Search Results (6,925)

Background: Flexible surgical instruments for Robot-Assisted Minimally Invasive Surgery (RAMIS) face a critical limitation: the inability to rotate the distal head while the instrument is in a bent configuration, which restricts the maneuverability in narrow surgical workspaces. Methods: This paper presents a novel 4-degree-of-freedom (DOF) flexible laparoscopic instrument with a 10 mm diameter, incorporating a 3D-printed flexible element. The design enables independent bending (0–90°), continuous distal head rotation (360°), gripper actuation (0–60°), and rod rotation (180°). A constant-curvature kinematic model was developed. The instrument was manufactured using PolyJet 3D printing technology and integrated with the ATHENA parallel robot for proof-of-concept experimental validation. Results: Experimental tests demonstrated successful independent 360° distal head rotation across the full bending range (0–90°), validated through simulated surgical procedures including stomach retraction. Quantitative characterization using optical motion capture revealed a maximum angular deflection of 79.85° at 670 g applied load, with tip displacements of 74.95 mm (X) and 91.18 mm (Y). The measured grasping force was approximately 2 N, tip position repeatability was ±2.86 mm, and fatigue testing demonstrated no degradation after 500 bending cycles, confirmed by digital microscope inspection. The instrument performed multiple manipulation tasks, including elastic band transfer, wire path navigation, spring manipulation, and tissue grasping. Conclusions: The proposed instrument addresses a significant white spot in surgical robotics by adding an additional functional capability enabling grasper reorientation without repositioning the entire instrument. Full article

Autonomous navigation in mobile robots operating in dynamic and partially known environments demands the coordinated integration of perception, decision-making, and control while ensuring stability, safety, and energy efficiency. This paper presents an integrated navigation framework for differential-drive mobile robots that combines deep learning-based visual perception, reinforcement learning (RL) for high-level decision-making, and a Lyapunov-based trajectory reference generator for low-level motion execution. A convolutional neural network processes RGB-D images to classify obstacle configurations in real time, enabling navigation without prior map information. Based on this perception layer, an RL policy generates adaptive navigation subgoals in response to environmental changes. To ensure stable motion execution, a Lyapunov-based control strategy is formulated at the kinematic level to generate smooth velocity references, which are subsequently tracked by embedded PID controllers, explicitly decoupling learning-based decision-making from stability-critical control tasks. The local stability of the trajectory-tracking error is analyzed using a quadratic Lyapunov candidate function, ensuring asymptotic convergence under ideal kinematic assumptions. Experimental results demonstrate that while higher control gains provide faster convergence in simulation, an intermediate gain value (K = 0.5I) achieves a favorable trade-off between responsiveness and robustness in real-world conditions, mitigating oscillations caused by actuator dynamics, delays, and sensor noise. Validation across multiple navigation scenarios shows average tracking errors below 1.2 cm, obstacle detection accuracies above 95% for human obstacles, and a significant reduction in energy consumption compared to classical A* planners, highlighting the effectiveness of integrating learning-based navigation with analytically grounded control. Full article

Maritime dynamic observation missions, such as environmental monitoring, marine ranching inspection, and emergency response, typically require large-scale and high-efficiency operations in complex and variable maritime environments. Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) offer complementary advantages in such missions: USVs provide long endurance and stable platform support, while UAVs enable rapid, high-coverage aerial perception. However, limited UAV battery capacity and dynamic task environments pose significant challenges to autonomous collaborative operations. This study proposes a collaborative operation and energy replenishment strategy for USV–UAV systems in maritime dynamic observation missions. Under a unified framework, task allocation, collaborative path planning, and energy replenishment are jointly optimized, where the USV serves as a mobile replenishment platform to provide energy support for the UAV. The proposed method incorporates dynamic task updates, environmental disturbances, and energy constraints, achieving real-time adaptive collaboration between heterogeneous agents. Validation through both simulations and actual sea trials demonstrates that the proposed strategy significantly outperforms four baseline methods (greedy strategy, static planning, multi-objective genetic algorithm, and reinforcement learning scheduler) across five core metrics: task completion rate (91.74% in simulation/90.85% in sea trials), total energy consumption (1284.66 kJ/1298.42 kJ), mission completion time (40.28 min/41.12 min), average response time (10.21 s/10.35 s), and path redundancy (13.79%/14.03%). Furthermore, ablation experiments verify that the energy replenishment strategy enhances the task completion rate in both simulation and field tests. This method provides a feasible and scalable collaborative solution for autonomous multi-agent systems, offering significant guidance for the practical deployment of future maritime observation and monitoring missions. Full article

As intelligent construction technology advances, new projects have become more technology-intensive, collaborative, and multi-objective. Traditional team formation methods based on people’s experience can no longer meet their complex management needs. This study reframes team formation as a multi-objective optimization problem to maximize person–job fit and team collaboration. By introducing a hierarchical penalty mechanism for structured resumes and performing semantic feature extraction on unstructured text via the BERT-base-Chinese model, we develop a job competency model, quantify person–job fit with cosine similarity, and assess team collaboration through MBTI theory and a project-specific scoring framework. An improved algorithm, CSCD-NSGA-II, is proposed, which combines K-means clustering and a modified crowding distance, to maintain solution diversity under constraints. It improves HV by 1.55% and reduces SP by 10.81% compared to the standard NSGA-II. Validation using real projects, simulated data, and algorithm comparisons demonstrates that CSCD-NSGA-II generates teams more efficiently than manual methods. Survey results indicate improved role diversity and the feasibility of collaboration, along with similar task adaptability. The algorithm also outperforms NSGA-II, MOPSO, and SPEA2, supporting intelligent team formation in modern construction. Full article

In signal processing and information analysis, the detection and identification of anomalies present in signals constitute a critical research focus. Accurately discerning these deviations using probabilistic, statistical, and information-theoretic methods is essential for ensuring data integrity and supporting reliable downstream analysis. Outlier detection in functional data aims to identify curves or trajectories that deviate significantly from the dominant pattern—a process vital for data cleaning and the discovery of anomalous events. This task is challenging due to the intrinsic infinite dimensionality of functional data, where outliers often appear as subtle shape deformations that are difficult to detect. Moving beyond conventional approaches that discretize curves into multivariate vectors, we introduce a novel framework that projects functional data into a low-dimensional space of meaningful features. This is achieved via a tailored weighting scheme designed to preserve essential curve variations. We then incorporate the Mahalanobis distance to detect directional outlyingness under non-Gaussian assumptions through a robustified bootstrap resampling method with data-driven threshold determination. Simulation studies validated its superior performance, demonstrating higher true positive and lower false positive rates across diverse anomaly types, including magnitude, shape-isolated, shape-persistent, and mixed outliers. The practical utility of our approach was further confirmed through applications in environmental monitoring using seawater spectral data, character trajectory analysis, and population data underscoring its cross-domain versatility. Full article

(1) Background: The complexity of today’s healthcare system requires the formation of highly cohesive work teams that guarantee safe and high-quality care. Clinical simulation has become established as a pedagogical strategy capable of promoting the collaborative skills of teams of students and healthcare professionals. The objective of this study was to analyze the influence of learning through clinical simulation on group cohesion in nursing student teams. (2) Methods: A pre–post quasi-experimental study without a control group was conducted with final-year nursing students using the short Spanish version of the Group Environment Questionnaire, validated for nursing students. This questionnaire was administered twice, before and after participation in clinical simulation sessions. (3) Results: Clinical simulation significantly increased group cohesion in most items and in all dimensions with moderate to large effect sizes (r > 0.5). The Group Integration-Task (GI-T) dimension showed the greatest improvement after clinical simulation. Although causal relationships cannot be established, the results suggest an association between exposure to clinical simulation and increased group cohesion. (4) Conclusions: Clinical simulation was associated with significant improvements in both task-oriented and social dimensions of group cohesion among nursing students. These findings suggest that clinical simulation may enhance collaboration, communication, and commitment to shared goals within student teams. Future studies including control groups are needed to confirm these associations and further explore the impact of clinical simulation on team performance in both student and healthcare professional contexts. Full article

In the design of shaking table tests concerning saturated sand–pile interactions, quantitatively achieving similarity in liquefaction responses between the model and the prototype has long been a challenging task. In addition, the dynamic shear modulus of the prepared model soil often fails to satisfy the ideal similarity conditions, which further exacerbates the difficulty of realizing liquefaction response similarity. To address the above issues, the authors have proposed a liquefaction similarity law for saturated sand–pile shaking table tests under horizontal excitation, considering the dynamic shear modulus error of the model soil. To further verify the accuracy of the proposed liquefaction similarity law, investigate its simulation capability, and evaluate its applicability under different conditions, this paper establishes and validates numerical models of saturated sand–pile dynamic interaction systems based on shaking table test results and conducts a series of parametric analyses via numerical simulation. The results indicate that when the proposed similarity law is applied, the acceleration similarity ratio should be set to 1, which can satisfy both gravity similarity and elastic force similarity simultaneously. A comparison with the artificial mass similarity law demonstrates the distinct advantages of the proposed similarity law. Finally, the applicability of the proposed similarity law under different parametric conditions is verified, and the influence of various parameters on the accuracy of the back-calculated results using the similarity law is investigated. Full article

Node classification is a fundamental task in graph-based learning, with applications in social networks, citation networks, and biological systems. Learning node representations for different graph datasets is necessary to find the correlation between different types of nodes. Graph Neural Networks (GNNs) play a critical role in providing revolutionary solutions for graph data structures. In this paper, we analyze the effect of combined GNN and multilayer perceptron (MLP) architecture to solve the node classification problem for different graph datasets. The feature information and network topology are efficiently captured by the GNN layer, and the MLP helps to make accurate decisions. We have selected popular datasets, namely Amazon-computer, Amazon-photo, Citeseer, Cora, Corafull, PubMed, and Wikics, for evaluating the performance of the proposed approach. In addition, in the GNN part, we have used six models to find the best model fit in the proposed architecture. We have conducted extensive simulations to find the node classification accuracy for the proposed model. The results show the proposed architecture can outperform previous studies in terms of test accuracy. In particular, the GNN algorithms SAGEConv, GENConv, and TAGConv show superior performance across different datasets. Full article

With the growth of large language models (LLMs), there are increasing calls to interpret their behavior through the prism of analogies to human cognitive mechanisms. At the same time, scientific literature points to the fundamental limitations of these systems, describing them, among other things, as models that generate a superficial simulation of reasoning without real access to semantic meanings (“stochastic parrots” or “illusion of reasoning”). This paper proposes an innovative, modular benchmark for assessing the cognitive competence of LLMs, integrating three complementary dimensions of language processing: factual, syntactic, and logical. Eight language models (LLama 3.2, Mistral 7B, LLama 3:8B, Gemini 2.5 Flash, ChatGPT-3, ChatGPT-4o mini, ChatGPT-4, and ChatGPT-5) were tested using a uniform procedure with context reset after each interaction and a three-point scoring scheme (0/0.5/1). The results obtained showed a clear advantage for the largest models in tasks based on general knowledge and formal transformations known from training, with a significant decrease in effectiveness, regardless of model size, in tasks requiring conjunctive reasoning based solely on new, local premises. Importantly, unstable but measurable corrective abilities of some models were also observed after feedback, suggesting the presence of reactive mechanisms, but were insufficient to consider them systems capable of cognitive self-reflection. The combined analysis indicates that LLMs effectively simulate syntax and logic rules when the task corresponds to recognizable formal patterns, but fail in situations requiring the construction of new, coherent chains of beliefs and symbolic inferences, which undermines the thesis of their cognitive “understanding”. The results justify the need to create more complex and semantically restrictive evaluation frameworks that will allow distinguishing statistical fit from systemic, multi-stage formal reasoning. The proposed benchmark is a step towards a more multidimensional and diagnostic evaluation of LLMs, shifting the focus from “will the model respond correctly?” to “why and under what conditions is the model able to reason?” Full article

As UAV popularity soars, so does the mission planning associated with it. Classical planning approaches suffer from the triple problems of decoupling of task assignment and path planning, poor real-time and scalability, and limited adaptability. Aiming at these challenges, this paper proposes a multi-UAV real-time collaborative mission planning method based on UAV states. First, the employed Dubins path accurately represents the distance between tasks and satisfies curvature constraints without smoothing, thus achieving a coupled solution for task assignment and path planning. Then, a series of acceleration techniques are applied to guarantee the real-time performance of the method, including task clustering to reduce the decision space, allocation strategies with fewer iterations, and efficient distance cost calculation methods. To enhance robustness and adaptability, real-time assignment of new tasks and task reassignment due to the reduction of available UAVs are appropriately handled. Finally, simulations highlight that the proposed method only increases the path length by 9.57% compared to benchmark method, while achieving a 4–5 orders-of-magnitude improvement in planning speed, with a single mission planning of about 0.0003 s. Moreover, it easily scales to large-scale scenarios (0.0029 s, with 1000 UAVs and 25,000 tasks). Full article

To address the computational-efficiency bottlenecks of Hybrid A* and its bidirectional variant in long-distance parking and narrow-slot scenarios, an improved bidirectional Hybrid A* algorithm is presented. First, the cohesion cost is reformulated in a vector-space representation. Distance and heading-consistency terms are evaluated using dot products, which eliminates trigonometric operations and reduces the overhead of node evaluation. Second, an RS (Reeds–Shepp) cost template is constructed on a sparse grid of key nodes. Neighborhood costs are approximated with Euclidean-distance correction. In addition, a geometry reachability-based trigger is designed for analytic RS connections to avoid redundant analytic linking and unnecessary RS curve computations. Third, a KD-tree spatial index is introduced to accelerate nearest-neighbor queries in the Voronoi potential field, and vehicle corner coordinates are updated in a vectorized manner to improve the efficiency of potential-field evaluation. Simulation results in parallel and perpendicular parking show that, compared with the baseline bidirectional Hybrid A* algorithm, RS computations are reduced by 98.7% and 97.8%, respectively, while total planning time is shortened by 63.2% and 57.5%, with stable path quality. These results indicate that the proposed method effectively mitigates the dominant computational costs of bidirectional Hybrid A* in complex parking tasks and improves the efficiency and real-time performance of automatic parking path planning. Full article

This paper analyzes the performance of a two-stage sequential hybrid MTS/MTO production system in which the order decoupling point is positioned between the manufacturing and assembly stages. In this configuration, the production control task separates into an upstream inventory control problem and a downstream order control problem. To address these jointly, this study examines a combined Kanban-POLCA (Paired-cell Overlapping Loops of Cards with Authorization) approach that applies Kanban to inventory control and POLCA to order control. The feasibility and behavior of this integrated system are evaluated through a simulation experiment. The results show that the considered control factors and their combinations significantly affect system performance, with the combined Kanban-POLCA system achieving superior results compared to the initial POLCA-POLCA system. Based on these findings, this paper outlines theoretical and practical implications that provide guidelines for designing and combining pull systems in two-stage production environments. Full article

Background: Mechanization in olive harvesting has improved productivity but introduced new ergonomic challenges, particularly related to vibration exposure and sustained overhead work. This study investigates the acute and short-term physiological effects of using an electric olive harvester through objective instrumental assessment. Methods: Ten healthy male volunteers performed a standardized 15-min simulated harvesting task using an electric olive harvester. Muscle tone, stiffness, and elasticity of bilateral deltoid, biceps, and triceps were assessed by myotonometry at baseline (T0), immediately post-task (T1), and after 2 h recovery (T2). Infrared thermography evaluated cervical, dorsal, and lumbar skin temperature at the same timepoints. Results: Significant, side-dependent alterations in myotonometric parameters were observed, with marked increases in tone and stiffness of dominant upper-limb muscles and asymmetric adaptations between limbs (p < 0.001, large effect sizes). Infrared thermography revealed significant post-task reductions in skin temperature across spinal regions, with a partial return toward baseline within the 2 h observation window (p < 0.01). These findings describe short-term, task-related thermoregulatory responses following sustained work. Conclusions: Even short-term use of electric olive harvesters induces measurable biomechanical and thermophysiological stress. The integrated use of myotonometry and infrared thermography provides a sensitive, field-adaptable framework for early ergonomic risk detection and prevention of work-related musculoskeletal disorders in agriculture. Full article

Hand localization in cluttered industrial environments remains challenging due to variations in appearance and the gap between synthetic and real-world data. Domain randomization addresses this “reality gap” by intentionally introducing randomized and unrealistic visual features in simulated scenes, encouraging neural networks to focus on essential domain-invariant cues. In this study, we applied domain randomization to generate a synthetic Red-Green-Blue–Depth (RGB-D) dataset for training multimodal instance segmentation models, with the aim of achieving color-agnostic hand localization in complex industrial settings. We introduce a new synthetic dataset tailored to various hand detection tasks and provide ready-to-use pretrained instance segmentation models. To enhance robustness in unstructured environments, the proposed approach employs multimodal inputs that combine color and depth information. To evaluate the contribution of each modality, we analyzed the individual and combined effects of color and depth on model performance. All evaluated models were trained exclusively on the proposed synthetic dataset. Despite the absence of real-world training data, the results demonstrate that our models outperform corresponding models trained on existing state-of-the-art datasets, achieving higher Average Precision and Probability-Based Detection Quality. Full article

With the rapid advancement in information technology, the digital twin and smart assembly process simulation have become an integral part of the design and manufacturing of high-precision products. However, conventional Tunnel Boring Machine (TBM) cutterhead design and on-site assembly planning remain largely experience-driven and fragmented, with limited interoperability between geological characterization, structural verification, and constructability validation. This study proposes a digital twin-driven framework for TBM cutterhead design optimization and assembly process simulation that integrates geology-aware design inputs, BIM-based information modelling, FEM-based structural assessment, and immersive virtual environments within a unified virtual–physical workflow. To ensure consistent data exchange across platforms, an IFC4.3-compliant ontology is established using a non-intrusive property-set (Pset) extension strategy to represent cutterhead components, geological parameters, FEM load cases/results, and assembly tasks. Tunnel-scale stress analysis and cutter–rock interaction modelling are used to define project-representative cutter loading envelopes, which are mapped to a high-fidelity cutterhead FEM model for iterative structural refinement. The optimized configuration is then transferred to a game-engine/VR environment to support full-scale design inspection and assembly rehearsal, followed by manufacturing and field deployment with bidirectional feedback. To validate the proposed framework, an implementation case study of a deep hard-rock tunnelling project is presented where five design iterations were tracked across BIM–FEM–VR and nine constructability issues detected and resolved prior to assembly. The results indicate that the proposed digital twin approach strengthens traceability from geology to loading to structural response, reduces localized stress concentration at critical interfaces, and improves assembly readiness for complex tunnelling projects. Full article