Search Results (195)

Virtual reality (VR) can enrich neuropsychological testing, yet the ergonomic trade-offs of its input modes remain under-examined. Seventy-seven healthy volunteers—young (19–29 y) and middle-aged (35–56 y)—completed a VR Trail Making Test with three pointing methods: eye-tracking, head-gaze, and a six-degree-of-freedom hand controller. Completion time, spatial accuracy, and error counts for the simple (Trail A) and alternating (Trail B) sequences were analysed in 3 × 2 × 2 mixed-model ANOVAs; post-trial scales captured usability (SUS), user experience (UEQ-S), and acceptability. Age dominated behaviour: younger adults were reliably faster, more precise, and less error-prone. Against this backdrop, input modality mattered. Eye-tracking yielded the best spatial accuracy and shortened Trail A time relative to manual control; head-gaze matched eye-tracking on Trail A speed and became the quickest, least error-prone option on Trail B. Controllers lagged on every metric. Subjective ratings were high across the board, with only a small usability dip in middle-aged low-gamers. Overall, gaze-based ray-casting clearly outperformed manual pointing, but optimal choice depended on task demands: eye-tracking maximised spatial precision, whereas head-gaze offered calibration-free enhanced speed and error-avoidance under heavier cognitive load. TMT-VR appears to be accurate, engaging, and ergonomically adaptable assessment, yet it requires age-specific–stratified norms. Full article

The automated detection and classification of immature macauba (Acrocomia aculeata) fruits is critical for improving post-harvest processing and quality control. In this study, we present a comparative evaluation of two state-of-the-art YOLO architectures, YOLOv11x and YOLOv12x, trained on the newly constructed VIC01 dataset comprising 1600 annotated images captured under both background-free and natural background conditions. Both models were implemented in PyTorch and trained until the convergence of box regression, classification, and distribution-focal losses. Under an IoU (intersection over union) threshold of 0.50, YOLOv11x and YOLOv12x achieved an identical mean average precision (mAP₅₀) of 0.995 with perfect precision and recall or TPR (true positive rate). Averaged over IoU thresholds from 0.50 to 0.95, YOLOv11x demonstrated superior spatial localization performance (mAP_50–95 = 0.973), while YOLOv12x exhibited robust performance in complex background scenarios, achieving a competitive mAP_50–95. Inference throughput averaged 3.9 ms per image for YOLOv11x and 6.7 ms for YOLOv12x, highlighting a trade-off between speed and architectural complexity. Fused model representations revealed optimized layer fusion and reduced computational overhead (GFLOPs), facilitating efficient deployment. Confusion-matrix analyses confirmed YOLOv11x’s ability to reject background clutter more effectively than YOLOv12x, whereas precision–recall and F1-score curves indicated both models maintain near-perfect detection balance across thresholds. The public release of the VIC01 dataset and trained weights ensures reproducibility and supports future research. Our results underscore the importance of selecting architectures based on application-specific requirements, balancing detection accuracy, background discrimination, and computational constraints. Future work will extend this framework to additional maturation stages, sensor fusion modalities, and lightweight edge-deployment variants. By facilitating precise immature fruit identification, this work contributes to sustainable production and value addition in macauba processing. Full article

The electromechanical composite transmission technology for tracked vehicles demonstrates excellent performance in energy efficiency, mobility, and ride comfort. However, due to frequent operation under harsh conditions, the components of the electric drive system, such as drive motors, are prone to failures. This paper proposes three fault-tolerant control methods for three typical fault scenarios of the electromechanical composite transmission system (ECTS) to ensure the normal operation of tracked vehicles. Firstly, an ECTS and the electromechanical coupling dynamics model of the tracked vehicle are established. Moreover, a double-layer anti-windup PID control for motors and an instantaneous optimal control strategy for the engine are proposed in the fault-free case. Secondly, an anti-windup PID control law for motors and an engine control strategy considering the state of charge (SOC) and driving demands are developed in the case of single-side drive motor failure. Thirdly, a B4 clutch control strategy during starting and a steering brake control strategy are proposed in the case of electric drive system failure. Finally, in the straight-driving condition of the tracked vehicle, the throttle opening is set as 0.6, and the motor failure is triggered at 15 s during the acceleration process. Numerical simulations verify the fault-tolerant control strategies’ feasibility, using the tracked vehicle’s maximum speed and acceleration at 30 s as indicators for dynamic performance evaluation. The simulation results show that under single-motor fault, its straight-line driving power drops by 33.37%; with electric drive failure, the drop reaches 43.86%. The vehicle can still maintain normal straight-line driving and steering under fault conditions. Full article

The increase in garbage generated in modern societies demands the implementation of a more sustainable model as well as new methods for efficient waste management. This article describes the development and implementation of a prototype of a smart bin that automatically sorts waste using a multi-agent system and blockchain integration. The proposed system has sensors that identify the type of waste (organic, plastic, paper, etc.) and uses collaborative intelligent agents to make instant sorting decisions. Blockchain has been implemented as a technology for the immutable and transparent control of waste registration, favoring traceability during the classification process, providing sustainability to the process, and making the audit of data in smart urban environments transparent. For the computer vision algorithm, three versions of YOLO (YOLOv8, YOLOv11, and YOLOv12) were used and evaluated with respect to their performance in automatic detection and classification of waste. The YOLOv12 version was selected due to its overall performance, which is superior to others with mAP@50 values of 86.2%, an overall accuracy of 84.6%, and an average F1 score of 80.1%. Latency was kept below 9 ms per image with YOLOv12, ensuring smooth and lag-free processing, even for utilitarian embedded systems. This allows for efficient deployment in near-real-time applications where speed and immediate response are crucial. These results confirm the viability of the system in both accuracy and computational efficiency. This work provides an innovative solution in the field of ambient intelligence, characterized by low equipment cost and high scalability, laying the foundations for the development of smart waste management infrastructures in sustainable cities. Full article

Treadmills are a convenient tool to study animal gait and behavior. Traditional animal treadmill designs often entail preset speeds and therefore have reduced adaptability to animals’ dynamic behavior, thus restricting the experimental scope. Fortunately, advancements in computer vision and automation allow circumvention of these limitations. Here, we introduce a series of real-time adaptive treadmill systems utilizing both marker-based visual fiducial systems (colored blocks or AprilTags) and marker-free (pre-trained models) tracking methods powered by advanced computer vision to track experimental animals. We demonstrate their real-time object recognition capabilities in specific tasks by conducting practical tests and highlight the performance of the marker-free method using an object detection machine learning algorithm (FOMO MobileNetV2 network), which shows high robustness and accuracy in detecting a moving rat compared to the marker-based method. The combination of this computer vision system together with treadmill control overcome the issues of traditional treadmills by enabling the adjustment of belt speed and direction based on animal movement. Full article

With the natural evolution of the Arctic route and advancements in related technologies, the development of new green ice-class ships is becoming a key technological breakthrough for the global shipbuilding industry. As a special vessel form that must perform icebreaking operations and undertake long-distance ocean voyages, an ice-class ship requires sufficient icebreaking capacity to navigate ice-covered water areas. However, since such ships operate for most of their time under open water conditions, it is also crucial to consider their resistance characteristics in these environments. Firstly, this paper employs linear interpolation to extract wind, wave, and sea ice data along the route and calculates the proportion of ice-covered and open water area in the overall voyage. This provides data support for hull form optimization based on real sea state conditions. Then, a resistance optimization platform for ice-class ships is established by integrating hull surface mixed deformation control within a scenario analysis framework. Based on the optimization results, comparative analysis is conducted between the parent hull and the optimized hull under various environmental resistance scenarios. Finally, the optimization results are evaluated in terms of energy consumption using a fuel consumption model of the ship’s main engine. The optimized hull achieves a 16.921% reduction in total resistance, with calm water resistance and wave-added resistance reduced by 5.92% and 27.6%, respectively. Additionally, the optimized hull shows significant resistance reductions under multiple wave and floating ice conditions. At the design speed, calm water power and hourly fuel consumption are reduced by 7.1% and 7.02%, respectively. The experimental results show that the hull form optimization process in this paper can take into account both ice-region navigation and ice-free navigation. The design ideas and solution methods can provide a reference for the design of ice-class ships. Full article

Effective monitoring of fish passage through river barriers is essential for evaluating fishway performance and supporting adaptive river management. Traditional methods are often invasive, labor-intensive, or too costly to enable widespread implementation across most fishways. Infrared (IR) beam-break counters offer a promising alternative, but their adoption has been limited by high costs and a lack of flexibility. We developed and tested a novel, low-cost infrared beam-break counter—FishTracker—based on open-source Raspberry Pi and Arduino platforms. The system detects fish passages by analyzing interruptions in an IR curtain and reconstructing fish silhouettes to estimate movement, direction, speed, and morphometrics under a wide range of turbidity conditions. It also offers remote access capabilities for easy management. Field validation involved controlled tests with dummy fish, experiments with small-bodied live specimens (bleak) under varying turbidity conditions, and verification against synchronized video of free-swimming fish (koi carp). This first version of FishTracker achieved detection rates of 95–100% under controlled conditions and approximately 70% in semi-natural conditions, comparable to commercial counters. Most errors were due to surface distortion caused by partial submersion during the experimental setup, which could be avoided by fully submerging the device. Body length estimation based on passage speed and beam-interruption duration proved consistent, aligning with published allometric models for carps. FishTracker offers a promising and affordable solution for non-invasive fish monitoring in multispecies contexts. Its design, based primarily on open technology, allows for flexible adaptation and broad deployment, particularly in locations where commercial technologies are economically unfeasible. Full article

This study introduces a rigid–flexible coupled constant force actuator integrated with Active Disturbance Rejection Control (ADRC) to tackle the rigidity–compliance trade-off in precision force-sensitive applications. The actuator utilizes compliant hinges to decrease contact stiffness by three orders of magnitude (${10}^6\to {10}^3 \mathrm{N}/\mathrm{m}$), facilitating effective force management through millimeter-scale placement (0.1∼1 mm) and inherently mitigating high-frequency disturbances. The ADRC framework, augmented by an Extended State Observer (ESO), dynamically assesses and compensates for internal nonlinearities (such as friction hysteresis) and external disturbances without necessitating accurate system models. Experimental results indicate enhanced performance compared to PID control: under dynamic disturbances, force deviations are limited to $\pm 0.2 \mathrm{N}$ with a 98.5% reduction in mean absolute error, a 96.3% increase in settling speed, and 99% suppression of oscillations. The co-design of mechanical compliance with model-free control addresses the constraints of traditional high-stiffness systems, providing a scalable solution for industrial robots, compliant material processing, and medical device operations. Validation of the prototype under sinusoidal perturbations demonstrates reliable force regulation (settling time $<0.56 \mathrm{s}$, errors $<0.5 \mathrm{N}$), underscoring its relevance in dynamic situations. This study integrates theoretical innovation with experimental precision, enhancing intelligent manufacturing systems via adaptive control and structural synergy. Full article

Few studies have examined Hybrid Aquatic–Aerial Vehicles (HAAVs), autonomous vehicles designed to operate in both air and water, especially those that are aircraft-launched and recovered, with a variable-sweep design to free dive into a body of water and breach under buoyant and propulsive force to re-achieve flight. The novel design research examines the viability of a recoverable sonar-search child aircraft for maritime patrol, one which can overcome the prohibitive sea state limitations of all current HAAV designs in the research literature. This paper reports on the analysis from computational fluid dynamic (CFD) simulations of such an HAAV diving into static seawater at low speeds due to the reverse thrust of two retractable electric-ducted fans (EDFs) and its subsequent breach back into flight initially using a fast buoyancy engine developed for deep-sea research vessels. The HAAV model entered the water column at speeds around 10 ms⁻¹ and exited at 5 ms⁻¹ under various buoyancy cases, normal to the surface. Results revealed that impact force magnitudes varied with entry speed and were more acute according to vehicle mass, while a sufficient portion of the fuselage was able to clear typical wave heights during its breach for its EDF propulsors and wings to protract unhindered. Examining the medium transition dynamics of such a novel HAAV has provided insight into the structural, propulsive, buoyancy, and control requirements for future conceptual design iterations. Research is now focused on validating these unperturbed CFD dive and breach cases with pool experiments before then parametrically and numerically examining the effects of realistic ocean sea states. Full article

Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration—exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems. Full article

Conventional model-free control (MFC) is widely used in motor drives due to its simplicity and model independence, yet its performance suffers from imperfect disturbance estimation and input gain mismatch. To address these issues, this paper proposes an adaptive enhanced model-free speed control (AEMFSC) scheme based on an ultra-local model for permanent magnet synchronous motor (PMSM) drives. First, by integrating a nonlinear disturbance observer (NDOB) and a PD control law into the generalized model-free controller, an enhanced model-free speed controller (EMFSC) was developed to ensure closed-loop stability. Compared with a conventional MFSC, the proposed method eliminated steady-state errors, reduced the speed overshoot, and achieved faster settling with improved disturbance rejection. Second, to address the performance degradation induced by input gain $\alpha$ mismatch during time-varying load conditions, we developed an online parameter identification method for real-time $\alpha$ estimation. This adaptive mechanism enabled automatic controller parameter adjustment, which significantly enhanced the transient tracking performance of the PMSM drive. Furthermore, an algebraic-framework-based high-precision identification technique is proposed to optimize the initial $\alpha$ selection, which effectively reduces the parameter tuning effort. Simulation and experimental results demonstrated that the proposed AEMFSC significantly enhanced the PMSM’s robustness against load torque variations and parameter uncertainties. Full article

Trajectory tracking is a crucial task for autonomous mobile robots, requiring smooth and safe execution in dynamic environments. This study uses a nonlinear model predictive controller (MPC) to ensure accurate trajectory tracking of a four-wheel drive, four-wheel steer (4WD-4WS) mobile robot. However, the MPC’s performance depends on the optimal tuning of its key parameters, a challenge addressed using the Multivariate Gaussian Mixture Model Continuous Ant Colony Optimization (MGMM-${\mathrm{ACO}}_R$) algorithm. This method improves on the classic ${\mathrm{ACO}}_R$ algorithm by overcoming two major limitations: the lack of consideration for interdependencies between optimized variables, and an inadequate balance between global exploration and local exploitation. The proposed approach is validated by a two-phase evaluation. Firstly, benchmark function evaluations demonstrate its superiority over established optimization algorithms, including ACO, ${\mathrm{ACO}}_R$, and PSO and its variants, in terms of convergence speed and solution accuracy. Secondly, MGMM-${\mathrm{ACO}}_R$ is integrated into the MPC framework and tested in various scenarios, including trajectory tracking with circular and eight trajectories and dynamic obstacle avoidance during trajectory tracking. The results, evaluated based on trajectory error, control effort, and computational latency, confirm the robustness of the proposed method. In particular, the explicit modeling of correlations between variables in MGMM-${\mathrm{ACO}}_R$ guarantees stable, collision-free trajectory tracking, outperforming conventional ${\mathrm{ACO}}_R$-based approaches that optimize variables independently. Full article

Three-dimensional concrete printing (3DCP) represents a paradigm shift in construction technology, enabling the automated, formwork-free fabrication of intricate geometries. Despite its rapid growth, successful implementation remains dependent on the precise control of material rheology and printing parameters. This review critically analyzes the foundational rheological properties of static yield stress, dynamic yield stress, plastic viscosity, and thixotropy and their influence on three core printability attributes, i.e., pumpability, extrudability, and buildability. Furthermore, it explores the role of critical process parameters, such as print speed, nozzle dimensions, layer deposition intervals, and standoff distance, in shaping interlayer bonding and structural integrity. Special emphasis is given to modeling frameworks by Suiker, Roussel, and Kruger, which provide robust tools for evaluating structural stability under plastic yield and elastic buckling conditions. The integration of these rheological and process-based insights offers a comprehensive roadmap for optimizing the performance, quality, and scalability of 3DCP. Full article

Recent advances in multiple autonomous underwater vehicles (AUVs) have highlighted formation control as a critical challenge for underwater collaborative operations. To address the inherent coupling between formation coordination and individual control in conventional approaches, this paper proposes a novel hierarchical framework of adaptive fixed-time formation control for multiple underactuated AUVs. This framework decouples AUVs’ formation requirements and individual control challenges into two distinct layers: the Collision-free Formation Trajectories Generation (CFTG) Layer and the Adaptive Trajectories Tracking (ATT) Layer. In the CFTG Layer, a consensus-based controller is developed to generate the desired trajectories for the AUVs to meet the requirements of complex formation tasks. And an improved artificial potential field method is proposed to ensure AUVs can reach the target point when the target is close to obstacles. In the ATT Layer, an auxiliary compensation system is designed to address the issue of input saturation. Furthermore, the adaptive fixed-time controllers are proposed to handle the uncertain parameters in the model, enabling underactuated AUVs to track the desired trajectory precisely. Both layers guarantee fixed-time convergence to increase the convergence speed. Simulations are conducted to demonstrate the effectiveness and better performance of the proposed method. Full article

LED polymer multilayer films offer clear advantages over single-layer coatings, such as minimized particle settling, finer control over particle distribution, and more precise spectral tuning. However, the standard “coat–dry–coat” process for these multilayer systems often traps air bubbles, degrading film quality and uniformity. This study investigates the air entrainment mechanism in multilayer film formation. Bubbles form when the cured bottom layer exhibits a low contact angle, which destabilizes the advancing liquid front. High-speed microscopy captured these interfacial dynamics, and contact-angle measurements quantified the wetting behavior. Numerical simulations further demonstrated that reduced wettability and vortex formation drive air entrainment. To mitigate air entrainment, a semi-cured slot die coating approach was proposed to modify the surface wettability and suppress the flow instabilities. Incorporating temperature-dependent viscosity into the simulation model improved its predictive accuracy, cutting the error in predicted coating-gap limits from 11.49% to 4.99%. This combined strategy delivers reliable, bubble-free multilayer films and paves the way for more consistent, high-quality LED polymer applications. Full article