Search Results (79)

We present a dual biosensing strategy integrating Quartz Crystal Microbalance (QCM) and Surface-Enhanced Raman Spectroscopy (SERS) for the quantitative and molecular-specific detection of FKBP12. Silver nanodendritic arrays were electrodeposited onto QCM sensors, optimized for SERS enhancement using Rhodamine 6G, and functionalized with a custom-designed receptor to selectively capture FKBP12. QCM measurements revealed a two-step Langmuir adsorption behavior, enabling sensitive mass quantification with a low limit of detection. Concurrently, in situ SERS analysis on the same sensor provided vibrational fingerprints of FKBP12, resolved through comparative studies of the free protein, surface-bound receptor, and surface-bound receptor–protein complex. Ethanol-induced denaturation confirmed protein-specific peaks, while shifts in receptor vibrational modes—linked to FKBP12 binding—demonstrated dynamic molecular interactions. A ratiometric parameter, derived from key peak intensities, served as a robust, concentration-dependent signature of complex formation. This platform bridges quantitative (QCM) and structural (SERS) biosensing, offering real-time mass tracking and conformational insights. The nanodendritic substrate’s dual functionality, combined with the receptor’s selectivity, advances label-free protein detection for applications in drug diagnostics, with potential adaptability to other target analytes. Full article

Research in marine hydrographic environmental monitoring continues to deepen, necessitating a hardware platform capable of traversing air–water interfaces to collect vertical gradient parameters across oceanographic profiles. This paper proposes a deeply integrated heterogeneous monitoring platform for marine hydrological vertical profiling, addressing the functional limitations of conventional unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs) in subsurface monitoring. By co-designing a hybrid aerial underwater vehicle (HAUV) with cross-domain capabilities and a USV, the system leverages USVs for long-endurance surface operations and HAUVs for high-speed vertical column monitoring. Key innovations include (1) a distributed collaborative architecture enabling “Air–Sea–Air” cyclic operations; (2) dynamic modeling of HAUV-USV interactions incorporating aerodynamic and hydrodynamic coupling; (3) an MPC-based collaborative tracking algorithm for real-time USV pursuit under marine disturbances; and (4) a vision-guided synchronous landing strategy achieving decimeter-level docking accuracy in bad conditions. Simulation experiments validate the system’s efficacy in trajectory tracking and precision landing. This work bridges the critical gap in marine vertical profile monitoring while demonstrating robust cross-domain coordination. Full article

Train–bridge dynamic interaction analysis is critical for the dynamic design of bridges and the safety and comfort assessment of trains. This study introduces a train–bridge dynamic model of a straddle monorail train and a steel–concrete composite track beam to investigate the dynamic performance of the bridge and train. It explores the influence of track irregularities and passenger loads on the dynamic response of train–bridge systems at various traveling speeds. The numerical results indicate that there is no significant resonance between the straddle monorail train and the steel–concrete composite bridge. However, track irregularities and train speed significantly amplify the responses of the train and bridge, including displacement, acceleration, and impact coefficient. Additionally, increased passenger load leads to a substantial rise in the vertical displacement of the bridge while reducing the vibration of the train, thereby improving riding comfort. The findings of this study provide valuable scientific insights and have significant practical applications for the use of steel–concrete composite bridges in straddle monorail systems. Full article

Epoxy Resin System (ERS) steel bridge pavement, which comprises a resin asphalt (RA) base layer and a modified asphalt wearing course, offers cost efficiency and rapid installation. However, the combined effects of traffic loads and environmental conditions pose significant challenges, requiring greater high-temperature stability than conventional pavements. The thermal sensitivity of resin materials and the use of conventional asphalt mixtures may weaken deformation resistance under elevated temperature conditions. This study investigates the thermal field distribution and high-temperature performance of ERS pavements under extreme conditions and explores temperature reduction strategies. A three-dimensional thermal field model developed using finite element analysis software analyzes interactions between the steel box girder and pavement layers. Based on simulation results, wheel tracking and dynamic creep tests confirm the superior performance of the RA05 mixture, with dynamic stability reaching 23,318 cycles/mm at 70 °C and a 2.1-fold improvement in rutting resistance in Stone Mastic Asphalt (SMA)-13 + RA05 composites. Model-driven optimization identifies that enhancing internal airflow within the steel box girder is possible without compromising its structural integrity. The cooling effect is particularly significant when the internal airflow aligns with ambient wind speeds (open-girder configuration). Surface peak temperatures can be reduced by up to 20 °C and high-temperature durations can be shortened by 3–7 h. Full article

This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human–machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% ± 1.4% accuracy, 68.7 ms ± 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (κ = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km/h), while distributed learning across multiple devices maintains consistent accuracy (variance < 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems. Full article

The Dual Active Bridge converter (DABC), known for its bidirectional power transfer capability and high efficiency, plays a crucial role in various applications, particularly in electric vehicles (EVs), where it facilitates energy storage, battery charging, and grid integration. The Dual Active Bridge Converter (DABC), when paired with a high-performance CLLC filter, is well-regarded for its ability to transfer power bidirectionally with high efficiency, making it valuable across a range of energy applications. While these features make the DABC highly efficient, they also complicate controller design due to nonlinear behavior, fast switching, and sensitivity to component variations. We have used a Fractional-order PID (FOPID) controller to benefit from the simple structure of classical PID controllers with lower complexity and improved flexibility because of additional filtering gains adopted in this method. However, for a FOPID controller to operate effectively under real-time conditions, its parameters must adapt continuously to changes in the system. To achieve this adaptability, a Multi-Agent Reinforcement Learning (MARL) approach is adopted, where each gain of the controller is tuned individually using the Deep Deterministic Policy Gradient (DDPG) algorithm. This structure enhances the controller’s ability to respond to external disturbances with greater robustness and adaptability. Meanwhile, finding the best initial gains in the RL structure can decrease the overall efficiency and tracking performance of the controller. To overcome this issue, Grey Wolf Optimization (GWO) algorithm is proposed to identify the most suitable initial gains for each agent, providing faster adaptation and consistent performance during the training process. The complete approach is tested using a Hardware-in-the-Loop (HIL) platform, where results confirm accurate voltage control and resilient dynamic behavior under practical conditions. In addition, the controller’s performance was validated under a battery management scenario where the DAB converter interacts with a nonlinear lithium-ion battery. The controller successfully regulated the State of Charge (SOC) through automated charging and discharging transitions, demonstrating its real-time adaptability for BMS-integrated EV systems. Consequently, the proposed MARL-FOPID controller reported better disturbance-rejection performance in different working cases compared to other conventional methods. Full article

In aging residential neighborhoods, insufficient public spaces and a weakened sense of belonging have led to declining community vitality. Addressing the widespread idleness of boiler room facilities in cold-region contexts, this study integrates GPS tracking, Wi-Fi probe detection, questionnaire surveys, and field observations to develop a three-dimensional “space–time–behavior” evaluation model comprising five core indicators: activity type, spatial range, duration, frequency, and volatility. Unlike prior studies that rely on single data sources or unidimensional metrics, our multi-source approach enhances spatiotemporal resolution, improves the accuracy of subjective perceptions, and enables cross-validation between objective behavioral trajectories and residents’ self-reports, thereby significantly strengthening the comprehensiveness and reliability of community vitality measurement. The results show that the community service center conversion model maximizes spatial efficiency through functional integration, achieving a vitality score of 3.64—substantially higher than those for recreational renovations (3.16) and non-renovated sites (2.67). This model increases space utilization by 2.2-fold, sustains 12 h daily vitality, reduces residents’ activity radii by 38%, and boosts intergenerational interaction frequency by 43%, effectively bridging age group divides. We identify a “functional hybridization–spatial permeability–usage sustainability” mechanism underlying renovation efficacy and recommend the community service center paradigm as a priority strategy. The quantitative decision support framework established here offers empirical guidance for renewing existing spaces in severe climatic environments. Full article

As urban rail transit networks age, understanding the synergistic impacts of multi-defect interactions on tunnel structural safety has become critical for underground infrastructure maintenance. This study investigates defect interaction mechanisms in shield tunnels and cross passages of Beijing Metro Line 8, integrating field monitoring, numerical simulations, and Bayesian network analysis. Long-term field surveys identified spatiotemporal coupling characteristics of four key defects—lining leakage, structural voids, material deterioration, and deformation—while revealing typical defect propagation patterns such as localized leakage at track beds and drainage pipe-induced voids. A 3D fluid–solid coupling numerical model simulated multi-defect interactions, demonstrating that defect clusters in structurally vulnerable zones (e.g., pump rooms) significantly altered pore pressure distribution and intensified displacement, whereas void expansion exacerbated lining uplift and asymmetric ground settlement. Stress concentrations were notably amplified at tunnel–cross passage interfaces. The Bayesian network risk model further validated the dominant roles of defect volume and burial depth in controlling structural safety. Results highlight an inverse correlation between defect severity and structural integrity. Based on these findings, a coordinated maintenance framework combining priority monitoring of high-stress interfaces with targeted grouting treatments is proposed, offering a systematic approach to multi-defect risk management that bridges theoretical models with practical engineering solutions. Full article

To address the seismic vulnerability of high-speed railway bridges (HSRBs) in seismically active regions, this study proposes a hierarchical curved steel damper (CSD) designed to mitigate excessive girder displacements induced by conventional isolation devices. The CSD integrates U-shaped and hollow diamond-shaped steel plates to achieve stable energy dissipation through coupled bending deformation. A finite element model is developed, and its hysteretic behavior is confirmed, with an energy dissipation coefficient of 1.82 and an equivalent damping ratio of 12.7%. An integrated high-speed railway track–bridge-CSD spatial coupling model is developed in OpenSees, which incorporates nonlinear springs for interlayer track interactions. Nonlinear time–history analyses under 40 spectrum-matched ground motions reveal that the CSD reduces transverse girder displacements by 73.7–79.2% and attenuates track slab acceleration peaks by 52.4% compared with uncontrolled cases. However, it increases the maximum bending moment at pier bases by up to 18.3%, necessitating supplemental energy-dissipating components for balanced force redistribution. This work provides a theoretical foundation and practical methodology for seismic response control and retrofitting of the HSRB in high-intensity seismic regions. Full article

With the rapid deployment of 5G networks and the increasing demand for high-reliability services across industries such as smart grids, healthcare, and unmanned aerial vehicle (UAV) trajectory tracking, the need for robust and efficient transport network protection mechanisms has become critical. Traditional protection schemes, such as optical layer and electrical layer linear protection, face significant challenges in handling multi-point failures and achieving fault isolation in cross domain scenarios. To address these limitations, this paper proposes a novel cross-domain protection mechanism based on segmented Dual-Node Interconnection (DNI). By constructing a DNI topology between adjacent domains and leveraging a three-point bridge mechanism, the proposed solution enables efficient fault isolation and rapid service recovery in the event of multi-point failures. The proposed protection mechanism also introduces periodic status interaction messages between nodes, ensuring coordinated protection switching across domains. We conducted the first field trial to validate the proposed protection mechanism in a multi-domain, multi-vendor network spanning over 2700 km. The results demonstrate that the proposed protection scheme achieves protection switching times of less than 50 ms under various fault scenarios, including intra-domain and inter-domain failures, significantly outperforming traditional linear protection methods. This work represents a significant advancement in cross domain protection, offering a robust solution for enhancing the reliability of long-distance transport networks. Full article

Communication barriers pose significant challenges for the Deaf and Hard-of-Hearing (DHH) community, limiting their access to essential services, social interactions, and professional opportunities. To bridge this gap, assistive technologies leveraging artificial intelligence (AI) and deep learning have gained prominence. This study presents a real-time American Sign Language (ASL) interpretation system that integrates deep learning with keypoint tracking to enhance accessibility and foster inclusivity. By combining the YOLOv11 model for gesture recognition with MediaPipe for precise hand tracking, the system achieves high accuracy in identifying ASL alphabet letters in real time. The proposed approach addresses challenges such as gesture ambiguity, environmental variations, and computational efficiency. Additionally, this system enables users to spell out names and locations, further improving its practical applications. Experimental results demonstrate that the model attains a mean Average Precision (mAP@0.5) of 98.2%, with an inference speed optimized for real-world deployment. This research underscores the critical role of AI-driven assistive technologies in empowering the DHH community by enabling seamless communication and interaction. Full article

This research introduces a virtual reality (VR) training system for improving human–robot collaboration (HRC) in industrial disassembly tasks, particularly for e-waste recycling. Conventional training approaches frequently fail to provide sufficient adaptability, immediate feedback, or scalable solutions for complex industrial workflows. The implementation leverages Quest Pro’s body-tracking capabilities to enable ergonomic, immersive interactions with planned eye-tracking integration for improved interactivity and accuracy. The Niryo One robot aids users in hands-on disassembly while generating synthetic data to refine robot motion planning models. A Robot Operating System (ROS) bridge enables the seamless simulation and control of various robotic platforms using Unified Robotics Description Format (URDF) files, bridging virtual and physical training environments. A Long Short-Term Memory (LSTM) model predicts user interactions and robotic motions, optimizing trajectory planning and minimizing errors. Monte Carlo dropout-based uncertainty estimation enhances prediction reliability, ensuring adaptability to dynamic user behavior. Initial technical validation demonstrates the platform’s potential, with preliminary testing showing promising results in task execution efficiency and human–robot motion alignment, though comprehensive user studies remain for future work. Limitations include the lack of multi-user scenarios, potential tracking inaccuracies, and the need for further real-world validation. This system establishes a sandbox training framework for HRC in disassembly, leveraging VR and AI-driven feedback to improve skill acquisition, task efficiency, and training scalability across industrial applications. Full article

This study investigated the impact coefficient of a large-span steel truss arch railroad bridge under moving train loads, with the Nanning Three Banks Yongjiang Special Bridge serving as the case study. Field tests were conducted to measure the bridge’s self-vibration characteristics, dynamic deflection, and strain. A coupled vehicle–bridge vibration model was developed using the finite element software ABAQUS 2022 for the bridge and multi-body dynamics software SIMPACK 2022 for the CRH2 train. The two models were integrated to simulate the dynamic interaction between the train and bridge under different speeds and single-/double-track operations. The results demonstrate that the joint simulation of SIMPACK and ABAQUS was an effective method for the vehicle–bridge coupled vibration analysis. The key findings include the following: the deflection and stress impact coefficients increased with the train speed, where the main span exhibited larger deflection coefficients than the side span. The stress impact coefficients varied significantly across different bridge components, where the lower chord of the side span and the ties of the main span showed the highest values. While there was no substantial difference in the deflection impact coefficients between the single- and double-track operations, the stress impact coefficients showed deviations, particularly in the side span’s lower chord and ties, highlighting their sensitivity to vehicle-induced deflection. This study concluded that the bridge’s deflection impact coefficient met design specifications, but the stress impact coefficient exceeded the specified values, suggesting that stress amplification should be carefully considered in the design of similar bridges to ensure operational safety. Full article

Based on the dynamic receptance method, a vehicle–track–bridge interaction model was developed to calculate the wheel–rail interaction forces and the forces transmitted to the bridge in an elevated urban rail transit system. A prediction model integrating the finite element method–boundary element method (FEM-BEM) and the statistical energy analysis (SEA) method was established to obtain the noise from the main girder, track slab, and wheel–rail system for elevated urban rail transit. The calculated results agree well with the measured data. Thereafter, the noise radiation characteristics of a single source and the total noise of elevated urban rail transit systems with resilient fasteners, trapezoidal sleepers, and steel spring floating slabs were investigated. The results demonstrate that the noise prediction model for elevated urban rail transit that was developed in this study is effective. The diversity of track forms altered the noise radiation field of elevated urban rail transit systems significantly. Compared to monolithic track beds, where the fastener stiffness is assumed to be 60 × 10⁶ N/m (MTB_60), steel spring floating slab tracks (FSTs), trapezoidal sleeper tracks (TSTs), and resilient fasteners with a stiffness of 40 × 10⁶ N/m (MTB_40) and 20 × 10⁶ N/m (MTB_20) can reduce bridge-borne noise by 24.6 dB, 8.8 dB, 2.1 dB, and 4.2 dB, respectively. These vibration-mitigating tracks can decrease the radiated noise from the track slab by −0.7 dB, −0.6 dB, 2.5 dB, and 2.6 dB, but increase wheel–rail noise by 0.4 dB, 0.8 dB, 1.3 dB, and 2.4 dB, respectively. The noise emanating from the main girder and the track slab was dominant in the linear weighting of the total noise of the elevated section with MTBs. For the TST and FST, the radiated noise from the track slab contributed most to the total noise. Full article

In many tasks of railway vibration, the structure, that is, the track, a bridge, and a nearby building and its floors, is coupled to the soil, and the soil–structure interaction and the damping by the soil should be included in the analysis to obtain realistic resonance frequencies and amplitudes. The stiffness and damping of a variety of foundations is calculated by an indirect boundary element method which uses fundamental solutions, is meshless, uses collocation points on the boundary, and solves the singularity by an appropriate averaging over a part of the surface. The boundary element method is coupled with the finite element method in the case of flexible foundations such as beams, plates, piles, and railway tracks. The results, the frequency-dependent stiffness and damping of single and groups of rigid foundations on homogeneous and layered soil and the amplitude and phase of the dynamic compliance of flexible foundations, show that the simple constant stiffness and damping values of a rigid footing on homogeneous soil are often misleading and do not represent well the reality. The damping may be higher in some special cases, but, in most cases, the damping is lower than expected from the simple theory. Some applications and measurements demonstrate the importance of the correct damping by the soil. Full article