Search Results (1,763)

Background/Objectives: Steady-State Visual Evoked Potential-based Brain–Computer Interfaces face a critical trade-off between system accuracy and user visual fatigue. To address this challenge, the objective of this study was to determine how the spatial manipulation of stimulus size modulates the full spectral dynamics of the Electroencephalogram, encompassing both the periodic oscillatory response and the aperiodic (1/f) background noise. Methods: Twenty-two healthy subjects completed a sustained visual attention task using a competitive stimulus paradigm (20 Hz and 30 Hz) presented in three spatial dimensions (Small, Medium, and Big). Parieto-occipital brain signals were decomposed using the spectral parameterization algorithm (SpecParam) to extract frequency-specific visually evoked response power and the aperiodic slope, while visual fixation was continuously monitored via eyetracking. Results: Increasing stimulus size induced a statistically significant gain in the power of the attended signal (Target) without increasing the response of the peripheral distractor. Simultaneously, larger stimuli produced a significant increase in the aperiodic slope during 20 Hz attention and visual rest, suggesting increased cortical inhibition and a reduction in broadband neural activity. This aperiodic modulation was not observed at 30 Hz. Conclusions: The improvement in Signal-to-Noise Ratio with increasing stimulus size arises from a dual neurophysiological mechanism: enhancement of the periodic evoked response together with a reduction in background neural noise. Full article

In flexible manufacturing systems, the composite mobile manipulator (CMM) is subject to nonlinear inertial disturbances arising from the dynamic coupling between the mobile platform and the robotic arm. These disturbances significantly impair positioning precision during grasping tasks. This paper addresses the dynamic decoupling of multi-body nonlinear inertial disturbances within CMM systems. Departing from the conventional “stop-then-plan” serial execution paradigm, we propose a full-cycle spatiotemporally coupled trajectory optimization method. The operation cycle is bifurcated into two synergistic stages: “dynamic calibration” and “static execution.” The dynamic calibration trajectory is pre-planned and executed synchronously during platform movement to actively compensate for inertial-induced pose deviations. Concurrently, the static execution trajectory is optimized and then triggered immediately upon platform standstill, ensuring a seamless and precise transition to the “Grasping Pose”. It is worth noting that the temporal characteristic central to this framework lies in the concurrent execution of static trajectory optimization and platform transit: by the time the platform reaches its destination, the pre-planned trajectory is already available for immediate triggering, achieving zero task-switching wait time at the planning layer. The term “zero-latency” here does not imply a fixed-cycle real-time response at the control layer, but rather the complete elimination of decision latency afforded by the parallel planning architecture. This framework eliminates computational latency, markedly enhancing operational efficiency. Key innovations include two novel constraints. First, the Adaptive Task-space Bounded Search Constraint (ATBSC) framework restricts optimization to a geometry-inspired search region, thereby enhancing search efficiency and ensuring controllable deviations. Second, the Multi-Rigid-Body Coupling Constraint (MRBCC) system explicitly models inertial transmission across motion phases to suppress pose fluctuations. The proposed framework is developed and validated within an obstacle-free workspace. In simulation-based validation on a UR10 6 degree-of-freedom manipulator model, experimental results indicate that ATBSC increases valid solution density to 84.7% and reduces average deviation by 72.8%. Furthermore, under the tested conditions, MRBCC mitigates end-effector position errors by 79.7–81.0% with a 97.5% constraint satisfaction rate. The improved Cuckoo Search algorithm (ICSA), serving as the solver component of the proposed framework, achieves an 11.9% lower fitness value and a 13.1% faster convergence rate compared to the standard Cuckoo Search algorithm in the tested scenarios, suggesting its effectiveness as a reliable solver for the constrained multi-objective trajectory optimisation problem. Full article

In recent years, Visual Education has emerged as an innovative and interdisciplinary teaching approach aimed at promoting meaningful learning through the conscious use of visual tools and languages. This educational paradigm helps to facilitate the understanding of complex concepts, translating them into clear and intuitive visual representations, while enhancing memorisation skills, critical information processing and the practical application of acquired knowledge. This systematic review, conducted according to the PRISMA (2020) protocol, analyses the most recent empirical evidence on the effectiveness of Visual Education in educational contexts. The main objective is to assess how the intentional use of visual tools—images, concept maps, educational videos, interactive digital materials, and virtual manipulatives—contributes to enhancing learning processes and developing transversal skills. Through a comparative analysis of fourteen international contributions published between 2020 and 2025, selected from the Scopus, Web of Science and EBSCO databases, the research highlights how Visual Education significantly influences the improvement of academic performance, motivation and cognitive and emotional engagement of students. The results also confirm the inclusive function of visual teaching, which can encourage participation, self-esteem and cooperation even in individuals with special educational needs. The discussion emphasises the need for the systematic integration of Visual Education into school curricula as a strategy to enhance soft skills and promote more equitable, effective learning geared towards the integral development of the individual. Full article

Multi-label deep learning models are widely used in real-world applications where predictions depend on the joint presence of several semantically correlated labels. However, existing adversarial attacks largely overlook these inter-label dependencies, often perturbing outputs indiscriminately and producing structurally implausible or easily detectable changes. This paper presents CAPG (Context-Aware Perturbation Generation), a white-box, label-space targeted adversarial framework for generating selective and contextually consistent perturbations in multi-label settings. CAPG incorporates correlation-weighted regularization into the adversarial objective, enabling targeted manipulation of specific labels while preserving the contextual integrity of non-target outputs. Using the Pascal VOC 2012 dataset and a ResNet-101 multi-label classifier, we show that CAPG achieves higher Attack Success Rates (ASR) and substantially improved Contextual Consistency Scores (CCSs) than FGSM, PGD, CW, and DeepFool under identical perturbation budgets. CAPG also produces lower perceptual distortion, yielding adversarial examples that better preserve contextual structure. These results highlight the importance of correlation-aware adversarial evaluation for assessing the robustness of modern multi-label deep learning systems. Full article

Underactuated system control is a central topic in nonlinear system control. For the three-link vertical underactuated manipulator with only the first joint actuated (APP manipulator), the control objective of swing-up and balancing is challenging. The advantages of this paper are as follows: (i) The proposed method avoids balancing region division in common partitioned control, preventing failure to stabilize at the target position due to improper partitioning. (ii) The time-based switching condition optimized via parameter tuning is easier to satisfy than the state-based condition. (iii) The proposed controller effectively suppresses state fluctuations caused by switching, yielding a smoother transition. (iv) The proposed controller avoids the singularity problem. The main procedures are as follows. First, the dynamic model of the APP manipulator is established. Then, a trajectory is designed to guide the active link from the initial position to the vicinity of the target position. On this basis, to ensure that all links can simultaneously reach the vicinity of the target position, the trajectory parameters are optimized according to the coupling relationship between the links. Next, an NFTSM-based tracking controller is developed to steer the links along the optimized trajectory. After that, an LQR-based stabilization controller is further employed to lock the system at the target position. Finally, the effectiveness of the proposed method is verified through simulations. Full article

Mixed Reality (MR) teleoperation offers an intuitive interface for Human-Robot Collaboration (HRC), yet it often faces the “Embodiment Gap”—a physical and kinematic mismatch between human operators and robotic platforms. Existing MR systems primarily rely on a “direct mapping” approach, where user movements are transferred directly to the robot. This forces operators to manually adapt to robotic constraints, such as singularities and joint limits, making task performance heavily dependent on individual skill. This study proposes Mixed reality Adaptive Spatial and Kinematic support (MASK), an adaptive shared-control framework designed to bridge the “Gulf of Execution” and “Gulf of Evaluation” by separating target selection from reachability and kinematic feasibility. The MASK system integrates three core modules: (1) Target Object Identification (TOI) based on body motion features to identify the intended manipulation target; (2) a Base Relocation Module (BRI) utilizing Inverse Reachability Maps to optimize the robot’s spatial configuration; and (3) a Kinematic Correction Module (KCM) that autonomously resolves kinematic constraints through pose blending and null-space optimization. Initial experimental results suggest that MASK reduces the operator’s cognitive and physical load by shifting the burden of kinematic resolution from the human to the system. This approach enables high-precision manipulation through an intuitive interface, potentially reducing the performance gap between different levels of operator proficiency. Full article

Introduction: Nerve-sparing (NS) during robot-assisted radical prostatectomy (RARP) plays a critical role in postoperative functional recovery, particularly urinary continence and erectile function. Despite the importance of precise neurovascular bundle (NVB) preservation, intraoperative assessment of NS quality remains largely subjective and lacks standardized evaluation tools. The aim of this study was to develop and preliminarily evaluate a structured intraoperative scoring system designed specifically for assessing NS quality during RARP. Methods: A novel 10-point intraoperative NS scoring system (NSQ Score) based on five domains was developed: dissection plane, bleeding control, bundle manipulation, continuity of dissection, and symmetry. Each parameter was rated on a 0–2 scale. Thirty robot-assisted radical prostatectomy (RARP) procedures performed in 2024 were randomly selected from a prospectively maintained institutional surgical video archive. Cases were not pre-filtered based on tumor stage, surgical difficulty, or intraoperative complexity. High-definition video recordings of the nerve-sparing phase were anonymized and independently evaluated by three experienced observers blinded to patient outcomes and to each other’s assessments. Inter-rater agreement was analyzed using weighted Cohen’s kappa statistics with quadratic weights, complemented by exact and near-agreement proportions. Cluster bootstrap resampling was applied to account for bilateral observations. Results: A total of 48 evaluable observations were analyzed. The overall inter-rater agreement demonstrated a weighted kappa of 0.41 (95% CI 0.36–0.48), indicating fair-to-moderate agreement among reviewers. Exact agreement occurred in 43% of observations, while near-agreement (allowing one ordinal level difference) reached 98%. Among individual parameters, symmetry demonstrated the highest reliability with substantial agreement (κ = 0.70; 95% CI 0.58–0.81). Other domains showed fair agreement, including intraoperative bleeding (κ = 0.36), continuity of dissection (κ = 0.39), bundle manipulation (κ = 0.34), and dissection plane (κ = 0.27). Agreement levels were comparable between left- and right-sided dissections. Conclusions: We propose a novel structured intraoperative scoring system for evaluating nerve-sparing quality during RARP. The scale is simple, procedure-specific, and feasible for structured postoperative or video-based assessment. Preliminary results demonstrate fair-to-moderate inter-rater reliability with very high near-agreement, supporting the feasibility of this tool for clinical use. The proposed scoring system may facilitate standardized training, objective performance assessment, and future studies correlating intraoperative NS quality with functional outcomes. Full article

Nano-tweezers, especially those based on photonic crystals and plasmonic structures, are powerful tools for trapping, manipulating, or accelerating nano-sized objects. However, the precise control of the inter-distance between trapped nanoparticles has rarely been considered. In this paper, we propose a mirror-symmetric optical conveyor belt, in which each unit contains three graded nano-slots. Through the optimized design of spacing between these nano-slots, the structure generates multiple trapping centers, enabling wavelength-selective control over trapping positions. The results show that, through dynamically shifting excitation wavelengths, the programmable bidirectional optical manipulation of nanoparticles can be achieved. Also, the inter-distance between trapped particles can be tuned with subwavelength precision. The proposed structure provides a versatile solution for lab-on-a-chip systems, especially for systems aiming to study the interactions between objects. Full article

Many hands-on tasks remain difficult to fully automate because they require human dexterity and flexible object handling. Data gloves offer a promising interface for sensing hand–object interactions, but most prior systems focus on gesture recognition or object classification rather than closed-loop, step-by-step task guidance. In this work, we develop and evaluate a tactile-sensing operation support system using an e-textile data glove with 88 pressure sensors, a tactile pressure sheet for placement verification, and a GUI that provides step-by-step instructions. As a core component, a CNN classifies the grasped state as bare hand or one of four discs with 93.3% accuracy using 16,175 training samples collected from five participants. In a user study on the Tower of Hanoi task as a controlled proxy for multi-step manipulation, the system reduced mean solving time by 51.5% (from 242.6 s to 117.8 s), reduced the number of disc movements (35.4 to 15, about 20 fewer moves on average), and lowered perceived workload (NASA-TLX) by 53.1% (from 68.5 to 32.1), while achieving a SUS score of 75. These results demonstrate the feasibility of tactile-based step verification and guidance in a controlled multi-step task; broader generalization requires evaluation with larger and more diverse participant groups and tasks. Full article

Reliable textile recycling requires automated unfolding to expose hidden hard components such as zippers, buttons, and metal fasteners, which otherwise risk damaging machinery and compromising downstream processes. This paper presents the design and implementation of an automated textile unfolding system based on a dual-arm robotic manipulation framework. The system uses two Interbotix WidowX 250s 6-DoF robotic arms and an Intel RealSense L515 LiDAR camera for visual perception. The unfolding process consists of three stages: initial dual-arm stretching to reduce major folds, refinement through a second stretch targeting the lower region, and a machine-learning stage that employs a YOLOv11 framework trained on depth-encoded textile images, followed by a depth-gradient-based estimator for fold direction. The system applies an extremity-based grasping strategy that selects leftmost and rightmost textile points from a custom error-corrected depth map, enabling robust grasp point selection, and a fold direction estimation method based on depth gradients around the detected fold. The most confident fold region is selected, an unfolding direction is determined using depth ranking, and the textile is manipulated until a flat state is confirmed through depth uniformity. Experiments show that depth correction significantly reduces spatial error in the robot frame, while segmentation and extremity detection achieve high accuracy across varied fold configurations, and the YOLOv11n-based model reaches 98.8% classification accuracy, while fold direction is estimated correctly in 87% of test cases. By enabling robust, largely autonomous textile unfolding, the system demonstrates a practical approach that could support safer and more efficient automated textile recycling workflows. Full article

Background/Objectives: Gastro-resistant multiparticulate systems are designed to protect drugs in acidic environments and to ensure intestinal release. In practice, the method of administration may need to be modified: pellet-containing capsules opened or tablets halved for patients with swallowing difficulties, yet the type of liquid used for administration is often not specified. This study examined the stability of gastro-resistant coated pellets after exposure to various aqueous media prior to ingestion. Methods: To evaluate administration instructions, 103 Summaries of Product Characteristics of gastro-resistant products were reviewed. Pellets were produced using a bottom-spray fluidized bed process and coated with Eudragit L 30 D-55. Dissolution testing in pH 1.2 medium was performed after pre-soaking the pellets for 5, 15, and 30 min in beverages with various pH and conductivity. Drug release was measured by UV-VIS method, and morphological changes were assessed by image analysis. Marketed gastro-resistant products were also examined visually. Results: SmPC review revealed that the beverage for intake was frequently unspecified. Among the tested beverages differences in pH and conductivity were observed. Alkaline medicinal mineral waters induced increased and time-dependent premature drug release compared to tap and filtered water. Image analysis indicated a reduction in surface area after exposure to alkaline media. Conclusions: Contact with non-specified aqueous media before swallowing may weaken the protective function of gastro-resistant films. More explicit recommendations on suitable administration manipulation and media may improve therapeutic consistency. Full article

This study presents a comprehensive multi-objective optimization of sewage wastewater treatment using bio-based coagulants, guided by the Grey Wolf Optimizer (GWO) and its multi-objective variant (MOGWO). Experimental coagulation data, employing Citrullus lanatus and Cucumis melo as natural coagulants, were modeled using multivariate regression techniques, yielding high coefficients of determination (R² > 0.95) across key water quality parameters. The optimization process targeted maximal reductions in turbidity, total suspended solids (TSS), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) through strategic manipulation of pH and coagulant dosage. The single-objective GWO achieved significant outcomes, including a 96.68% turbidity reduction at pH 5 and 50 mg/L dosage. The MOGWO algorithm identified Pareto-optimal solutions, such as a 94.2% turbidity reduction at pH 5 and 72 mg/L dosage, and a balanced BOD reduction of 52.7% at pH 7. The predictive models indicated that optimal treatment conditions could reduce chemical usage by up to 90% compared to conventional coagulants, resulting in potential cost savings of up to 30%. Moreover, the algorithms demonstrated rapid convergence, averaging 200 iterations, highlighting their computational efficiency and robustness. These findings illustrate that integrating bio-based coagulants with advanced optimization techniques can achieve high treatment efficiency while reducing chemical inputs, thus directly supporting environmental sustainability by minimizing sludge and secondary pollution. In this situation, the wastewater treatment plant will focus on resource-recovery systems with less or no waste at the end of the treatment process. This approach aligns with circular economy principles by promoting eco-friendly, cost-effective wastewater treatment solutions suitable for resource-limited settings. The study offers a forward-looking pathway for environmentally responsible wastewater management practices that significantly reduce chemical dependency and contribute to pollution mitigation efforts. Full article

As robotics prehension systems and virtual reality applications are in constant evolution, the need for high-fidelity haptic interaction increases. This helps ensure and enhance user immersion and handling precision. While commercial haptic interfaces offer high performance, their prohibitive cost limits their widespread adoption in general-purpose robotics. Furthermore, many low-cost solutions suffer from limited transparency, where the operator constantly fights the friction of the actuator even during free motion. This article presents the design and development of an innovative, cost-effective master–slave robotic system aimed at democratizing efficient haptic feedback devices. The solution is intended for remote manipulation of objects with a maximum mass of 1 kg, while limiting the gripping force to 50 N, thus ensuring the integrity of objects being manipulated. The device includes a master haptic module in the form of a clamp that reproduces the thumb–index–middle finger gripping motion performed by the user. The system relies on a custom haptic interface measuring the angular position of the master gripper, which is transmitted in real time to the slave gripper, so as to adjust the position of the clamp accordingly, thus optimizing the grasping control loop. As soon as an object is detected, using a force sensor integrated into the slave gripper, the master motor renders a resistive force, preventing the user from closing the haptic module. The other part of the system is the slave mechanical gripper with three fingers, each with three phalanges based on human anatomy, allowing the clamp to mechanically conform to irregular object geometries with a single actuator. The last but not least innovative aspect lies in the implementation of a current sensor, which provides the haptic feedback. The force applied by the user is reproduced by the slave gripper using current sensors, eliminating the need for expensive force-torque sensors while maintaining a responsive feedback loop. Full article

Background and Objectives: Overt perioperative stroke remains a feared complication of adult cardiac surgery. Diffusion-weighted magnetic resonance imaging (DWI-MRI) has revealed a more prevalent form of cerebral injury, termed silent stroke or silent brain injury (SBI). Covert ischemic lesions occur without focal neurological deficits but are increasingly associated with postoperative delirium, cognitive decline, and elevated long-term cerebrovascular risk. Despite growing recognition, the true burden, mechanisms, and clinical relevance of SBI remain incompletely integrated into perioperative practice. Materials and Methods: We performed a narrative review of the literature published between January 2000 and December 2025, identified through PubMed/MEDLINE and Scopus. Eligible studies included prospective and retrospective cohorts, randomized trials, systematic reviews, and meta-analyses involving adult patients undergoing coronary artery bypass grafting, valve surgery, or minimally invasive cardiac procedures, with or without cardiopulmonary bypass, and reporting MRI-detected ischemic lesions or validated surrogate markers of cerebral injury. Pediatric studies, transcatheter interventions, case reports, and non-English publications were excluded. Sixty studies met the inclusion criteria. Results: Silent stroke occurred more frequently than clinically apparent stroke, with new DWI-MRI lesions detected in approximately 20–60% of patients following cardiac surgery. Lesions were typically small, multifocal, and embolic in distribution, predominantly affecting cortical and watershed regions. Cardiopulmonary bypass-related factors, including aortic manipulation, cerebral microembolization, hemodilution, hypoperfusion, and impaired oxygen delivery, emerged as key contributors. Several studies demonstrated associations between SBI burden and postoperative delirium, early cognitive dysfunction, and functional decline. Perfusion-based neuroprotective strategies showed mechanistic benefit, although no single intervention conclusively prevented SBI. Conclusions: Silent stroke represents the most frequent form of neurological injury in adult cardiac surgery. Evidence suggests that these covert lesions reflect clinically meaningful cerebral injury, with potential short- and long-term consequences. Recognition of silent stroke as a relevant neurological endpoint supports a shift toward multimodal, perfusion-driven neuroprotective strategies and the routine incorporation of MRI-based outcomes in future cardiac surgical research. Full article

The integration of emerging technologies such as robotics and artificial intelligence (AI) has the potential to transform agricultural harvesting by improving efficiency, reducing waste, lowering labor dependency, and enhancing produce quality. This paper presents the development of an intelligent robotic berry harvesting system that combines deep learning–based perception with autonomous robotic manipulation for real-time strawberry harvesting. A computer vision pipeline based on the YOLOv11 segmentation model was developed and integrated into a Smart Mobile Manipulator (SMM) equipped with autonomous navigation, a 6-degree-of-freedom (6-DoF) xArm 6 robotic arm, and ROS middleware to enable real-time operation. Using a publicly available strawberry dataset comprising 2,800 images collected under ridge-planted cultivation conditions, the proposed YOLOv11-small segmentation model achieved 84.41% mAP@0.5, outperforming YOLOv11 object detection, Faster R-CNN, and RT-DETR in segmentation quality while maintaining real-time performance at 10 FPS on an NVIDIA Jetson Orin Nano edge GPU. A PCA-based fruit orientation and geometric analysis method achieved 86.5% localization accuracy on 200 test images. Controlled indoor harvesting experiments using synthetic strawberries demonstrated an overall harvesting success rate of 72% across 50 trials. The proposed system provides a general-purpose platform for berry harvesting in controlled environments, offering a scalable and efficient solution for autonomous harvesting. Full article