In this paper, we study discrepancy questions for spanning subgraphs of $k$-uniform hypergraphs. Our main result is that, for any integers $k \ge 3$ and $r \ge 2$, any $r$-colouring of the edges of a $k$-uniform $n$-vertex hypergraph $G$ with minimum $(k-1)$-degree $\delta (G) \ge (1/2+o(1))n$ contains a tight Hamilton cycle with high discrepancy, that is, with at least $n/r+\Omega (n)$ edges of one colour. The minimum degree condition is asymptotically best possible and our theorem also implies a corresponding result for perfect matchings. Our tools combine various structural techniques such as Turán-type problems and hypergraph shadows with probabilistic techniques such as random walks and the nibble method. We also propose several intriguing problems for future research.

Variable topological space robots are essential for providing adaptability and flexibility, enabling the robot to adjust its morphology to perform a range of tasks in the unstructured environment of space. However, impact is a common consequence of topology transformation in space robotics, which may lead to irreversible damage, such as the shedding of solid lubrication on joints. Nevertheless, determining the precise force-time relationships of such impacts poses significant challenges, especially when accounting for various connection mechanisms. In this work, a docking strategy that optimizes the manipulator’s joint angle configuration to minimize the impulse when the topology changes is proposed. First, an estimation technique is developed to quantify the impulse generated by topology transformation, employing spatial operator algebra and generalized momentum balance equations. Based on this model, the impulse minimization is modelled as a bilevel optimization problem, which decomposes a complex multipolar problem into two simpler subproblems. Although this optimization model may compromise computational efficiency, it increases the probability of achieving an optimal solution. To address this, a bilevel solution strategy based on a heuristic algorithm is proposed. In this framework, the lower level uses particle swarm optimization to determine the global optimum, while the upper level adopts simulated annealing to enhance computational speed. Finally, simulations are conducted to validate the proposed approach. Results demonstrate that the proposed method substantially reduces impulse.

Due to the effects of tolerance, design, and manufacturing deviations, there are clearances in the revolute joints of mechanical arms. These clearances can easily lead to system impacts and vibrations, resulting in a decrease in dynamic performance and affecting the trajectory tracking accuracy of the end effector. The existing dynamic models of mechanisms with clearance in revolute joints lack comprehensiveness, universality, and systematicity, and have not addressed the impact of joint reaction forces within clearance revolute joints on the system. The impact collision problem of the revolute joints with clearance was systematically, accurately, and comprehensively modeled and simulated in this study based on multibody dynamics theory. Based on Hertz’s elastic theory, the LuGre friction model, and joint reaction forces, this paper constructs constraint and mechanical models of revolute joints with clearance based on the theory of multibody dynamics. To facilitate multibody dynamics analysis, the collision impact direction matrix is proposed and used for the first time to transform the mechanical model of revolute joints with clearance into external forces. The dynamic models of mobile parallel and double serial manipulators are then constructed. Through numerical simulations on different clearance amounts, tracking trajectories, and load parameters, the impact of revolute joint clearances on system dynamic performance is analyzed. The engineering significance of this research in dynamic analysis of mobile parallel manipulators under imperfect revolute joint conditions is also discussed.

Limited research has examined the quality of language MOOCs and no existing instrument has been developed to gauge learners’ evaluation of LMOOC quality. This study develops an LMOOC Quality Evaluation Scale (LQES) and validates it in the Chinese context, which has the largest number of LMOOC learners in the world. The data were collected from 2,315 LMOOC learners in China using a mixed-method approach. Development and validation of the scale involved (1) generation of an initial item pool based on a semi-structured interview and literature review, (2) refinement of scale items through consultation of LMOOC experts and a focus group interview, (3) exploration of the factor structure of the scale using exploratory factor analysis, and (4) validation and confirmation of the final scale using confirmatory factor analysis. A four-factor model, comprising Instructional Design, L2 Teachers’ Competence, Teaching Implementation, and Technical Support, emerged and was validated. The 26-item LQES provides an original and comprehensive framework for understanding the complexities of LMOOC quality. This study highlights the critical factors underpinning the evaluation of LMOOC quality and paves the way for further refining of the instrument in future research.

In this paper, the notion of structural completeness is explored in the context of a generalized class of superintuitionistic logics that also involve systems that are not closed under uniform substitution. We just require that each logic must be closed under $D$-substitutions assigning to atomic formulas only $\vee$-free formulas. For these systems, we introduce four different notions of structural completeness and study how they are related. We focus on superintuitionistic inquisitive logics that validate a schema called Split and have the disjunction property. In these logics, disjunction can be interpreted in the sense of inquisitive semantics as a question-forming operator. It is shown that a logic is structurally complete with respect to $D$-substitutions if and only if it validates Split. Various consequences of this result are explored. For example, it is shown that every superintuitionistic inquisitive logic can be characterized by a Kripke model built from $D$-substitutions. We also formulate an algebraic counterpart of this result that says that the Lindenbaum–Tarski algebra ${\mathscr{H}}$ of any inquisitive logic can be embedded into the Heyting algebra formed from left ideals of endomorphisms on ${\mathscr{H}}$. Additionally, we resolve a conjecture concerning superintuitionistic inquisitive logics due to Miglioli et al. and show that a false conjecture about superintuitionistic logics due to Minari and Wroński becomes true in the broader space of regular generalized superintuitionistic logics.

Exception handling has been successfully proposed in the past years as a simple yet powerful software engineering tool to promote modularity and decoupling, while also preserving robustness. Multi-agent systems (MAS) and organizations (MAOs), in turn, offer powerful abstractions to build distributed systems; current models and methodologies, however, fall short in addressing exception handling in a systematic way, not considering exceptions as part of their design. Thus, the problem is usually approached by ad hoc solutions that hamper code modularization and decoupling. In this work, we outline a vision of how exception handling in MAS can be granted by design. We present an extension of the organizational model and infrastructure adopted in JaCaMo, that explicitly encompasses the notion of exception as a first-class element in the design of an organization. Relying on such a model, we propose an exception handling mechanism that is seamlessly integrated with organizational concepts, such as responsibilities, goals, and norms. In an organization, besides responsibilities for organizational goals, we propose to specify also responsibilities for managing exceptions, that is, for providing feedback about the context in which exceptions occur, and for handling it.

In this paper, an improved U-net welding engineering drawing segmentation model is proposed for the automatic segmentation and extraction of sheet metal engineering drawings in the process of mechanical manufacturing, to improve the cutting efficiency of sheet metal parts. To construct a high-precision segmentation model for sheet metal engineering drawings, this paper proposes a U-net jump structure with an attention mechanism based on the Convolutional Attention Module (CBAM) attention mechanism. At the same time, this paper also designs an encoder jump structure with vertical double pooling convolution, which fuses the features after maximum pooling+convolution of the high-dimensional encoder with the features after average pooling+convolution of the low-dimensional encoder. The method in this paper not only improves the global semantic feature extraction ability of the model but also reduces the dimensionality difference between the low-dimensional encoder and the high-dimensional decoder. Using Vgg16 as the backbone network, experiments verify that the IoU, mAP, and Accu indices of this paper’s method in the welding engineering drawing dataset segmentation task are 84.72%, 86.84%, and 99.42%, respectively, which are 22.10, 19.09 and 0.05 percentage points higher compared to the traditional U-net model, and it has a relatively excellent value in engineering applications.

This research proposes an adaptive human-robot interaction (HRI) that combines voice recognition, emotional context detection, decision-making, and self-learning. The aim is to overcome challenges in dynamic and noisy environments while achieving real-time and scalable performance. The architecture is based on a three-stage HRI system: voice input acquisition, feature extraction, and adaptive decision-making. For voice recognition, modern pre-processing techniques and mel-frequency cepstral coefficients are used to robustly implement the commands. Emotional context detection is governed by neural network classification on pitch, energy, and jitter features. Decision-making uses reinforcement learning where actions are taken and then the user is prompted to provide feedback that serves as a basis for re-evaluation. Iterative self-learning mechanisms are included, thereby increasing the adaptability as stored patterns and policies are updated dynamically. The experimental results show substantial improvements in recognition accuracy along with task success rates and emotional detection. The proposed system achieved 95% accuracy and a task success rate of 96%, even against challenging noise conditions. It is apparent that emotional detection achieves a high F1-score of 92%. Real-world validation showed the system’s ability to dynamically adapt, thus mitigating 15% latency through self-learning. The proposed system has potential applications in assistive robotics, interactive learning systems, and smart environments, addressing scalability and adaptability for real-world deployment. Novel contributions to adaptive HRI arise from the integration of voice recognition, emotional context detection, and self-learning mechanisms. The findings act as a bridge between the theoretical advancements and the practical utility of further system improvements in human-robot collaboration.