Search Results (198)

The prevailing challenges of fading characteristics and identity crises in historical and cultural districts of small and medium-sized cities have been identified. Traditional analytical methods have been found to be deficient in systematically capturing the unique forms and urban memory of these districts. The present study thus adopts the Shangcheng Historical and Cultural District of Huangling County as a case study, proposing a comprehensive analytical framework that integrates urban memory and multi-dimensional methods such as space syntax, grounded-theory-inspired coding, and urban image analysis. The district is subject to a systematic assessment of its spatial form, structural design, and the mechanisms by which urban memory is conveyed. The proposal sets out targeted renewal strategies for four aspects: paths, edges, nodes and landmarks, and districts. The research findings are as follows: (1) Paths with high integration and connection degrees simultaneously serve as both sacrificial axes and carriers of folk narratives. (2) Edges are composed of the city wall ruins, Loess Plateau landform, and street spaces. The fishbone-like street structure leads to significant differences in the connection degrees of main and secondary roads. (3) Nodes such as Guanyv Temple-Confucian Temple, the South Gate, and the North City Wall Ruins Square have high visual control, while the visual integration and visual control of the Qiaoshan Middle School and Gongsun Road historical nodes are relatively low, and their spatial accessibility is insufficient. (4) Based on the “memory–space” coupling relationship, the district is divided into the Academy Life Area, the Historical and Cultural Core Experience Area, and the Comprehensive Service Area, providing an effective path to alleviate the problem of functional homogenization. The present study proffers a novel perspective on the revitalization mechanisms of historical districts in small and medium-sized cities, encompassing both theoretical integration and practical strategy levels. It further contributes methodological inspirations and localized planning experiences for addressing the cultural disconnection and spatial inactivity problems of historical urban areas on a global scale. Full article

Unmanned airships are highly sensitive to parametric uncertainty, persistent wind disturbances, and sensor noise, all of which compromise reliable path-following. Classical control schemes often suffer from chattering and fail to handle index discontinuities on closed-loop paths due to the lack of mechanisms and cannot simultaneously provide formal guarantees on state constraint satisfaction. We address these challenges by developing a unified, constraint-aware guidance and control framework for path-following in uncertain environments. The architecture integrates an extended state observer (ESO) to estimate and compensate lumped disturbances, a barrier Lyapunov function (BLF) to enforce state constraints on tracking errors, and a super-twisting terminal sliding-mode (ST-TSMC) control law to achieve finite-time convergence with continuous, low-chatter control inputs. A constructive Lyapunov-based synthesis is presented to derive the control law and to prove that all tracking errors remain within prescribed error bounds. At the guidance level, a nonlinear curvature-aware line-of-sight (CALOS) strategy with an index-increment mechanism mitigates jump phenomena at loop-closure and segment-transition points on closed yet discontinuous paths. The overall framework is evaluated against representative baseline methods under combined wind and parametric perturbations. Numerical results indicate improved path-following accuracy, smoother control signals, and strict enforcement of state constraints, yielding a disturbance-resilient path-following solution for the cruise of an unmanned airship. Full article

With the rapid growth of the low-altitude economy, ensuring safe unmanned aerial vehicle (UAV) operations over large public events has become a critical issue for urban air mobility. This study proposes a dynamic risk identification and mitigation framework that integrates UAV inherent risk, aerial traffic density, and ground crowd density into a risk evaluation model. To address the absence of real urban air-route data, a simulated low-altitude network was constructed using ArcGIS, K-means clustering, and Delaunay triangulation, while flight paths were optimized through the ripple-spreading algorithm. Based on this model, a risk-aware control mechanism combining rerouting and hovering strategies was implemented to adaptively respond to varying ground risk levels. A total of 412 UAV missions were simulated over a 6.5 h city marathon scenario, followed by an extended evaluation with 1873 missions to assess scalability. The results show that over 20% of UAVs required detouring or hovering under dynamic risk conditions, leading to a 35–50% reduction in high-risk exposure time while maintaining acceptable operational efficiency. The proposed framework demonstrates good adaptability and scalability for risk-aware UAV operations in complex urban environments. Full article

To improve the continuous chordwise bending performance of morphing wings, this study proposes a rigid–flexible coupled wing rib structure and its control strategy. Initially, the optimal rigid–flexible hybrid configuration was optimized via the mean camber line parameterization and genetic algorithm. For the flexible segment, topology optimization was conducted using the load path method, followed by subspace-based shape–size alternating optimization; bionic “longbow” curved beams and ‘S’-shaped substructures were adopted to enhance deformability. Biomimetic pneumatic muscles were used as actuators, and a fuzzy-adjusted PI sliding mode controller was designed to address the issue that traditional PI sliding mode controllers cannot achieve precise control under non-optimal parameters or when there is a significant difference in deformation targets. Experimental results show that when the flexible rib deflects by 15°, the three-rib wing box achieves a 30° deflection, with stresses within the allowable limit of 7075Al-T6 (540 MPa) and a deformation error of only 7.6%. For the 15° downward bending control, the adjustment time is 6.06 s, the steady-state error is 0.19°, and the overshoot is 1.8%. This study verifies the feasibility of the proposed rigid–flexible coupled structure and fuzzy PI-SMC, providing a technical reference for morphing aircraft. Full article

Aiming at the robust path-following control problem of underwater snake robot (USR) systems subject to modeling uncertainties and time-varying external disturbances, this paper proposes a robust path-following control algorithm based on a fast fixed-time extended state observer (FTESO). First, a fixed-time stability framework with a shorter settling time than existing systems is introduced, and a novel extended state observation system based on the fixed-time stability framework is constructed. Subsequently, by combining the disturbance estimates from the proposed observer with a nonsingular fast fixed-time path-following controller, a robust fixed-time path-following controller is developed. This control strategy incorporates a dynamic event-triggering mechanism, which accomplishes the path-following task while conserving computational resources. The fixed-time convergence of the closed-loop control system is rigorously proved using Lyapunov stability theory. Furthermore, a novel head joint suppression function is designed to reduce the probability of losing the tracking target. Simulation results demonstrate that, compared with conventional control methods, the proposed approach exhibits superior tracking performance and enhanced disturbance rejection capability in complex underwater environments. Full article

Parkinson’s disease (PD) alters both speech and movement, yet most automated assessments still treat these signals separately. We examined whether combining voice with head motion improves discrimination between patients and healthy controls (HC). Synchronous measurements of acoustic and inertial signals were collected using a HoloLens 2 headset. Data were obtained from 165 participants (72 PD/93 HC), following a standardized mixed-reality (MR) protocol. We benchmarked single-modality models against fusion strategies under 5-fold stratified cross-validation. Voice alone was robust (pooled AUC ≈ 0.865), while the inertial channel alone was near chance (AUC ≈ 0.497). Fusion provided a modest but repeatable improvement: gated early-fusion achieved the highest AUC (≈0.875), cross-attention fusion was comparable (≈0.873). Gains were task-dependent. While speech-dominated tasks were already well captured by audio, tasks that embed movement benefited from complementary inertial data. Proposed MR capture proved feasible within a single session and showed that motion acts as a conditional improvement factor rather than a sole predictor. The results outline a practical path to multimodal screening and monitoring for PD, preserving the reliability of acoustic biomarkers while integrating kinematic features when they matter. Full article

This paper addresses the issues of target unreachability and local optima in traditional artificial potential field (APF) methods for UAV swarm path planning by proposing an improved collaborative obstacle avoidance algorithm. By introducing a virtual target position function to reconstruct the repulsive field model, the repulsive force exponentially decays as the UAV approaches the target, effectively resolving the problem where excessive obstacle repulsion prevents UAVs from reaching the goal. Additionally, we design a dynamic virtual target point generation mechanism based on mechanical state detection to automatically create temporary target points when UAVs are trapped in local optima, thereby breaking force equilibrium. For multi-UAV collaboration, intra-formation UAVs are treated as dynamic obstacles, and a 3D repulsive field model is established to avoid local optima in planar scenarios. Combined with a leader–follower control strategy, a hybrid potential field position controller is designed to enable rapid formation reconfiguration post-obstacle avoidance. Simulation results demonstrate that the proposed improved APF method ensures safe obstacle avoidance and formation maintenance for UAV swarms in complex environments, significantly enhancing path planning reliability and effectiveness. Full article

The operational efficiency and precision of boom sprayers, as critical equipment for protecting field crops, are vital to global food security and agricultural sustainability. In precision agriculture systems, achieving uniform pesticide application fundamentally depends on maintaining stable boom posture during operation. However, severe boom vibration not only directly causes issues like missed spraying, double spraying, and pesticide drift but also represents a critical bottleneck constraining its functional realization in cutting-edge applications. Despite its importance, achieving absolute boom stability is a complex task. Its suspension system design faces a fundamental technical contradiction: effectively isolating high-frequency vehicle vibrations caused by ground surfaces while precisely following large-scale, low-frequency slope variations in the field. This paper systematically traces the evolutionary path of self-balancing boom technology in addressing this core contradiction. First, the paper conducts a dynamic analysis of the root causes of boom instability and the mechanism of its detrimental physical effects on spray quality. This serves as a foundation for the subsequent discussion on technical approaches for boom support and balancing systems. The paper also delves into the evolution of sensing technology, from “single-point height measurement” to “point cloud morphology perception,” and provides a detailed analysis of control strategies from classical PID to modern robust control and artificial intelligence methods. Furthermore, this paper explores the deep integration of this technology with precision agriculture applications, such as variable rate application and autonomous navigation. In conclusion, the paper summarizes the main challenges facing current technology and outlines future development trends, aiming to provide a comprehensive reference for research and development in this field. Full article

Continuous-flow separation of magnetically labeled cells according to surface-marker expression levels is increasingly needed to study phenotypic heterogeneity and support downstream assays. Here, we present a microfluidic platform that uses spatially engineered soft magnetic strips (SMS) to sculpt lateral magnetic deflection fields for quantitative, label-guided cell fractionation. Under a uniform bias field, the SMS generates controllable magnetic gradients within the microchannel, producing distinct lateral velocities among EpCAM-labeled tumor cells that carry different Dynabead loads, which indirectly report membrane protein expression. Multi-outlet collection converts these “race-based” trajectory differences into discrete expression-level-resolved fractions. A COMSOL–MATLAB framework and a force-equivalent metric |(H·∇)H| are used to optimize key structural parameters of the magnetic interface, including strip thickness, width, and vertical spacing from the flow channel. Three journey nodes at 1.5, 3, and 9 mm along the flow path define a three-stage cascade that partitions MDA-MB-231, Caco-2, and A549 cells into four EpCAM-related magnetic subgroups: high (H), medium (M), low (L), and near-negative (N). Experiments show that the sorted fractions follow the expected expression trends reported in the literature, while maintaining high cell recovery (>90%) and viability retention of 98.2 ± 1.3%, indicating compatibility with downstream whole-blood assays and culture. Rather than introducing a new biomarker, this work establishes a quantitative magnetic-field design strategy for continuous microfluidic sorting, in which the spatial configuration of soft magnetic elements is exploited to implement expression-level-dependent fractionation in next-generation magneto-fluidic separation systems. Full article

Gel treatments have been widely applied to control water production in oil and gas reservoirs. However, for water shutoff in dense gas reservoirs, most gel-based treatments focus on individual wells rather than the entire reservoir, exhibiting limited treatment depth, poor durability, and inadequate repeatability Notably, formation damage is a primary consideration in treatment design—most dense gas reservoirs have a permeability of less than 1 mD, making them highly susceptible to damage by formation water, let alone viscous polymer gels. Constrained by well completion methods, gelant can only be bullheaded into deep gas wells in most scenarios. Due to the poor gas/water selective plugging capability of conventional gels, the injected gelant tends to enter both gas and water zones, simultaneously plugging fluid flow in both. Although several techniques have been developed to re-establish gas flow paths post-treatment, treating gas-producing zones remains risky when no effective barrier exists between water and gas strata. Additionally, most water/gas selective plugging materials lack sufficient thermal stability under high-temperature and high-salinity (HTHS) gas reservoir conditions, and their injectivity and field feasibility still require further optimization. To address these challenges, treatment design should be optimized using non-selective gel materials, shifting the focus from directly preventing formation water invasion into individual wells to mitigating or slowing water invasion across the entire gas reservoir. This approach can be achieved by placing large-volume gels along major water flow paths via fully watered-out wells located at structurally lower positions. Furthermore, the drainage capacity of these wells can be preserved by displacing the gel slug to the far-wellbore region, thereby dissipating water-driven energy. This study evaluates the viability of placing gels in fully watered-out wells at structurally lower positions in an edge-water drive gas reservoir to slow water invasion into structurally higher production wells interconnected via numerous microfractures and high-permeability streaks. The gel system primarily comprises polyethyleneimine (PEI), a terpolymer, and nanofibers. Key properties of the gel system are as follows: Static gelation time: 6 h; Elastic modulus of fully crosslinked gel: 8.6 Pa; Thermal stability: Stable in formation water at 130 °C for over 3 months; Injectivity: Easily placed in a 219 mD rock matrix with an injection pressure gradient of 0.8 MPa/m at an injection rate of 1 mL/min; and Plugging performance: Excellent sealing effect on microfractures, with a water breakthrough pressure gradient of 2.25 MPa/m in 0.1 mm fractures. During field implementation, cyclic gelant injections combined with over-displacement techniques were employed to push the gel slug deep into the reservoir while maintaining well drainage capacity. The total volumes of injected fluid and gelant were 2865 m³ and 1400 m³, respectively. Production data and tracer test results from adjacent wells confirmed that the water invasion rate was successfully reduced from 59 m/d to 35 m/d. The pilot test results validate that placing gels in fully watered-out wells at structurally lower positions is a viable strategy to protect the production of gas wells at structurally higher positions. Full article

Model Predictive Control (MPC) stands out as a prominent method for achieving optimal control in autonomous driving applications. However, the effectiveness of MPC approaches critically depends on the availability of accurate dynamic models and often necessitates substantial computational overhead for real-time optimization procedures at every iteration. Recently, the research community has been increasingly drawn to the concept of cloud-assisted MPC, which harnesses the capabilities of powerful cloud computing to provide users with on-demand computational resources and data storage services. Within these cloud-assisted MPC frameworks, control signals are merged with a cloud-based MPC, which leverages the substantial processing power of cloud infrastructure to determine optimal control actions using detailed nonlinear models for greater accuracy. Simultaneously, a local MPC runs on simplified linear models constrained by limited on-device computing resources, delivering prompt control responses at the cost of reduced model accuracy. To achieve an effective trade-off between rapid response and model fidelity, this work presents a new model-free deep reinforcement learning structure designed to merge cloud and local MPC outputs. Tests conducted on path-following scenarios show that the introduced method achieves superior control performance compared to existing reinforcement learning baselines and conventional rule-based fusion strategies. Full article

To address the issue of sound absorption valleys in open-cell aluminum foam and enhance mid-to-high frequency (800–6300 Hz) performance, we developed a novel pore-penetrating 316L stainless steel fiber–aluminum foam (PPFCAF) composite using an infiltration method. The formation mechanism of the pore-penetrating fibers, the resultant pore-structure, and the accompanying sound absorption properties were investigated systematically. The PPFCAF was fabricated using 316L stainless steel fiber–NaCl composites created by an evaporation crystallization process, which ensured the full embedding of fibers within the pore-forming agent, resulting in a three-dimensional fiber-pore interpenetrating network after infiltration and desalination. Experimental results demonstrate that the PPFCAF with a porosity of 82.8% and a main pore size of 0.5 mm achieves a sound absorption valley value of 0.861. An average sound absorption coefficient is 0.880 in the target frequency range, representing significant improvements of 9.8% and 9.9%, respectively, higher than that of the conventional infiltration aluminum foam (CIAF). Acoustic impedance reveal that the incorporated fibers improve the impedance matching between the composite material and air, thereby reducing sound reflection. Finite element simulations further elucidate the underlying mechanisms: the pore-penetrating fibers influence the paths followed by air particles and the internal surface area, thereby increasing the interaction between sound waves and the solid framework. A reduction in the main pore size intensifies the interaction between sound waves and pore walls, resulting in a lower overall reflection coefficient and a decreased reflected sound pressure amplitude (0.502 Pa). In terms of energy dissipation, the combined effects of the fibers and refinement increase the specific surface area, thereby strengthening viscous effects (instantaneous sound velocity up to 46.1 m/s) and thermal effects (temperature field increases to 0.735 K). This synergy leads to a notable rise in the total plane wave power dissipation density, reaching 0.0609 W/m³. Our work provides an effective strategy for designing high-performance composite metal foams for noise control applications. Full article

The development of control strategies for autonomous vehicles requires a reliable and cost-effective validation approach. In this context, testbeds enabling repeatable experiments under controlled conditions are gaining relevance. Scaled vehicles have proven to be a valuable alternative to full-scale or simulation-based testing, enabling experimental validation while reducing costs and risks. This work presents a 1:5 scale modular vehicle platform, derived from a commercial Radio-Controlled (RC) vehicle and adapted as experimental testbed for control strategy validation and vehicle dynamics studies. The vehicle features an electric powertrain, operated through a Speedgoat Baseline Real-Time Target Machine (SBRTM). The hardware architecture includes a high-performance Inertial Measurement Unit (IMU) with embedded Global Navigation Satellite System (GNSS). An Extended Kalman Filter (EKF) is implemented to enhance positioning accuracy by fusing inertial and GNSS data, providing reliable estimates of the vehicle position, velocity, and orientation. Two path-following algorithms, i.e., Stanley Controller (SC) and the Linear Quadratic Regulator (LQR), are designed and integrated. Outdoor experimental tests enable the evaluation of tracking accuracy and robustness. The results demonstrate that the proposed scaled testbed constitutes a reliable and flexible platform for benchmarking autonomous vehicle controllers and enabling experimental testing. Full article

This study proposes a fractional-order adaptive super-twisting sliding mode control (FO-ASTSMC) strategy to mitigate the difficulties arising from nonlinearity, uncertain parameters, and substantial external interferences during path-following operations of a hydraulic excavator working device. The developed approach merges a high-order sliding mode differentiator aimed at state observation, a fresh fractional-order sliding manifold that embeds a memory component for bolstering transient performance and equilibrium accuracy, together with an adaptable super-twisting coefficient. This adaptive gain eliminates the requirement for prior awareness of disturbance limits, all the while mitigating chattering effects and bolstering system robustness. Utilizing Lyapunov theory, the finite-time stability of the overall closed-loop framework has been thoroughly demonstrated. For controller verification, joint simulations employing AMESim and Simulink platforms were performed, pitting its efficacy against both terminal sliding mode control (TSMC) and adaptive fuzzy sliding mode control (AFSMC). In nominal scenarios, the FO-ASTSMC method yielded the lowest root mean square error (RMSE) along with maximum error (MAXE) across boom, arm, and bucket articulations, registering mean decreases of $60\%$ in RMSE and $58.2\%$ in MAXE when benchmarked against AFSMC, alongside $41.8\%$ in RMSE and $43.6\%$ in MAXE versus TSMC. Facing sudden variations in loading, it exhibited enhanced robustness, achieving reductions of $64.2\%$ in RMSE and $54.5\%$ in MAXE beyond AFSMC, as well as $39\%$ in RMSE and $36.5\%$ in MAXE in comparison to TSMC. Outcomes from the simulations affirm that the suggested controller exhibits elevated precision, formidable robustness, and good applicability to actuators, thereby highlighting its considerable promise for implementation in actual engineering scenarios. Full article

Cross-seasonal and cross-regional operations make it inevitable for low-temperature cycling of lithium–ion batteries, which accelerates battery aging and induces large inconsistency between batteries in the battery pack. This causes slight overcharging. However, the influence of long low-temperature cycling on the following slight overcharging aging and aging mechanism under multi aging path is not studied clearly. This affects the function of the battery management system (BMS), including state of health (SOH) prediction, state of charge estimation, etc. This work takes 18,650-type batteries as the study objects. Battery aging at low temperature (−10 °C) and slight overcharging (4.4 V) aging after low-temperature cycling are studied in this work. Hybrid pulse power characteristic, incremental capacity analysis, scanning electron microscope, and X-ray diffraction are used to reveal the aging mechanisms. The results indicate that a negative electrode degradation affects the cycle life of batteries more compared to a positive electrode, and the primary aging mechanisms are “dead lithium” and electrolyte decomposition. Compared to low-temperature cycling, slight overcharging is the lower stress factor. Cycling at low stress factor suppresses aging of battery cycled at high stress factor. When the SOH of battery is near 90%, lithium plating growing at low temperature is consumed after slight overcharging cycling. The generated products suppress further lithium plating. When the SOH is near 80%, although lithium plating is consumed, it also grows continuously. Slight overcharging causes more transition metal dissolution and graphite exfoliation. When SOH is near 90%, thermal management strategies should operate to control operation temperature of battery to avoid further low-temperature cycling. The results in this work are important to battery design and battery management system development. Full article