball’s launch angle is small and when situational and kinematic cues are limited is unknown. The purpose of this study was to determine whether passing heights can be judged adequately from ball-flight cues and whether judgments of passing height improve as viewing time increases under the aforementioned conditions. Twenty subjects stood 40 feet (12.19 m) from a pneumatic pitching machine that propelled tennis balls toward them at three speeds (from 53 mph (85 kph) to 77 mph (124 kph)). The ball’s launch angle was constant. The subject’s vision was blocked at 100 ms or 250 ms after pitch release. Subjects indicated the height at which they expected the ball to arrive. In the absence of explicit situational cues or kinematic cues and in the presence of a small and constant launch angle, the longer viewing time of ball-flight cues improved passing height estimates but did not result in accurate responses for the slower speeds. Full article

Background: UPLC-Q-TOF-MS (Ultra-Performance Liquid Chromatography–Quadrupole Time-of-Flight Mass Spectrometry) is a high-precision, high-density technology for lipid analysis. Sensitive skin is a sub-stable condition, and it has been reported that the population of sensitive skin in China is predominantly female. Meanwhile, women with sensitive skin have different physiological parameters as well as lipid compositions at different ages. The Yellow Emperor’s Classic of Internal Medicine states that the number of women’s life cycles is seven, that major changes are manifested every 7 years, and that aging begins at age 35. At present, the correlation between facial lipid composition and aging indicators of sensitive skin in women aged 36–42 and 43–49 years has not been reported. Objective: This study reveals the relationship between key lipid composition of the facial skin and the aging of sensitive skin in women aged 36–42 and 43–49 years. Methods: We used UPLC-Q-TOF-MS technology to study the changes in lipid composition in the sensitive skin of woman aged 36–42 and 43–49 years, using a multi-probe adapter system with different types of skin-testing probes to test physiological parameters. Three types of multivariate data—questionnaires, physiological indicators, and lipid composition—were used together to assess differences in aging in a population of women with sensitive skin at different ages. Results: 1. In the questionnaire part, the T1 group was more susceptible to sunburn and the T2 group was more susceptible to tanning. 2. In the physiological index part, the aging characteristics of facial skin in the T2 group were obvious, with the b-value, as well as the brown area size, being significantly higher than the T1 group, while the TWEL, sebum, R2 value, ITA value, pore count, and concentration of the red area were significantly lower than the T1 group. 3. In the lipid part, the total facial lipid content was higher in the T2 group, with a significantly higher GP lipid, and the 47 VIP lipids obtained were analyzed by ROC curves, narrowing down to six lipids, PS(2-OMe-21:0/0:0), PS(O-18:0/20:5 (5Z,8Z,11Z,14Z,17Z)), PA(O-16:0/20:5 (5Z,8Z,11Z,14Z,17Z)), PS (P-16:0/12:0, PA (O-16:0/22:2 (13Z,16Z)), and PC (19:3 (10Z,13Z,16Z)/0:0)), and all six lipids were higher in the T2 group. 4. In Spearman correlation analysis, PS(O-18:0/20:5(5Z,8Z,11Z,14Z,17Z)), PS(P-16:0/12:0), PS(2-OMe-21:0/0:0), PA(O-16:0/20:5(5Z,8Z,11Z,14Z,17Z)), and PC(19:3( 10Z,13Z,16Z)/0:0), which are five lipids and skin aging indicators (TWEL, sebum, ITA value, b-value, pore count, concentration of red area, and brown area size) were significantly correlated. Conclusions: Through correlation analysis, it was found that changes in the composition of skin surface lipids (SSLs) in both age groups have an important influence on facial physiological indicators (aging manifestations) and played an important role in furthering the understanding of sensitive skin aging. Therefore, these lipid components also provide theoretical support for the development of cosmetic ingredients that slow down the aging of sensitive skin. Full article

In situ X-ray diffraction has been used to investigate the stability of expanded austenite during annealing in vacuum for the austenitic stainless steel 316Ti, the super-austenitic stainless steel 904L, and the duplex steel 318LN. Expanded austenite has been formed using plasma immersion ion nitriding before. Time-of-flight secondary ion mass spectrometry before and after annealing yielded complementary information regarding nitrogen depth profiles and CrN precipitation using cluster analysis. The decay of expanded austenite during annealing was found to be thermally activated with an activation energy of 1.8 ± 0.3 eV, starting within five minutes at 550 °C and taking more than two hours below 450 °C. The decay occurs simultaneously throughout the whole nitrogen-containing zone—and not at the surface as during nitriding. Nitrogen diffusion occurring in parallel slightly complicates the data analysis. Further transmission electron microscopy investigations are necessary to understand the microstructure after annealing in vacuum. The limit for operating hard and wear-resistant expanded austenite layers at elevated temperatures of up to 350 °C is given, however, by nitrogen diffusion and not the decay into CrN. Full article

Due to their high flexibility, low cost, and energy-saving advantages, applying Unmanned Aerial Vehicles (UAVs) in logistics is a promising field to achieve better social and economic benefits. Since UAVs’ energy storage capacity is generally low, it is essential to reduce energy costs to improve their system’s energy efficiency. In this paper, we proposed a novel trajectory planning framework to achieve the optimal trajectory with the minimum amount of energy consumption under the constraints of obstacles in a static environment. Based on UAV dynamics, we first derived the required power functions of multi-rotor UAVs in vertical and horizontal flight. To generate a feasible trajectory, we first adopted the A* algorithm to find a path and developed a safe flight corridor for the UAV to fly across by expanding the waypoints against the environment, and then proposed a time-discretization method to formulate the trajectory generation problem and solve it by the convex optimization algorithm. The optimization results in a static environment with obstacles demonstrated that the proposed method could efficiently and effectively obtain the optimal trajectory with the minimum amount of energy consumption under different allowed mission times and payloads. The framework would promote a variety of logistics UAV applications relevant to trajectory planning. Full article

Recently, artificial intelligence (AI) has been adopted in a number of Internet of Things (IoT) application systems to enhance intelligence. We have developed a ready-made server with rich built-in functions to collect, process, display, analyze, and store data from various IoT devices, the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform, in which various AI techniques have been implemented to enhance its capabilities. In this paper, we present an application of SEMAR to a drone-based wall inspection system using an object detection AI model called You Only Look Once (YOLO). This system aims to detect wall cracks at high places using images taken via a camera on a flying drone. An edge computing device is installed to control the drone, sending the taken images through the Kafka system, storing them with the drone flight data, and sending the data to SEMAR. The images are analyzed via YOLO through SEMAR. For evaluations, we implemented the system using Ryze Tello for the drone and Raspberry Pi for the edge, and we evaluated the detection accuracy. The preliminary experiment results confirmed the effectiveness of the proposal. Full article