The functionality and aesthetic of 3D-printed components can be compromised if visible defects appear on their external surfaces. To overcome this issue, CNC machines were traditionally adopted for milling machining. More recently, industrial robots have been demonstrated to be a valid alternative. This study presents a robotic workstation developed for contouring machining 3D thermoplastic components printed using the material extrusion technology. The workstation adopts a collaborative robot with a novel, custom-designed, and low-cost end-effector made of a powered contouring tool integrated with three load cells for measuring the cutting forces along three perpendicular directions. The tool path planning is defined by a proposed and validated procedure. By a vision algorithm and a touch-stop operation, the 3D CAD model-based tool path is adapted to the current position and orientation of the workpiece. The experimental activity for determining the optimal set of contouring machining parameters (rotational speed, cut depth, and feed rate) and for measuring cutting forces confirms the feasibility of adopting the cobot-based solution for this application and suggests potential improvements for future works.

Despite their widespread use, purely data-driven methods often suffer from overfitting, lack of physical consistency, and high data dependency, particularly when physical constraints are not incorporated. This study introduces a novel data assimilation approach that integrates Graph Neural Networks (GNNs) with optimization techniques to enhance the accuracy of mean flow reconstruction, using Reynolds-averaged Navier–Stokes (RANS) equations as a baseline. The method leverages the adjoint approach, incorporating RANS-derived gradients as optimization terms during GNN training, ensuring that the learned model adheres to physical laws and maintains consistency. Additionally, the GNN framework is well-suited for handling unstructured data, which is common in the complex geometries encountered in computational fluid dynamics. The GNN is interfaced with the finite element method for numerical simulations, enabling accurate modeling in unstructured domains. We consider the reconstruction of mean flow past bluff bodies at low Reynolds numbers as a test case, addressing tasks such as sparse data recovery, denoising, and inpainting of missing flow data. The key strengths of the approach lie in its integration of physical constraints into the GNN training process, leading to accurate predictions with limited data, making it particularly valuable when data are scarce or corrupted. Results demonstrate significant improvements in the accuracy of mean flow reconstructions, even with limited training data, compared to analogous purely data-driven models.

The results of Section 3.1 of the 2017 paper “Isomorphism Theorems between Models of Mixed Choice” need an additional assumption when $\bullet$ is “$1$.” If $\bullet$ is nothing or “$\leq 1$,” no change is needed. Also, the mistake only applies to the angelic cases, namely to the maps $r_{{\mathtt {A}}{\mathtt {P}}}$ and $s^\bullet _{{\mathtt {A}}{\mathtt {P}}}$; the demonic cases $r_{{\mathtt {D}}{\mathtt {P}}}$ and $s^\bullet _{{\mathtt {D}}{\mathtt {P}}}$ are unaffected. If $\bullet$ is “$1$,” and in the angelic cases, instead of just assuming that $\mathcal L X$ is locally convex, we need to additionally assume that $X$ is compact, or that $\mathcal L X$ is locally convex-compact, sober, and topological – for example, if $X$ is core-compact – or that $X$ is LCS-complete, namely, a homeomorph of a $G_\delta$ subspace of a locally compact sober space.

We develop the theory of limits and colimits in $\infty$-categories within the synthetic framework of simplicial homotopy type theory established by Riehl and Shulman. We also show that in this setting, the limit of a family of spaces can be computed as a dependent product.

Consulting dictionaries during writing requires time and cognitive resources. ColloCaid, a writing assistance prototype freely available online, was designed to minimize the cognitive strain on writers by embedding a collocation database within the writing environment. Usability surveys have shown ColloCaid can indeed help. In this study, we go beyond user perceptions. Using authentic excerpts of student academic writing by 27 advanced L2 English speakers, we analysed (1) the lexical coverage of the tool, (2) the collocation changes prompted by the tool, (3) the reasons behind decisions to revise collocations, (4) the effect of revisions prompted by ColloCaid, and (5) the participants’ perceptions of using the tool to revise authentic writing assignments. Our findings indicate that ColloCaid offered good academic collocation coverage, that the participants tended to accept its collocation prompts with discernment, and that the revisions made resulted in more fluent texts overall.

Human–machine compatibility and collaborative control for stroke patients utilizing lower limb rehabilitation robots have attracted considerable research attention. As a highly human–machine-coupled system, ensuring adequate compliance and safety is fundamental to efficient and comfortable rehabilitation. Therefore, this paper first quantifies human–machine contact interactions, proposes a human–machine coupling dynamics modeling method, and identifies the robot’s dynamic inertia parameters and human lower limb parameters. Second, a dual closed-loop controller for the rehabilitation robot is designed. Based on the bottom position control, an adaptive admittance control algorithm is proposed that employs the root-mean-square propagation (RMSprop) algorithm to tune the adaptive gain. In rehabilitation training, the controller can adaptively adjust the admittance parameters according to the human–machine interaction force to achieve responsiveness to the dynamic changes of the human–machine system. The experimental results of the control system show that the human–machine cooperative control performance is significantly improved, the maximum joint angle error is reduced by more than 40.9%, and the maximum human–machine interaction force is reduced by more than 19.4%.

Rehabilitation treatment is often labor-intensive and time-consuming, but it also lacks quantitative and objective assessment. With regard to the matter of balance rehabilitation machines, the continuous advancement of parallel robot technology provides new solutions for balance rehabilitation. However, these robots have inherent limitations, including a confined workspace, excessive height, a complex structure, and unstable movement due to singularity in workspace. Therefore, this study presents a new 3-2PUS double-triangular construction mechanism with six degrees of freedom for use in balance rehabilitation therapy. First, the forward and inverse kinematic models are established, and then the Newton–Raphson method is employed to resolve the forward kinematics. Subsequently, the velocity model is analyzed and its singular configuration is determined. Finally, the workspace of the 3-2PUS parallel mechanism is delineated, and the findings indicate that its structure is compact and that the workspace is free of singularities. This ensures that the rehabilitative devices will remain stable throughout the rehabilitation process, thus preventing any additional injuries that might otherwise result from unstable movement. To validate the study of the full parallel mechanism, a series of simulations is conducted using computational analysis software. Based on the analysis results, a prototype of a balance rehabilitation parallel manipulator is presented.