Search Results (4,338)

Instance segmentation of cotton leaves in complex field environments presents challenges including low accuracy, high computational complexity, and costly data annotation. This paper presents GS-BiFPN-YOLO, a lightweight instance segmentation method that integrates SAM for semi-automatic labeling and enhances YOLOv11n-seg with GSConv, BiFPN, and CBAMs to reduce annotation cost and improve accuracy. To streamline parameters, the YOLOv11-seg architecture incorporates the lightweight GSConv module, utilizing group convolution and channel shuffle. Integration of a Bidirectional Feature Pyramid Network (BiFPN) enhances multi-scale feature fusion, while a Convolutional Block Attention Module (CBAM) boosts discriminative focus on leaf regions through dual-channel and spatial attention mechanisms. Experimental results on a self-built cotton leaf dataset reveal that GS-BiFPN-YOLO achieves a bounding box and mask mAP@0.5 of 0.988 and a recall of 0.972, maintaining a computational cost of 9.0 GFLOPs and achieving an inference speed of 322 FPS. In comparison to other lightweight models (YOLOv8n-seg to YOLOv12n-seg), the proposed approach achieves superior segmentation accuracy while preserving high real-time performance. This research offers a practical solution for precise and efficient cotton leaf instance segmentation, thereby facilitating the advancement of intelligent monitoring systems for cotton production. Full article

Trajectory planning for intelligent connected vehicles (ICVs) must simultaneously address safety, efficiency, and environmental impact to align with sustainable development goals. This paper proposes a novel hierarchical trajectory planning framework, designed for intelligent connected vehicles (ICVs) that integrates a semantic corridor with a spatiotemporal potential field. First, a spatiotemporal safety corridor, enhanced with semantic labels (e.g., low-carbon zones and recommended speeds), delineates the feasible driving region. Subsequently, a multi-objective sampling optimization method generates candidate trajectories that balance safety, comfort and energy consumption. The optimal candidate is refined using a spatiotemporal potential field, which dynamically integrates obstacle predictions and sustainability incentives to achieve smooth and eco-friendly navigation. Comprehensive simulations in typical urban scenarios demonstrate that the proposed method reduces energy consumption by up to 8.43% while maintaining safety and a high level of comfort, compared with benchmark methods. Furthermore, the method’s practical efficacy is validated using real-world vehicle data, showing that the planned trajectories closely align with naturalistic driving behavior and demonstrate safe, smooth, and intelligent behaviors in complex lane-changing scenarios. The validation using 113 real-world truck lane-changing cases demonstrates high consistency with naturalistic driving behavior. These results highlight the framework’s potential to advance sustainable intelligent transportation systems by harmonizing safety, comfort, efficiency, and environmental objectives. Full article

With the increase in thrust–weight ratio of advanced aeroengines, the rotor and stator often exhibit comparable stiffness characteristics, leading to significant vibration coupling which harms the safety and reliability of operations. However, an effective vibration coupling evaluation method for complex rotor–stator systems is still lacking. This paper proposes the Vibration Coupling Evaluation Factor (VCEF) to quantitatively evaluate the interaction between the rotor and stator within the framework of the linear system. Then a new evaluation procedure is established for the structural optimization during the early design phase and the fault source localization in troubleshooting scenarios in the high-speed rotating machinery. In this paper, two typical rotor–stator systems are studied with the VCEF method: a simplified rotor–stator system is studied numerically to reveal the influence pattern of different parameters, and a complex rotor–stator system is studied numerically and experimentally to examine the validity of the evaluation method. The results show that VCEF can effectively capture rotor–stator vibration coupling. The VCEF curve with rotational speed shows a significant stepped decrease, indicating a significant strengthening of the rotor–stator vibration coupling, which aligns closely with experimental data. This evaluation method quantitatively assesses the degree of rotor–stator vibration coupling by comparing the differences in modal characteristics between the rotor system and the rotor–stator system under the gyroscopic effect. Optimizing rotor–stator stiffness and mass distribution based on VCEF mitigates operational risks in high-speed regimes. This methodology provides engineers with a systematic, quantitative tool to determine when integrated rotor–stator analysis is essential for accurate dynamic prediction and offers broad applicability to aeroengine design and other high-speed rotating machinery. Full article

Recent advances in experimental techniques for visualizing cloud behavior, pit formation, and erosion in cavitating jets have been reviewed. To characterize the erosion behavior of cavitating jets and clarify their erosion mechanisms, various experimental techniques—such as high-speed imaging, frame difference method, proper orthogonal decomposition (POD) analysis, pit sensors, polyvinylidene fluoride (PVDF) sensors, laser schlieren imaging, and cross schlieren imaging—have been developed. Experimental results demonstrated that the erosion mechanism of cavitating jets is highly correlated with periodic cloud behaviors, including the growth, shrinkage, and collapse, which generate impulsive pressure on the wall material. This pressure initiates random pits on the wall surface and is associated with the generation of microjets caused by the reentrant-jet mechanism during cloud collapse near the wall. Several shockwaves were generated at peak impulsive pressures when the cavitation cloud collapsed, and a microjet was formed. Some of these experimental findings were successfully reproduced in recent numerical studies; however, further numerical modeling of erosion behavior in cavitating jets is still needed. Furthermore, the behavior of cavitating jets on rough walls requires future study, as the erosion rate is significantly higher than that on smooth walls. Full article

This study presents a comprehensive systematic analysis, investigating hardware accelerators specifically designed for real-time cardiovascular signal processing, focusing mainly on Electrocardiogram (ECG), Photoplethysmogram (PPG), and blood pressure monitoring systems. Cardiovascular Diseases (CVDs) represent the world’s leading cause of morbidity and mortality, creating an urgent demand for efficient and accurate diagnostic technologies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically analysed 59 research papers on this topic, published from 2014 to 2024, categorising them into three main categories: signal denoising, feature extraction, and decision support with Machine Learning (ML) or Deep Learning (DL). A comprehensive performance benchmarking across energy efficiency, processing speed, and clinical accuracy demonstrates that hybrid Field Programmable Gate Array (FPGA)-Application Specific Integrated Circuit (ASIC) architectures and specialised Artificial Intelligence (AI) on Edge accelerators represent the most promising solutions for next-generation CVD monitoring systems. The analysis identifies key technological gaps and proposes future research directions focused on developing ultra-low-power, clinically robust, and highly scalable physiological signal processing systems. The findings provide guidance for advancing hardware-accelerated cardiovascular diagnostics toward practical clinical deployment. Full article

Stroke often results in lasting cognitive impairments that severely reduce independence and quality of life. Traditional neuropsychological assessments rely on mean scores that provide an average estimate of overall cognitive function but neglect the fluctuations in performance. The variability in performance can be captured as inconsistency, i.e., fluctuations across multiple trials within a single task or as dispersion, i.e., fluctuations across multiple tasks. While inconsistency has been extensively studied, the impact of post-stroke cognitive impairment on cognitive dispersion is unknown. In this study, ninety-five stroke survivors (41 cognitively impaired and 54 cognitively normal) completed a neuropsychological battery that captured performance across five cognitive domains: executive function, attention, memory, language, and processing speed. We compared the stroke groups on across- and within-domain cognitive dispersion. Cognitively impaired stroke individuals showed elevated dispersion within executive function compared to cognitively normal individuals. The two groups did not differ on any other within-domain or across-domain cognitive dispersion. Post-stroke cognitive impairment increased variability within executive functioning. Incorporating cognitive dispersion into routine post-stroke assessment can advance clinical practice by identifying subtle cognitive instability, anticipate supportive needs, and tailor rehabilitation plans for improving stroke care. Full article

Electrospinning has advanced from a lab technique to an industrial method, enabling modern filters that are high-performing, sustainable, recyclable, and non-toxic. This study produced recycled PA6 nanofibers using green solvents and incorporated non-toxic CuO nanoparticles via industrial free-surface electrospinning. Polymer solutions with concentrations of 12.5, 15.0 and 17.5 (w/v)% were electrospun directly onto recyclable polypropylene spunbond/meltblown nonwoven substrates to produce nanofibers with average fiber sizes of 80–250 nm. Electrospinning parameter optimization revealed that the 12.5 wt.% PA6 solution and the 2–3 mm·s⁻¹ winding speed had the optimal performance, attaining 98.06% filtering efficiency and a 142 Pa pressure drop. The addition of 5 wt.% CuO nanoparticles increased the membrane density and reduced the pressure drop to 162 Pa, thereby improving the filtration efficiency to 98.23%. Bacterial and viral filtration studies have demonstrated pathogen retention above 99%. Moreover, antibacterial and antiviral testing has demonstrated that membranes trap and inactivate microorganisms, resulting in a 2.0 log (≈approximately 99%) reduction in viral titer. This study shows that recycled PA6 can be converted into high-performance membranes using green, industrial electrospinning, introducing innovations such as non-toxic CuO functionalization and ultra-fine fibers on recyclable substrates, yielding sustainable filters with strong antimicrobial and filtration performance, which are suitable for personal protective equipment and medical filtration. Full article

With the advancement of smart management technologies, research on self-powered silicon PIN photodetectors has become increasingly important. In this paper, a triboelectric nanogenerator (TENG)-driven silicon PIN photodetector based on power management circuitry is proposed. Through rectification and filtering, the pulse signal from the TENG is converted into stable DC voltage, providing reverse bias for the photodetector. With a 5 MΩ sampling resistor, the system generates a voltage of 0.4 V in the absence of light, which gradually increases to 7.3 V and saturates as the light intensity increases to 300 Lux, demonstrating good compatibility and near independence from the TENG rotation speed. Additionally, a light communication system is constructed, with the TENG-driven silicon PIN photodetector as the receiver unit and a signal transmission unit consisting of a finger-pressed TENG combined with an LED. This system successfully transmits Morse code signals such as “SOS” and “TENG”. Full article

The H.266/Versatile Video Coding (VVC) standard was developed to address the growing demand for compressing ultra-high-definition video content, supporting resolutions ranging from 4K to 8K and beyond. H.266/VVC improves coding efficiency by introducing a flexible quadtree with nested multi-type tree (QT-MTT) partitioning and various advanced coding tools. However, these improvements substantially increase the encoding complexity. To address this issue, we propose a perception-driven and object-aware algorithm that accelerates the MTT process in H.266/VVC intra coding. Our method integrates pixel-level saliency detection with object bounding box detection. Specifically, visually distinguishable (VD) pixels are identified using a just noticeable distortion (JND) model based on average background luminance, while detected-object regions are extracted using a YOLO object detection network. These two types of perceptual information are combined to guide adaptive encoding decisions. For each frame, a perception-driven pixel map labeled with VD pixels and a YOLO-based object map are generated. Within the MTT framework, partitioning decisions are determined jointly by standard deviation metrics derived from VD pixels and detected-object region coverage. By incorporating flexible threshold settings, the proposed method can meet different users’ requirements. In this paper, we performed experiments under three threshold settings. The experimental results demonstrate that the proposed method reduces H.266/VVC intra coding time by 27.94% to 43.11%, with BDBR increases of only 1.02% to 1.53%, thus achieving an appropriate trade-off between encoding speed and coding efficiency. Full article

With the advancement of welding technology, the demand for 70S-6 welding wire steel has steadily increased in industries such as construction, automotive, pressure vessels, and line pipe manufacturing. To optimize the production process of the target material, this study utilized the finite-element software ABAQUS to numerically simulate the multi-pass finish rolling process of 70S-6 welding wire steel. The study investigates the effects of the key rolling parameters—pass distribution (8/10/12 passes), finish rolling temperature (860/880/900 °C), and rolling speed (0.5 Vp/1.0 Vp/1.5 Vp, here Vp denotes the reference industrial rolling speed) on the rolling force, temperature field, and equivalent stress/strain during finish rolling. The results show that the increased number of passes homogenizes deformation, reduces local stress concentration and enhances mechanical properties. Specifically, 12 passes reduce the peak rolling force from 250,972 N to 208,124 N, significantly enhancing stress and temperature uniformity across the section. Increasing the finish rolling temperature lowers the pass-averaged flow stress and attenuates rolling-force fluctuations. At 880 °C, the simulated core–surface temperature gradient is minimal (50 °C), whereas at 900 °C the gradient increases (80 °C) but the rolling-force histories exhibit a lower peak level and smaller low-frequency oscillations; thus 880 °C is preferable when through-thickness thermal uniformity is targeted, while 900 °C is more suitable when a smoother load response is required. Increasing the finish rolling speed from 0.5 Vp to 1.5 Vp reduces the peak rolling force from 233,165 N to 183,665 N and significantly damps low-frequency load oscillations. However, it concurrently intensifies stress localization at the outer-surface tracking points P3/P4, which are in direct contact with the rolls, where the local equivalent stress approaches 523 MPa, even though the overall strain distribution along the bar length becomes more uniform. Overall, the optimal processing window is identified as a 12-pass schedule, a finish rolling temperature of 880–900 °C, and a rolling speed of 1.0–1.5 Vp, which can improve both rolling quality and temperature and stress and strain uniformity. Full article

Axial harmonic excitation is an emerging method for enhancing drilling speed, yet its influence on the torsional dynamics of a drill string remains unclear. To investigate these effects, this study establishes a single-degree-of-freedom (SDOF) nonlinear torsional dynamic model capable of coupling axial harmonic excitation. The model, based on Stribeck friction theory, describes the interaction by coupling the axial harmonic load with the torsional dynamic equation. After non-dimensionalizing the model, the influence patterns of static load amplitude, dynamic load amplitude, and excitation frequency on the system’s dynamics are systematically investigated. The results show that increasing the static load amplitude aggravates stick-slip vibrations, whereas increasing the dynamic load amplitude is largely ineffective for suppression and may even induce complex motions. In contrast, adjusting the excitation frequency can suppress and even eliminate stick-slip vibrations, allowing the system to achieve stable, continuous rotation. Furthermore, an interaction effect exists between the static load amplitude and the excitation frequency; at any given frequency, the Percentage of Sticking Time (PST) increases as the static load amplitude grows. This study also reveals the non-monotonic nature of the frequency’s suppression effect on vibration. These findings demonstrate that frequency optimization is the fundamental strategy for vibration suppression, requiring the dynamic load frequency to be adjusted to a specific range based on the actual weight on bit (WOB) in drilling operations. This research provides not only a deep mechanistic understanding of the drill string’s nonlinear dynamics under complex excitation but also a key theoretical basis for designing vibration suppression strategies in advanced drilling technologies. Full article

Background: In the era of rapid digital transformation, efficient food logistics (FL) is critical for sustainability and competitiveness. Maintaining food quality, minimizing waste, and optimizing costs are complex challenges that advanced digital technologies aim to address, particularly amid growing e-commerce and last-mile delivery demands. This underscores the need for a structured, quantitative evaluation of technological solutions to ensure operational reliability, efficiency, and sustainability. Methods: This study employs a Multi-Criteria Decision Making (MCDM) model combining Criterion Impact LOSs (CILOS) and Multi-Objective Optimization on the basis of Simple Ratio Analysis (MOOSRA) to evaluate key FL technologies: IoT, blockchain, Big Data analytics, automation and robotics, and cloud/edge computing. Nine evaluation criteria relevant to logistics providers were used, covering operational efficiency, flexibility, sustainability, food safety, data reliability, KPI support, scalability, costs, and implementation speed. CILOS determined criteria weights by considering interdependencies, and MOOSRA ranked technologies by benefits-to-costs ratios. Sensitivity analysis validated result robustness. Results: Automation and robotics ranked highest for enhancing efficiency, reducing errors, and improving handling and safety. Blockchain was second, supporting traceability and data security. Big Data analytics was third, enabling demand prediction and inventory optimization. IoT ranked fourth, providing real-time monitoring, while cloud/edge computing ranked fifth due to indirect operational impact. Conclusions: The CILOS–MOOSRA model enables transparent, structured evaluation, integrating quantitative metrics with logistics providers’ priorities. Results highlight technologies that enhance efficiency, reliability, and sustainability while revealing integration challenges, providing a strategic foundation for digital transformation in FL. Full article

High-speed silicon (Si) photonic transmitters operating in the O-band require higher on-chip optical power to support advanced modulation formats and ever-increasing line rates. A straightforward approach is to operate laser diodes at higher output power or employ more specialized sources, but this raises cost and exacerbates nonlinear effects such as self-phase modulation, two-photon absorption, and free-carrier generation in high-index-contrast Si waveguides. This paper proposes a low-cost 4 × 1 tree-cascade multimode interference (MMI) power combiner on a Si-on-insulator platform at 1310 nm wavelength that enables coherent power scaling while remaining fully compatible with standard commercial O-band lasers. The device employs adiabatic tapers and low-loss S-bends to ensure uniform field evolution, suppress local field enhancement, and mitigate nonlinear phase accumulation. The optimized layout occupies a compact footprint of 12 µm × 772 µm and achieves a simulated normalized power transmission of 0.975 with an insertion loss of 0.1 dB. Spectral analysis shows a 3 dB bandwidth of 15.8 nm around 1310 nm, across the O-band operating window. Thermal analysis shows that wavelength drift associated with ±50 °C temperature variation remains within the device bandwidth, ensuring stable operation under realistic laser self-heating and environmental changes. Owing to its broadband response, fabrication tolerance, and compatibility with off-the-shelf laser diodes, the proposed combiner is a promising building block for O-band transmitters and photonic neural-network architectures based on cascaded splitter and combiner meshes, while preserving linear transmission and enabling dense, large-scale photonic integration. Full article

Wave energy is gaining momentum as a viable and environmentally sustainable source of renewable power, with the potential to contribute significantly to the global energy mix. Central to wave energy conversion is the power take-off system, where electromagnetic generators play a crucial role in determining overall system performance, reliability, and efficiency. This paper provides a review of wave energy conversion devices and classifies the main power take-off mechanisms. It evaluates and compares key generator types based on their performance under wave energy conditions. Among these, Permanent Magnet Synchronous Generators have demonstrated strong potential due to their high efficiency, power density, and suitability for low-speed direct drive configurations typical of wave environments. The review presents a detailed analysis of advanced permanent magnet generator topologies, focusing on structural designs, control methods, and wave-specific trade-offs. It also investigates hierarchical control strategies, where high-level decisions are based on wave conditions and low-level control ensures accurate generator operation. The paper aims to provide a broad perspective on the design and control of electromagnetic generators for wave energy systems. Full article

We investigated the radio frequency (RF) performance of AlGaN/GaN high electron mobility transistors (HEMTs) fabricated on silicon carbide substrates, featuring two distinct T-shaped gate structures. A comparative analysis between a silicon nitride (SiN_x)-passivated T-gate and a floating T-gate design reveals significant differences in parasitic capacitance and high-frequency behavior. The floating gate structure effectively reduces fringe capacitance, resulting in improved cut-off frequency (f_T) and maximum oscillation frequency (f_max), achieving f_T = 82.7 GHz and f_max = 80.2 GHz, respectively. These enhancements underscore the critical importance of optimizing gate structures to advance GaN-based HEMTs for high-speed and high-power applications. The findings provide valuable insights for the design of future RF and millimeter-wave (mm-wave) devices. Full article