Search Results (117)

In wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and the spatial distribution of pesticide efficacy. However, current research lacks comprehensive quantification and correlation analysis of the temporal response characteristics of leaves under wind disturbances. To address this gap, a systematic analytical framework was proposed, integrating real-time leaf segmentation and tracking, geometric feature quantification, and statistical correlation modeling. High-frame-rate videos of fluttering leaves were acquired under controlled wind conditions, and background segmentation was performed using principal component analysis (PCA) followed by clustering in the reduced feature space. A fine-tuned Segment Anything Model 2 (SAM2-FT) was employed to extract dynamic leaf masks and enable frame-by-frame tracking. Based on the extracted masks, time series of leaf area and inclination angle were constructed. Subsequently, regression analysis, cross-correlation functions, and Granger causality tests were applied to investigate cooperative responses and potential driving relationships among leaves. Results showed that the SAM2-FT model significantly outperformed the YOLO series in segmentation accuracy, achieving a precision of 98.7% and recall of 97.48%. Leaf area exhibited strong linear coupling and directional causality, while angular responses showed weaker correlations but demonstrated localized synchronization. This study offers a methodological foundation for quantifying temporal dynamics in wind–leaf systems and provides theoretical insights for the adaptive control and optimization of intelligent spraying strategies. Full article

In recent years, 3D pixel sensors have been a topic of increasing interest within the High Energy Physics community. Due to their inherent radiation hardness, demonstrated up to a fluence of $3\times {10}^{16}$ 1 MeV equivalent neutrons per square centimeter, 3D pixel sensors have been used to equip the innermost tracking layers of the ATLAS and CMS detector upgrades at the High-Luminosity Large Hadron Collider. Additionally, the next generation of vertex detectors calls for precise measurement of charged particle timing at the pixel level. Owing to their fast response times, 3D sensors present themselves as a viable technology for these challenging applications. Nevertheless, both radiation hardness and fast timing require 3D sensors to be operated with high bias voltages on the order of ∼150 V and beyond. Special attention should therefore be devoted to avoiding problems that could cause premature electrical breakdown, which could limit sensor performance. In this paper, TCAD simulations are used to gain deep insight into the impact of surface isolation layers (i.e., p-stop and p-spray) used by different vendors on the high-voltage tolerance of small-pitch 3D sensors. Results relevant to different geometrical configurations and irradiation scenarios are presented. The advantages and disadvantages of the available technologies are discussed, offering guidance for design optimization. Experimentalmeasurements from existing samples based on both isolation techniques show good agreement with simulated breakdown voltages, thereby validating the simulation approach. Full article

Decontamination robots are becoming more common in environments where reducing human exposure to hazardous substances is essential, including healthcare settings, laboratories, and industrial cleanrooms. Designing terrain-capable decontamination robots quickly is challenging due to varying operational surfaces and mobility limitations. To tackle this issue, a structured recommendation framework is proposed to automate selecting optimal locomotion types and track configurations, significantly cutting down design time. The proposed system features a two-stage evaluation process: first, it creates an annotated compatibility score matrix by validating locomotion types against a robust dataset based on factors like friction coefficient, roughness, payload capacity, and slope gradient; second, it employs a weighted scoring model to rank wheel/track types based on their appropriateness for the identified environmental conditions. User needs are processed dynamically using a large language model, enabling flexible and scalable management of various deployment scenarios. A prototype decontamination robot was developed following the proposed algorithm’s guidance. This framework speeds up the configuration process and establishes a foundation for more intelligent, terrain-aware robot design workflows that can be applied to industrial, healthcare, and service robotics sectors. Full article

Quasicrystalline coatings based on Al₆₅Cu₂₀Fe₁₅ are of increasing interest as potential alternatives to conventional wear-resistant materials due to their unique structural and tribological properties. This study explores the influence of air pressure during high-velocity oxy-fuel (HVOF) spraying on the phase composition, morphology, and wear behavior of Al₆₅Cu₂₀Fe₁₅ coatings deposited on U8G tool steel. Coatings were applied at a fixed spraying distance of 350 mm using three air pressures (1.9, 2.1, and 2.3 bar), with constant propane (2.0 bar) and oxygen (2.1 bar) supply. X-ray diffraction analysis identified the formation of Al₇₈Cu₄₈Fe₁₄ and Al_0.5Fe_1.5 phases, while scanning electron microscopy revealed a dense, uniform microstructure with low porosity and homogeneous element distribution across all samples. Tribological testing using the ball-on-disk method showed wear track widths ranging from 853.47 to 952.50 µm, depending on the air pressure applied. These findings demonstrate that fine-tuning the air pressure during HVOF spraying significantly influences the structural characteristics and wear resistance of the resulting quasicrystalline coatings, highlighting their promise for advanced surface engineering applications. Full article

With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, DRL can help UAVs plan more efficient flight paths to cover more areas in less time. To enhance the systematicity and credibility of this review, this paper systematically examines the application status, key issues, and development trends of DRL in agricultural scenarios, based on the research literature from mainstream Chinese and English databases spanning from 2018 to 2024. From the perspective of algorithm–hardware synergy, the article provides an in-depth analysis of DRL’s specific applications in agricultural ground platform navigation, path planning for intelligent agricultural end-effectors, and autonomous operations of low-altitude unmanned aerial vehicles. It highlights the technical advantages of DRL by integrating typical experimental outcomes, such as improved path-tracking accuracy and optimized spraying coverage. Meanwhile, this paper identifies three major challenges facing DRL in agricultural contexts: the difficulty of dynamic path planning in unstructured environments, constraints imposed by edge computing resources on algorithmic real-time performance, and risks to policy reliability and safety under human–machine collaboration conditions. Looking forward, the DRL-driven smart transformation of agricultural machinery will focus on three key aspects: (1) The first aspect is developing a hybrid decision-making architecture based on model predictive control (MPC). This aims to enhance the strategic stability and decision-making interpretability of agricultural machinery (like unmanned tractors, harvesters, and drones) in complex and dynamic field environments. This is essential for ensuring the safe and reliable autonomous operation of machinery. (2) The second aspect is designing lightweight models that support edge-cloud collaborative deployment. This can meet the requirements of low-latency responses and low-power operation in edge computing scenarios during field operations, providing computational power for the real-time intelligent decision-making of machinery. (3) The third aspect is integrating meta-learning with self-supervised mechanisms. This helps improve the algorithm’s fast generalization ability across different crop types, climates, and geographical regions, ensuring the smart agricultural machinery system has broad adaptability and robustness and accelerating its application in various agricultural settings. This paper proposes research directions from three key dimensions-“algorithm capability enhancement, deployment architecture optimization, and generalization ability improvement”-offering theoretical references and practical pathways for the continuous evolution of intelligent agricultural equipment. Full article

To address the challenges of low pesticide utilization, insufficient automation, and health risks in orchard plant protection, we developed an autonomous spraying vehicle using ArduPilot firmware and a robot operating system (ROS). The system tackles orchard navigation hurdles, including global navigation satellite system (GNSS) signal obstruction, light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) error accumulation, and lighting-limited visual positioning. A key innovation is the integration of an extended Kalman filter (EKF) to dynamically fuse T265 visual odometry, inertial measurement unit (IMU), and GPS data, overcoming single-sensor limitations and enhancing positioning robustness in complex environments. Additionally, the study optimizes PID controller derivative parameters for tracked chassis, improving acceleration/deceleration control smoothness. The system, composed of Pixhawk 4, Raspberry Pi 4B, Silan S2L LIDAR, T265 visual odometry, and a Quectel EC200A 4G module, enables autonomous path planning, real-time obstacle avoidance, and multi-mission navigation. Indoor/outdoor tests and field experiments in Sun Village Orchard validated its autonomous cruising and obstacle avoidance capabilities under real-world orchard conditions, demonstrating feasibility for intelligent plant protection. Full article

The tribological behavior of molybdenum disulfide (MoS₂) coatings was systematically investigated under various controlled gas environments in a vacuum chamber. A hemispherical steel pin was slid cyclically over a MoS₂-coated steel disk, prepared via high-speed powder spraying. The study measured both dynamic and static friction coefficients under different gaseous atmospheres, including high vacuum, helium, argon, dry air, and water vapor. In high vacuum (10⁻⁵ Pa), an ultra-low dynamic friction coefficient (µ ≈ 0.01) was observed, while increasing values were recorded with helium (µ ≈ 0.03), argon (µ ≈ 0.04), dry air (µ ≈ 0.17), and water vapor (µ ≈ 0.30). Static friction coefficients followed a similar trend, decreasing significantly upon evacuation of water vapor or injection of inert gases. Surface analyses revealed that friction in vacuum or inert gases promoted smooth wear tracks and basal plane alignment of MoS₂ crystallites, while exposure to water vapor led to rougher, more disordered wear surfaces. Mass spectrometry and energetic modeling of physisorption interactions provided further insights into gas–solid interfacial mechanisms. These results demonstrate that the tribological performance of MoS₂ coatings is highly sensitive to the surrounding gas environment, with inert and vacuum conditions favoring low friction through enhanced basal plane orientation and minimal gas–surface interactions. In contrast, water vapor disrupts this structure, increasing friction and surface degradation. Understanding these interactions is crucial for optimizing MoS₂-based lubrication systems in varying atmospheric or sealed environments. Full article

The fluttering of leaves under wind fields significantly impacts the efficiency and precision of agricultural spraying. However, existing spraying technologies often overlook the complex mechanisms of wind–leaf interactions. This study integrates the fine-tuned Segment Anything Model 2 with multi-dimensional dynamic behavior analysis to provide a systematic approach for investigating leaf fluttering under wind fields. First, a segmentation algorithm based on Principal Component Analysis was employed to eliminate background interference in leaf fluttering data. The results showed that the segmentation algorithm achieved an Intersection over Union (IoU) ranging from 98.2% to 98.7%, with Precision reaching 99.0% to 99.5%, demonstrating high segmentation accuracy and reliability. Building on this, experiments on leaf segmentation and tracking in dynamic scenarios were conducted using the SAM2-FT model. The results indicated that SAM2-FT effectively captured the dynamic behavior of leaves by integrating spatiotemporal information, achieving Precision and AP50/% values exceeding 97%. Its overall performance significantly outperformed mainstream YOLO-series models. In the analysis of dynamic response patterns, the Hilbert transform and time-series quantification methods were introduced to reveal the amplitude, frequency, and trajectory characteristics of a leaf fluttering under wind fields across three dimensions: area, inclination angle, and centroid. This comprehensive analysis highlights the dynamic response characteristics of leaves to wind field perturbations. Full article

Background: Ciprofloxacin (CIP) is a poorly water-soluble fluoroquinolone-type antibiotic that can be useful in the treatment of lung infections. When the drugs are delivered directly to the lungs, a smaller dosage is needed to achieve the desired effect compared to the oral administration. Moreover, the application of nanoparticles potentially enhances the effectiveness of the treatments while lowering the possible side effects. Therefore, we aimed to develop a “nano-in-micro” structured dry powder inhaler formulation containing CIP. Methods: A two-step preparation method was used. Firstly, a nanosuspension was first prepared using a high-performance planetary mill by wet milling. After the addition of different additives (leucine and mannitol), the solid formulations were created by spray drying. The prepared DPI samples were analyzed by using laser diffraction, nanoparticle tracking analysis, scanning electron microscopy, X-ray powder diffraction, and differential scanning calorimetry. The solubility and in vitro dissolution tests in artificial lung fluid and in vitro aerodynamic investigations (Spraytec^® device, Andersen Cascade Impactor) were carried out. Results: The nanosuspension (D50: 140.0 ± 12.8 nm) was successfully prepared by the particle size reduction method. The DPIs were suitable for inhalation based on the particle diameter and their spherical shape. Improved surface area and amorphization after the preparation processes led to faster drug release. The excipient-containing systems were characterized by large lung deposition (fine particle fraction around 40%) and suitable aerodynamic diameter (between 3 and 4 µm). Conclusions: We have successfully formulated a nanosized antibiotic-containing formulation for pulmonary delivery, which could provide a potential treatment for patients with different respiratory infections. Full article

Soil compaction is a widespread problem, leading to soil degradation, yield losses, and adverse environmental impacts. Nowadays, various measurement methods exist to assess and map soil compaction, with vertical cone penetration resistance measurements being one of the most commonly used. This method is easy, rapid, inexpensive, and generally accepted. However, manual penetration resistance measurements are time-consuming, labor-intensive, and often less accurate due to inconsistent penetration speed. To address these limitations, an automated penetrometer was developed and integrated on an autonomous robot platform, paving the way for high-resolution compaction mapping as a starting point for precision subsoiling to remediate soil compaction. The performance of this setup was validated in controlled and field conditions against a hand-held penetrometer. Therefore, experiments were conducted in soil-filled cylinders and on plots of a long-term field experiment, including measurements across spraying tracks. The automated penetrometer demonstrated high correlations with the hand-held device under controlled conditions, though the correlation was somewhat lower in the field due to the soil’s heterogeneity. Deviations between the two measurement devices were likely caused by the inconsistent insertion speed of the hand-held penetrometer, particularly in soils with high penetration resistance. Both penetrometers successfully identified the plow pan at a depth of 30–40 cm but were unable to clearly show the effect of the long-term presence of spraying tracks. Full article

Based on the characteristics of small agricultural machinery in terms of flexibility and high efficiency when operating in small plots of hilly and mountainous areas, as well as the demand for improving the automation and intelligence levels of agricultural machinery, this paper conducted research on the path tracking control of the automatic navigation operation of a crawler sprayer. Based on the principles of the kinematic model and the position prediction model of the agricultural machinery chassis, a pure pursuit controller based on adaptive look-ahead distance was designed for the tracked motion chassis. Using a lightweight crawler sprayer as the research platform, integrating onboard industrial control computers, sensors, communication modules, and other hardware, an automatic navigation operation system was constructed, achieving precise control of the crawler sprayer during the path tracking process. Simulation test results show that the path tracking control method based on adaptive look-ahead distance has the characteristics of smooth control and small steady-state error. Field tests indicate that the crawler sprayer exhibits small deviations during path tracking, with an average absolute error of 2.15 cm and a maximum deviation of 4.08 cm when operating at a speed of 0.7 m/s. In the line-following test, with initial position deviations of 0.5 m, 1.0 m, and 1.5 m, the line-following times were 7.45 s, 11.91 s, and 13.66 s, respectively, and the line-following distances were 5.21 m, 8.34 m, and 9.56 m, respectively. The maximum overshoot values were 6.4%, 10.5%, and 12.6%, respectively. The autonomous navigation experiments showed a maximum deviation of 5.78 cm and a mean absolute error of 2.69 cm. The proportion of path deviations within ±5 cm and ±10 cm was 97.32% and 100%, respectively, confirming the feasibility of the proposed path tracking control method. This significantly enhanced the path tracking performance of the crawler sprayer while meeting the requirements for autonomous plant protection spraying operations. Full article

This paper focuses on the design and development of a customized 7-axis suspended robotic arm for automated spraying production lines. The design process considers factors such as workspace dimensions, workpiece sizes, and suspension positions. After analyzing degrees of freedom and workspace coordinates, 3D modeling ensures the arm can reach designated positions and orientations. Servo motors and reducers are selected based on load capacity and speed requirements. A suspended body method allows flexible use within the workspace. Kinematics analysis is conducted, followed by trajectory-tracking experiments using the manifold deformation control method. Results from simulation and real experiments show minimal error in tracking, demonstrating the effectiveness of the control method. Finally, the actual coating thickness sprayed by the 7-axis suspended robotic arm at four locations on the motorcycle shell was measured. The results show that the measured values at each location fall within the standard range provided by the manufacturer, demonstrating consistency in spraying across different regions. This consistency highlights the precision and effectiveness of the robotic arm’s control system in achieving uniform coating thickness, even on complex and curved surfaces. Therefore, the robotic arm has been successfully applied in a factory’s automated spraying production line. Full article

Wood–plastic composites (WPC) combine the properties of polymers and wood, providing an attractive alternative to traditional materials, particularly for terrace flooring. When exposed to various environmental conditions, WPCs are affected by factors, such as water and ultraviolet (UV) radiation. Although most test methods for assessing the durability of these products have focused on changes in mechanical properties and linear dimensions, out-of-plane deformations (concavity and convexity) are often overlooked. This study focusses on evaluating the usefulness of the test method that allows for precise determination of these deformations after ageing. The test procedure involves exposure to classic weathering for decking boards, including moisture, UV radiation, and water spray, followed by three-dimensional (3D) scanning to track deformation after different exposure times. Analysis of variance was used to assess whether the sensitivity of this method is sufficient to detect minor deformations. Additionally, scanning electron microstructural images of the aged samples were examined to determine whether there was a relationship between the deformation and the microstructural changes. This study demonstrated the potential to use scanning methods for assessing the aspects of ageing resistance of this type of composite product in the context of deformation. Full article

This study examines the impact of various abrasive balls and sliding loads on WC-12Co coatings. For this purpose, 4 N, 8 N, and 12 N loads were applied to the WC-12Co composite coatings with Al₂O₃ and Si₃N₄ balls. WC-12 Co composite was deposited by the high-velocity oxygen fuel method on the AISI 304 substrate. The wear tests were conducted in accordance with ASTM G99 on a ball-on-disc tribometer at room temperature. In order to study the results of the coating tests, wear volume loss was measured against each counter body. Surface roughness and microstructure changes before and after wear were examined by electron microscopy. The resulting wear tracks were examined with an optical profilometer and the wear amount was calculated. When comparing the Al₂O₃ ball with the Si₃N₄ ball, the Al₂O₃ ball corrodes WC-12Co coatings more and is most susceptible to abrasive grooving, brittle cracking, and spalling. Wear rates rose by 77%, 58%, and 67% when the Si₃N₄ abrasive sample and the samples with Al₂O₃ coating were subjected to 4 N, 8 N, and 12 N loads, respectively. WC-12Co coating layers and powders were subjected to X-ray diffraction analyses, which revealed that coarse WC-12Co powder underwent less decarburization due to HVOF spraying. Full article

The present work examines the relationship between aerial spraying and its health impacts on the population living in the banana production areas of Ecuador (the rural sectors of the cantons Milagro and Naranjito, Guayas Province). Objectives: the objectives of this study are to obtain information on sanitation, basic services, and environmental rationality and to interpret the low levels of cholinesterase and prevalent diseases among the population. Methods: the methodology involved a face-to-face questionnaire, the formal authorization of an informed consent document, and venipuncture for cholinesterase tests. The information was processed in the EPI–INFO system 7.2 (statistical software for professionals and researchers dedicated to public health), with the certification of protocols issued by the Bioethics Committee of the Kennedy Hospital Clinic of Ecuador. Results: the results showed that 89.5% of inhabitants do not have access to drinking water, 92.5% do not have a sewage disposal service, 97.50% experience aerial spraying at their homes or workplaces, and 57% have low cholinesterase levels. Additionally, several gastrointestinal, respiratory, neurological, dermatological, and reproductive disorders were detected among the inhabitants. Conclusions: we found that companies in the banana sector have not implemented corporate social responsibility measures. For example, no blood tests are conducted to monitor cholinesterase levels or to track hereditary disorders. Moreover, entities such as the Ministry of Public Health have not taken action to serve this at-risk population. Full article