Search Results (925)

This study proposes an attitude stabilization method based on an improved adaptive Kalman filter (AKF). The aim is to address attitude fluctuations and rollover risks in rail-based agricultural transport platforms on hilly terrain caused by slope changes, load shifts and vibrations. A dynamic model integrating the load distribution and center-of-mass migration was established, and an adaptive noise covariance mechanism was used to precisely estimate the roll and pitch angles in real time. A dual-channel proportional–integral–derivative controller was designed for automatic leveling, and a rollover risk index (RRI) was adopted for safety evaluation. Simulations revealed the ability of the improved AKF to decrease the roll estimation (RMSE) from 1.2684° to 0.8670° and the stabilization time from 0.6250 to 0.3830 s for the roll and from 0.6930 to 0.4110 s for the pitch. Under 10–30° slope disturbances, the average RRI decreased from 0.1861 to 0.1506. Field tests further demonstrated decreases in the peak roll and pitch angles from 4.8° and 4.1° to 3.1° and 2.7°, respectively, and a decrease in the average RRI from 0.203 to 0.169. The improvements in estimation accuracy, leveling performance, and operational safety under complex disturbances indicate the strong engineering potential of the proposed method. Full article

Small satellites provide cost-effective platforms for environmental monitoring. Open-source commercial off-the-shelf (COTS) hyperspectral payloads, such as those launched with HYPSO-1 and -2, have a ground sampling distance (GSD) of 100 m. However, detecting smaller features, such as water quality in lakes, requires a GSD below 10 m and a high signal-to-noise ratio. Terrestrial COTS Schmidt–Cassegrain telescopes lack launch-load stiffness and in-orbit refocus capability. This study presents a deployable modified COTS (MCOTS) Schmidt–Cassegrain telescope that uses the original optical COTS components, a 3D-printed high-performance polymer (HPP) structure, and a dual-lead-screw deployment and focusing mechanism. The telescope has a stowed length of 280 mm and deploys to an additional 110 mm, making integration into a 16U platform with a payload length of 290 mm feasible. The modified structure is evaluated using shock and sine-sweep vibration testing, with collimation and focus verified before and after testing. Collimation remained concentric within measurement uncertainty. Complementary random-vibration finite-element simulations predicted a $3\sigma$ von Mises stress of 26.5 MPa, yielding a safety factor of 2.8. The results demonstrate a feasible pathway for adapting COTS telescopes toward space-grade COTS (SCOTS) payloads, bridging the gap between rapid production, cost efficiency, and performance for small Earth observation missions. Full article

Flow-induced vibration of high-speed pantographs becomes increasingly important as train speed approaches and exceeds 400 km/h. This paper develops an efficient computational framework that couples component-resolved unsteady aerodynamic loads from improved delayed detached eddy simulation (IDDES) with a dynamic-stiffness-method (DSM) flexible model of a high-speed pantograph. Two operating orientations, namely, knuckle-downstream and knuckle-upstream, are compared at 400 km/h, and the more unfavorable knuckle-upstream orientation is further investigated over 400 km/h to 600 km/h. The DSM model contains 49 beam elements and 42 nodes and shows good agreement with a refined three-dimensional solid-element finite element model in the low-order frequency range. For a 2 s transient analysis, the proposed model predicts the panhead displacement response with peak errors below 5% relative to the finite element model while reducing the computational time from 53 min 22 s to 35 s on the same platform. The results show that vertical vibration dominates the structural response, with the panhead peak vertical displacement reaching about 20 mm in the studied 400 km/h open-line case. Frequency-domain inspection of the panhead aerodynamic lift and vertical displacement shows that broadband aerodynamic excitation mainly activates the low-order structural modes, with a low-frequency aerodynamic component around 3 Hz to 4 Hz and additional energy mainly over the 20 Hz to 30 Hz range. The knuckle-upstream orientation increases the standard deviation of the equivalent contact-force response by 46% compared with the knuckle-downstream orientation at 400 km/h. For the knuckle-upstream orientation, increasing speed from 400 km/h to 600 km/h raises the standard deviation by 189%. The proposed framework provides an efficient tool for rapid comparative evaluation of pantograph flow-induced vibration under multiple operating conditions. Full article

This paper presents a fully automated platform for real-time monitoring of fatigue-induced microstructural changes in metallic materials, using Rayleigh surface waves and Laser Doppler Vibrometry (LDV). The system integrates ultrasonic excitation, non-contact optical sensing, and high-speed signal processing in a unified LabVIEW environment. Rayleigh waves are generated via a contact transducer, while LDV captures surface vibrations with sub-nanometric velocity resolution, ensuring repeatability and eliminating coupling variability. The software automates synchronization, deterministic data acquisition, filtering, FFT analysis, and extraction of nonlinear coefficients (β₂, β₃) at high execution rates without the need for post-processing. Experimental validation under cyclic loading revealed a clear sensitivity hierarchy: the Rayleigh wave velocity remained invariant, the acoustic attenuation responded gradually, while the nonlinear parameters exhibited the earliest and steepest response to fatigue damage, confirming their superiority as early-stage indicators. The system offers low-latency timing, long-term stability, and modular design, establishing a robust data-streaming foundation that can support future integration with digital twin frameworks and machine learning models. Furthermore, the acoustic findings were successfully cross-validated using Infrared Thermography, which confirmed the critical damage transition phase. This work bridges nonlinear acoustics and software automation, providing a scalable diagnostic solution for predictive maintenance within structural health monitoring systems. Full article

Natural disasters such as floods and earthquakes cause severe physical, social, and economic losses, highlighting the critical need for timely and reliable early warning systems. Conventional water level and structural health monitoring technologies are often costly, limiting deployment to high-priority infrastructure only. This paper presents the development and validation of two low-cost Internet of Things (IoT) systems for multi-hazard disaster monitoring and early warning, explicitly supporting UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities) by enabling equitable monitoring of rural or minor bridges. The proposed system achieves a significant cost reduction (approximately $300 compared to conventional systems typically exceeding $5000), highlighting its potential for scalable and sustainable deployment. The first system integrates a Raspberry Pi, Pi Camera, Lidar Lite V3, and ADXL355 accelerometer to simultaneously capture floodwater images, measure water levels, and record bridge vibrations, with distance measurements recorded at user-defined intervals and vibration data sampled up to 100 Hz. Laboratory repeatability and uncertainty analyses of the Lidar Lite V3 indicate a root mean square error of ~2.4 cm over a 0–25 cm range, demonstrating stable performance for flood monitoring and sufficient accuracy for early warning applications using low-cost sensing systems. The ADXL355 accelerometer is validated through harmonic excitation tests (0.1–2 Hz) and real earthquake recordings, confirming its suitability for low-frequency structural response monitoring. The second system combines a Raspberry Pi, an HX711 amplifier, and a CDP25 displacement transducer to measure bridge-bearing displacements up to 25 cm, with data acquisition at sampling rates of up to 80 Hz, with laboratory tests demonstrating consistent and repeatable measurements during both loading and unloading cycles. The IoT framework is resilient, incorporating solar power and local data storage to ensure operation during power or network outages. Unlike prior studies focusing on individual sensors, this work delivers a fully integrated multi-sensor platform with formalized early warning logic based on predefined thresholds. The results demonstrate the feasibility of scalable, real-time, low-cost monitoring for disaster risk reduction and infrastructure resilience, providing a sustainable solution for community-scale early warning applications. Full article

Mechanized harvesting in the industrial tomato sector is currently bottlenecked by excessive mechanical injuries and elevated levels of foreign materials generated during electro-mechanical combine harvesting operations. To combat these limitations, this comprehensive review explores recent breakthroughs in harvester-mounted smart grading systems engineered specifically for complex, open-field conditions. Rather than relying solely on conventional optical inspection, the study examines the transition toward advanced, heterogeneous edge-computing frameworks—incorporating FPGAs and embedded GPUs—deployed within electro-mechanical harvesting platforms. This architectural evolution plays a crucial role in mitigating unpredictable processing delays caused by intense operational vibrations, although achieving absolute real-time stability under extreme field conditions remains an ongoing challenge. To minimize bruising and physical deterioration, our analysis synthesizes findings from multi-scale explicit dynamic finite element simulations, unpacking the underlying microstructural failure modes of the crop. We illustrate how regulating applied forces via soft robotic effectors can help approach a ‘damage-free’ handling threshold, though empirical results vary depending on fruit maturity and dynamic operational speeds. Furthermore, coupling multi-modal sensor fusion with Convolutional Neural Networks (CNNs) shows promising potential for non-destructive internal property evaluation under the vibration, dust, and throughput constraints of electro-mechanical harvesters, pending broader validation across diverse field datasets. Ultimately, by projecting future trends in onboard electro-mechanical harvester separation and advocating for a closer synergy between agronomic practices and machine engineering, this paper delivers a comprehensive blueprint for building next-generation, highly resilient, and gentle sorting machinery. Full article

Deploying unmanned aerial vehicles (UAVs) cooperatively with legged robots for disaster response and inspection requires autonomous docking on miniature walking platforms. This study addresses the problem of landing a foldable quadrotor onto the back of a trotting wheel-legged robot ($300\times 180$ mm) and subsequently taking off while carrying it as a payload. Four tightly coupled challenges distinguish this task from conventional mobile-platform landing: (i) an extremely small landing surface, (ii) gait-induced periodic vibrations at $2.5$ Hz, (iii) continuous platform translation at $0.3$–$0.8$ m/s, and (iv) surface docking that requires simultaneous position and attitude matching rather than mere point tracking. The proposed framework comprises four components: (1) a novel single-servo crank-rocker folding mechanism that reduces the folded body footprint by 48.5% and the maximum linear dimension from 590 mm to 309 mm (↓47.6%) compared with the prior dual-servo design; (2) a staged Continuous Fast Nonsingular Terminal Sliding Mode (CFNTSM) controller combined with a Gait-Frequency-Aware Finite-time Extended Observer (GFA-FEO); (3) a Feature-wise Linear Modulation Soft Actor-Critic (FiLM-SAC) residual reinforcement-learning policy conditioned on physical states and mission phase, with an adaptive trust weight $\lambda \left(t\right)$; and (4) a payload-adaptive takeoff strategy with parameter hot-switching to handle the twofold mass increase. Extensive Monte Carlo simulations and ablation studies across three experiment groups demonstrate that the proposed hierarchical framework achieves sub-centimetre (<10 mm) position accuracy and <3° attitude matching on a walking platform. Quantitatively, the full method reduces docking RMSE by 42% relative to the model-based CFNTSM + GFA-FEO controller without residual RL (4.2 vs. 7.2 mm) and reduces post-lock takeoff RMSE by 63% through FEO hot-switching (16.2 vs. 44.2 mm). Full article

This article presents an intelligent differential capacitive bioelectronic sensing system that provides an experimental foundation for future AI-assisted reliable microfluidic reagent delivery in automated pathology. The proposed platform integrates a slot-type microfluidic chamber, a differential slot-line capacitive sensor, embedded readout and signal-conditioning electronics, and a supervisory state assessment concept within a unified architecture. Its purpose is to support stable microliter-scale reagent exchange together with non-invasive process observability in automated staining workflows. The experimental study included flow calibration, analysis of feed direction and chamber tilt angle, preliminary vibration-assisted bubble mobilization, and evaluation of the sensing subsystem. The results showed that reliable operation is achieved only within a practically admissible regime in which fluidic stability and sensing informativeness overlap. In the investigated setup, upper-feed delivery and low chamber tilt angles provided the most favorable filling conditions, while the differential capacitive subsystem enabled stable detection of liquid-state changes in narrow microtubes. The reported results establish a foundation for future AI-assisted transport-state recognition and adaptive monitoring in automated pathology platforms. Full article

As is well known, vibration, especially ultra-low-frequency vibration, is harmful to machinery’s accuracy and service life and even human health. This paper experimentally validates vibration isolation technology for low-frequency applications based on quasi-zero stiffness (QZS) properties. Firstly, a platform for isolating low-frequency vibration, referred to as LFVIP, is introduced, featuring a quasi-zero stiffness characteristic. Then, the dynamic stiffness of this platform is analyzed and established. Based on this analytical model, a solution for designing the platform to obtain the desired stiffness in the equilibrium state is suggested. Secondly, an experimental setup is established to verify the isolation performance of the platform under base displacement excitation. In addition, the isolation effectiveness of the LFVIP is compared with that of its linear counterpart (LC). The experimental results indicate that the LFVIP provides the starting isolation for effective isolation at approximately 2 Hz, while that of LC is around 6 Hz. Moreover, the vibration attenuation of the LFVIP is greater than that of the LC. Vibration isolation technology based on quasi-zero stiffness is superior to the LC, particularly in the low-frequency region. This work offers useful insights for the design of vibration isolators, suspension systems, and related applications, particularly by demonstrating how the superior vibration attenuation of the LFVIP can be leveraged to improve the performance of these systems. Full article

This paper presents a low-cost Internet-of-Things (IoT) telemetry and alerting system for monitoring and protecting sea turtle nests. The proposed platform integrates temperature, humidity, vibration, ultrasonic proximity, and ambient light sensors into an autonomous sensing node based on the ESP8266 microcontroller. Measurements are transmitted wirelessly to a cloud backend for real-time visualization and rule-based alert generation. The system is designed to support continuous nest-level monitoring and rapid response to environmental and anthropogenic threats such as overheating, artificial light exposure during hatching, and physical disturbance. In contrast to approaches that require extensive historical datasets or machine-learning models, the proposed solution employs transparent threshold-based rules that provide reliable operation without training data. The platform emphasizes low cost, ease of deployment, and scalability, making it suitable for large-scale conservation deployments across multiple nesting sites. It provides conservation practitioners with actionable situational awareness that complements existing field-based monitoring and protection strategies. Full article

Planetary bearings are critical components in double planetary gear trains. The influence of carrier rotation direction on bearing dynamic behavior remains insufficiently understood, which hinders accurate reliability assessment and optimal design. To investigate this issue, a dynamic model of the double planetary gear train is developed. The model captures the coupled interactions and motion characteristics of both gears and bearings. Furthermore, an experimental platform is constructed to validate the accuracy of the proposed model. A comparative analysis is conducted to examine the dynamic loads and vibration responses of the planetary bearings under forward and reverse carrier rotations. The results show that reverse rotation significantly intensifies collision forces, particularly under low-speed and high-torque conditions, where the increases for inner and outer bearings reach 38.34% and 31.25%, respectively. In terms of contact forces, the inner bearing exhibits higher loads under reverse rotation, whereas the outer bearing carries greater loads under forward rotation. Vibration response analysis reveals that the carrier rotation direction has a limited effect on the vibration of the inner bearing, but significantly amplifies that of the outer bearing. Under reverse rotation, the acceleration amplitudes of the outer cage in the x- and y-directions increase by 96.20% and 95.74%, respectively, markedly exceeding the approximate 26% increase observed for the inner bearing. This study provides new insights into the asymmetric tribological behavior of planetary bearings under bidirectional rotation. These findings provide theoretical guidance for the design and optimization of planetary bearings in double planetary gear trains. Full article

The friction-induced stick-slip vibration (FISSV) generated by intense friction between the brake disc and brake pads of high-speed trains is a critical issue affecting braking stability, the service life of foundational braking components, and ride comfort. The floating friction block structure, which effectively regulates interfacial contact characteristics through the elastic deformation of disc springs, thereby improving tribological behavior, represents an effective approach for mitigating FISSV. However, the topic of how to design the floating structure of the friction block to produce the best suppression impact on FISSV emerges, using the choice of disc spring material as an example. Thus, the purpose of this study is to look at how disc spring material affects stick-slip vibration (SSV) at the high-speed train floating brake interface. Four typical disc spring materials—304 stainless steel, Mubea-specific spring steel, 50CrVA high-alloy spring steel, and 60Si₂MnA silicon-manganese spring steel—were selected. Through braking tribological tests and explicit dynamics-wear coupling simulations, the effects of material differences on interfacial friction-wear characteristics and SSV behavior were systematically studied. The findings show that the stiffness of the disc spring material greatly influences the dynamic responsiveness of the system and the contact pressure distribution at the braking interface, elasticity, and damping characteristics. 60Si₂MnA spring steel, owing to its excellent elastic recovery and load equalization capability, promoted the formation of uniformly dispersed medium-to-small contact platforms on the interface, resulting in the mildest wear. Concurrently, its system vibration energy exhibited a more dispersed distribution in the frequency domain, with low SSV intensity and weak nonlinear behavior, demonstrating the best comprehensive performance. Materials with poorer compatibility, such as 304 stainless steel, tended to cause localized stress concentration, exacerbating wear and intensifying severe high-frequency SSV. The influence mechanism of disc spring material at the interface is shown by this work, providing an important basis for material optimization and vibration suppression design in floating brake pad structures. Full article

N-Methyl-D-aspartate (NMDA) receptors are ligand- and voltage-gated ion channels essential for synaptic plasticity, learning, and memory. The GluN2B subunit, highly expressed in the forebrain and spinal cord, is implicated in multiple neurological and psychiatric disorders, making it an attractive target for positron emission tomography (PET) imaging. However, the development of selective GluN2B PET radioligands remains challenging. Here, we describe the design, synthesis, and evaluation of eighteen 3-alkylaryl derivatives of 7-methoxy-2,3,4,5-tetrahydro-1H-benzo[d]azepin-1-ol, including enantiomerically resolved compounds, as candidate PET radioligands. Structure–activity relationship studies show that binding affinity is largely insensitive to electronic and steric variation at the terminal aryl group but strongly dependent on alkyl linker length, with a four-carbon chain providing optimal affinity. Binding affinity does not correlate with calculated lipophilicity, suggesting hydrophobicity is not the primary determinant of receptor interaction. Absolute configuration was established using vibrational circular dichroism and infrared spectroscopy, and docking studies provided insight into enantiomer-specific binding modes. Two ligands, L3 and L6, and their enantiomers exhibited high GluN2B affinity, favorable physicochemical properties, and suitability for carbon-11 labeling. Separate PET imaging studies confirmed strong and specific brain binding of the radiolabeled compounds. These findings establish this scaffold as a promising platform for GluN2B PET ligand development. Full article

Therapeutic interventions within the urinary system are often limited by the complex and tortuous anatomy of the renal pelvis and ureters, restricting access to deep regions and increasing the risk of mucosal trauma. In this study, we present a multifunctional, magnetically controlled ferrofluid droplet robotic platform engineered for high deformability and precision navigation. A custom electromagnetic actuation system was developed and optimized via COMSOL Multiphysics (version 6.3, COMSOL Inc., Stockholm, Sweden) simulations to generate programmable magnetic fields. Experimental validation in both simplified environments and anatomically realistic 3D-printed urinary tract models demonstrated the droplets’ capacity for controlled locomotion, reversible deformation, and traversing constrictions significantly smaller than their resting diameter. The droplets’ locomotion and extreme deformability are governed by the dynamic balance between the applied magnetic gradient forces, the restoring interfacial tension of the ferrofluid, and the fluidic viscous drag. Quantitatively, the droplets achieved robust translational velocities up to 260 mm/s under single-coil actuation (51 mT, 20 Hz) and 108 mm/s under a more stable dual-coil configuration (51 mT, 8.3 Hz). Furthermore, two clinically relevant functionalities were successfully executed: rapid vibration-induced release of encapsulated dye for targeted drug delivery, and the precise mechanical capture and transport of artificial kidney stones. These results establish a highly versatile platform for minimally invasive urological procedures, highlighting the immense potential of soft magnetic microrobotics for integrated therapeutic applications. Full article

To monitor online the operational condition and quality of a desktop fused deposition modeling (FDM) printer, the dynamics of vibro-acoustics must be accurately understood. In this paper, an electromechanical coupling (EMT) framework is established to relate the dynamics of stepper actuation, the transmission chain, and machine motion, deriving a steady-state frequency–velocity mapping for steady or near steady printing segments. The mapping is evaluated by numerical calculation to obtain a theoretical drive frequency for different toolpath directions and commanded printing velocities. Validation is performed on the experiment platform I. Drive-side vibration is measured by an accelerometer mounted on the x-axis beam near the motor end. An acoustic channel is recorded as an auxiliary qualitative cross-check rather than for quantitative error evaluation. For steady printing segments, the dominant frequency in drive-side vibration is compared with the theoretical drive frequency. In the tested steady segments and toolpath directions, the relative error remained below 3%. In a further case study, the G-code is modified to introduce two constant printing velocity segments (40 mm/s and 80 mm/s) within the same continuous record, enabling a direct comparison of dominant frequencies between two steady segments. The results show that, under open-loop stepper drive and within the steady/near steady scope adopted here, a drive-related dominant frequency can be observed stably in the x-axis beam vibration response and matches the theoretical drive frequency. When the commanded constant printing velocity is doubled, the dominant frequency in drive-side vibration in the corresponding steady segment changes by approximately a proportional factor. This study provides a verifiable drive referenced frequency–velocity mapping for steady segments under the tested configuration and a traceable frequency reference for steady segment comparisons within the same print record in subsequent case studies. Full article