Developing polymer composites with improved mechanical and thermal properties requires strategies to overcome the problem of agglomeration and weak interfacial interactions of carbon nanofillers. This paper presents an effective strategy for the covalent functionalization of graphene oxide (GO) with melamine to create high-performance epoxy nanocomposites. The functionalization results in the formation of nitrogen-containing heterocyclic structures on the GO surface, as confirmed by FTIR and Raman spectroscopy. The addition of the obtained modified filler (mel-GO) into the epoxy matrix provides a synergistic effect: the melamine amino groups catalytically accelerate curing, reducing the gelation time from 146 to 48 min and increasing the maximum self-heating temperature from 94 to 122 °C, thus indicating enhanced interfacial interaction. This interaction results in a remarkable overall improvement in mechanical properties: tensile and flexural strengths increase by more than 20%, and elastic moduli by 31% and 58%, respectively, compared to the composite containing unmodified GO. At the same time, impact strength (from 14 to 23 kJ/m2) and hardness (up to 87 Shore D) increase. A key achievement is a dramatic increase in thermal and thermal-oxidative stability: the onset temperature of decomposition (T5%) increases by 27 °C, the half-decomposition temperature (T50%) by 45 °C, and the thermal stability index (THRI) increases from 119.3 to 128.9 °C. A more than twofold increase in coke residue yield (to 9.29%) and an increase in the Vicat softening point to 175 °C confirm the formation of an effective thermally stabilizing barrier layer due to the combined action of nitrogen-containing groups and dispersed graphene flakes. The proposed approach to functionalizing graphene oxide with melamine opens the way for the creation of next-generation epoxy composites with a record-breaking combination of strength, impact toughness, and thermal stability for applications in aerospace, electronics, and composite structures operating under extreme conditions.

In human-machine (autonomous vehicles and robots) interaction scenarios, pedestrians often appear in groups. Pedestrian groups provide richer information compared to individuals, which helps address occlusion problems in pedestrian-machine interactions. However, the randomness and spatiotemporal complexity of pedestrian activity make pedestrian group activity recognition (PGAR) a highly challenging task. This article provides a detailed description of the PGAR task. For the first time, a definition of pedestrian group and activity for autonomous vehicles and robots is provided. Existing datasets and methods are systematically summarized. Furthermore, the unique challenges and trends in PGAR for autonomous vehicles and robots are outlined. Although some related surveys have been published, there has not yet been a survey specifically focused on PGAR in autonomous driving and robotics scenarios. Therefore, the goal of this article is to narrow the gap in this topic and provide a comprehensive reference for researchers in this field.

Density results using the method of fundamental solutions for a fluid-structure interaction problem

Soliton–mean flow interaction problem for a non-convex system with quadratic-cubic nonlinearity in the stratified fluid

Viscoelastic floating membranes can be used as flexible wave breakers to protect coastal and offshore structures or as flexible wave energy converters. Despite their potential, the role of viscoelastic floating membranes in optimally harvesting or dissipating wave energy remains largely unexplored, particularly regarding how spatially varying material properties influence their performance. To address this gap, we develop an adjoint-based PDE-constrained optimization framework, built on a monolithic finite element formulation of the coupled fluid-structure interaction problem, to investigate and optimize the viscoelastic properties of floating membranes. This methodology enables a systematic optimization of design parameters such as the mass, tension, and damping, which govern the response of the membrane at different wave conditions. In this study we demonstrate that the proposed methodology allows for the optimization of homogeneous and inhomogeneous properties of membranes for different wave excitation frequencies, leading to significant improvements in energy absorption. The framework is implemented in Julia using the Gridap package ecosystem, which enables automatic differentiation of adjoints and avoids the need to derive complex adjoint formulations.

This article is devoted to researching the specifics of contemporary marimba performance in ensemble music-making, which underwent active development in the second half of the 20th century and early 21st century and became one of the key trends in percussion art. Despite the growing interest of composers and performers in the marimba, there are still few scientific works in Ukrainian musicology that comprehensively analyze its role in collective performance. The aim of the study is to identify and characterize the main performance and artistic-stylistic features of contemporary marimba playing in an ensemble context, as well as to clarify its role in the formation of new sound and communicative possibilities of a percussion ensemble. The research methodology is based on a comprehensive approach: the historical and cultural method is used to trace the evolution of the ensemble use of the marimba; the systemic and analytical method is used to generalize the performance techniques and structural features of the instrument; and elements of semiotic analysis are used to interpret timbral and expressive means. The article shows that the marimba occupies a special place in modern percussion ensembles due to its wide range, rich timbral nuances, and ability to integrate into both traditional and experimental forms of music-making. The article shows that the marimba occupies a special place in contemporary percussion ensembles due to its wide range, rich timbral nuances, and ability to integrate into both traditional and experimental forms of music-making. The article examines the problems of interaction between performers in an ensemble, the specifics of the repertoire, and innovative sound production techniques. It is proven that contemporary ensemble practice with the marimba is characterized by a synthesis of acoustic and electroacoustic solutions, the expansion of spatial models of instrument placement, and the use of intergenre and intermedia means. The results of the study allow us to outline the prospects for further research on the domestic school of ensemble performance on the marimba against the backdrop of the international musical context.

The understanding of gestures and the synthesis of choreography can be viewed as two distinct sides of the human-AI interaction problem, which cannot be viewed as complementary and must be addressed through joint modeling of perception, synthesis, and real-time interaction. An interactive multimodal neural architecture consisting of spatial-temporal gesture encoding, latent motion representation learning, and style-conditioned choreography synthesis is proposed to facilitate end-to-end transfer of human movement from sense to expressive synthesized movement. The semantic consistency constraints in joint optimization will be used to ensure consistency between the perceived gesture intent and the synthesized choreography, while an edge cloud deployment approach will be utilized to facilitate interactive latency and energy-efficient execution. The experimental evaluation on benchmark datasets and live co-creative applications demonstrate high recognition accuracy, smooth and diverse motion synthesis, and successful semantic agreement and consistency in co-creating real-time settings. The formal user study also reveals high levels of perceptual realism, sense of expression, usability, and creative satisfaction, which verifies the framework as an excellent collaborative partner and not a passive generative tool. Managerial analysis Networks have lower production costs, scalable deployment opportunities, and therapeutic engagement of benefits in the areas of creative media, rehabilitation, and social robotics. The findings place gesture-based creative AI as a promising foundation of embodied intelligent interaction, and future research directions include the integration of emotion in creative choreography synthesis, adaptive reinforcement learning co-creation, and extreme low-latency edge synthesis

We present a theoretical analysis of a gyroscopic wave energy converter (GWEC), which generates electricity via the precession induced by the flywheel’s rotation and the pitch motion of a floating body. The coupled wave–body–gyroscope interaction problem is formulated under the assumptions of linear waves and resulting linear motions of both the floating body and the gyroscope. Within this framework, we identify the optimal control parameters that maximise the energy absorption efficiency. The analysis reveals that the GWEC can theoretically achieve the maximum energy absorption efficiency of 1/2 at any wave frequency through appropriate tuning of the flywheel’s rotational speed and the generator parameters. The derived theory is verified through numerical simulations in both the frequency and time domains. Furthermore, time-domain simulations incorporating the nonlinear gyroscopic response are conducted to assess the limitations of the linear gyroscopic model. These findings provide valuable insights for the future design of wave energy harvesting technologies.

Objective: To investigate the association between cosleeping and emotional-behavioral problems in preschool children, and analyze their differences across sleep anxiety status. Methods: A data analysis was conducted from June 2024 to October 2025. The prospective cohort data were derived from the Shanghai Children's Health, Education and Lifestyle Evaluation-Preschool cohort (SCHEDULE-P). Using a stratified cluster random sampling method, 20 899 newly enrolled preschool children from 191 kindergartens in Shanghai were recruited in November 2016 completed the first survey. Follow-up surveys were carried out in April 2018 and April 2019, during which parents completed structured questionnaires. Children's emotional-behavioral problems including emotional problems, conduct problems, hyperactivity problems, peer relationship problems, and prosocial behavior problems were assessed using the strengths and difficulties questionnaire (SDQ). Children's sleep quality was evaluated by the children's sleep habits questionnaire (CSHQ). The sleep anxiety status was evaluated by the sleep anxiety subscale and cosleeping status was reported via the question "Does the child sleep alone in their own bed?" of CSHQ. Mixed-effects Logistic regression models were used to examine the relationship between cosleeping and emotional-behavioral problems. A moderation model was constructed by including the interaction term between cosleeping and sleep anxiety, and further stratified analysis were conducted by dividing children into 2 groups based on the presence or absence of sleep anxiety, to clarify the association between cosleeping and emotional-behavioral problems within each group. Results: After excluding invalid data, a total of 15 679 children were included in the final analysis, including 8 082 boys (51.5%) and 7 597 girls (48.5%). The age was (3.73±0.29) years and 75.7% (11 872/15 679) of the children practiced cosleeping at the first survey. The mixed-effects Logistic regression model showed that preschool children who practiced cosleeping had a higher risk of emotional-behavioral problems compared with those who slept independently (OR=1.12, 95%CI 1.03-1.21, P=0.006), particularly in the domains of emotional problems, hyperactivity problems, and prosocial behavior problems (all P<0.01). The moderation model indicated statistically interaction effects between cosleeping and sleep anxiety on the total score and all subscales of SDQ (all P<0.01). Simple effect analyses revealed that among children with sleep anxiety, cosleeping was associated with lower risks of emotional-behavioral problems (OR=0.87, 95%CI 0.77-0.99, P=0.033), especially in peer relationship and conduct problems (both P<0.05).In contrast, among children without sleep anxiety, cosleeping was associated with higher risks of peer interaction and prosocial behavior problems (both P<0.05). Conclusions: The association between cosleeping and emotional-behavioral problems differs depending on the presence of sleep anxiety in preschool children. For children with sleep anxiety, cosleeping is linked to fewer emotional-behavioral problems; whereas for children without sleep anxiety, cosleeping tends to be associated with more emotional-behavioral problems.

A velocity reconstruction algorithm for immersed smoothed point interpolation method in fluid-structure interaction problems

Bullying and victimization are pervasive problems in adolescent social interactions, often creating a cycle where victims may become perpetrators and vice versa. This study investigated how malevolent creativity contributes to this dynamic, and how emotion regulation self-efficacy (ERSE) influences these relationships. Drawing on a sample of middle school students (grades 7th-9th), we explored the bidirectional relationship among bullying, victimization, and malevolent creativity, as well as the role of ERSE, using a moderated network model. Our findings revealed that malevolent creativity is positively associated with bullying behaviors and victimization, potentially escalating aggressive interactions. Importantly, ERSE emerged as a critical moderator, attenuating the pathway from victimization to malevolent creativity by enabling better management of negative emotions, such as despondency-distress and anger-irritation. Findings indicated that malevolent creativity contributes significantly to the dynamics of bullying and victimization. This connection emphasizes the importance of early recognition and intervention strategies. By improving emotional regulation self-efficacy, particularly in relation to despondency-distress, we can disrupt the cycle involving malevolent creativity, victimization, and bullying, helping to promote more constructive peer interactions and a substantial decrease in the prevalence of bullying behaviors in school environments.

Introduction This article proposes a new dataset for Named Entity Recognition based on PubMed articles and aiming to address the problem of Herb-Drug Interactions. It aims to offer a new dataset for recognizing herb-drug interaction entities, including contextual information. Background Machine learning and Deep learning provide users with powerful tools for task automation, but require large quantities of data to perform well. In the field of Natural Language Processing, training Deep Learning models requires the annotation of large corpora of text. While some corpora exist in medical literature, each specific task requires an adapted corpus. Methods The dataset was tested using a classical Named Entity Recognition pipeline, as well as new possibilities offered by generative AI. Results The dataset proposes annotated sentences of around a hundred articles and covers 15 entities, including herbs, drugs, and pathologies, as well as contextual information, such as cohort composition, patient information, or pharmacological clues. Discussion The study demonstrates that this dataset performs comparably to the DDI (Drug-Drug Interaction) corpus — a standard dataset in the drug Named Entity Recognition — for drug recognition, and performs well on most of the entities. Conclusion : We believe this corpus could help diversify pharmacological Named Entity Recognition.

Industrial gateways serve as critical data aggregation points within the Industrial Internet of Things (IIoT), enabling seamless data interoperability that empowers enterprises to extract value from equipment data more efficiently. However, their role exposes a fundamental trade-off between computational efficiency and prediction accuracy—a contradiction yet to be fully resolved by existing approaches. The rapid proliferation of IoT devices has led to a corresponding surge in network traffic, posing significant challenges for traffic forecasting methods, while deep learning models like Transformers and GNNs demonstrate high accuracy in traffic prediction, their substantial computational and memory demands hinder effective deployment on resource-constrained industrial gateways, while simple linear models offer relative simplicity, they struggle to effectively capture the complex characteristics of IIoT traffic—which often exhibits high nonlinearity, significant burstiness, and a wide distribution of time scales. The inherent time-varying nature of traffic data further complicates achieving high prediction accuracy. To address these interrelated challenges, we propose the lightweight and theoretically grounded DOA-MSDI-CrossLinear framework, redefining traffic forecasting as a hierarchical decomposition–interaction problem. Unlike existing approaches that simply combine components, we recognize that industrial traffic inherently exhibits scale-dependent temporal correlations requiring explicit decomposition prior to interaction modeling. The Multi-Scale Decomposable Mixing (MDM) module implements this concept through adaptive sequence decomposition, while the Dual Dependency Interaction (DDI) module simultaneously captures dependencies across time and channels. Ultimately, decomposed patterns are fed into an enhanced CrossLinear model to predict flow values for specific future time periods. The Dream Optimization Algorithm (DOA) provides bio-inspired hyperparameter tuning that balances exploration and exploitation—particularly suited for the non-convex optimization scenarios typical in industrial forecasting tasks. Extensive experiments on real industrial IoT datasets thoroughly validate the effectiveness of this approach.

This article scientifically analyzes the use of non-traditional approaches in the process of primary education, their role in increasing student activity and their impact on educational effectiveness. It is highlighted that non-traditional methods, such as interactive games, problem situations, creative tasks, STEAM elements, project-based learning and integrative technologies, serve to form students' competencies for independent thinking, communication, and practical activity. The article is based on the person-oriented, activity-based and creativity-stimulating features of non-traditional approaches, unlike traditional teaching.

Abstract A decoupled finite element method based on the scalar auxiliary variable and vector penalty projection approach is constructed and analysed for solving a fluid–fluid interaction problem, which includes two Navier–Stokes equations coupled by some nonlinear interface conditions. The proposed full-discrete scheme is a combination of mixed finite element approximation for spatial discretization, backward Euler scheme for temporal discretization, as well as explicit treatment for the interface conditions, and can penalize for lack of mass conservation. Furthermore, unconditional energy stability is given and error estimates for the fully discrete scheme are showed. Finally, some numerical experiments are provided to illustrate the theoretical results and efficiency of the presented method.

Abstract Three-phase fluid-structure interaction (FSI) problems couple the motion of solid structures and two different fluids, often exhibiting complex dynamics and posing challenges for computational investigation. The main objective of this paper is to present a new computational framework for the modeling, simulation, and application of such problems. To that end, we propose an integrated numerical algorithm that utilizes the volume of fluid method for the two-fluid flow and the immersed boundary method for the fluid-solid interaction, with a special emphasis on three-phase FSI computation in micro-scale settings. To demonstrate our methodology, we conduct a pilot FSI study on cellulose microfibrils in plant cell walls by simulating the interaction between microfibrils and two fluids, pectin and water. We investigate the evolution of the flow fields, the speed and spatial organization of the microfibrils, and the impact of different initial settings on microfibril dynamics. We find that the microfibril-pectin-water interaction leads to transversely oriented microfibrils with respect to the elongation axis of the plant cell. The simulation results indicate that our methodology may provide a novel approach to investigate the complex behavior of microfibrils and gain insights into the intrinsic dynamical properties of such microstructures.

Interactive problem solving has been proposed as an experimental manipulation that significantly increases the success of solving various matchstick algebra problems by allowing solvers to interact with physical representations of the problems. In contrast to this claim, we hypothesized that the influence of interactivity would vary based on the specific sources of difficulty inherent in the problems: perceptual chunks and cognitive constraints. We carried out a conceptual extended replication across three experimental series with conditions of varying degrees of interactivity, but failed to reproduce interactive solutions amongst our participants. A follow-up analysis of motor activity showed that the movements of the solvers did not contribute to chunk decomposition but significantly interfered with the relaxation of higher-level constraints. These findings suggest that motor activity can hinder performance when it does not align with the cognitive demands of the task. We therefore call for a more targeted and problem-specific understanding of how physical interaction contributes to restructuring in insight problem solving.

Abstract Analytical solutions to Fluid-Structure Interaction (FSI) problems are almost absent in the literature. However, they are crucial for validation and convergence analysis of numerical methods, as well as for providing insight into the complex coupling dynamics between fluids and solids. In this paper, we derive two analytical and one semi-analytical solutions for three FSI problems, spanning a class of solutions by varying their geometrical and physical parameters. All solutions exhibit complex nonlinear behaviours, which we compare with numerical simulations using a monolithic method. These three FSI problems are described in the cylindrical coordinates, drawing inspiration from Couette flow, with two of them featuring a moving fluid-solid interface and the third incorporating a nonlinear constitutive solid model. To the best of our knowledge, for the first time, we present FSI problems with analytical solutions that include a moving interface.

In order to solve the dynamic analysis and interactive imaging control problems in the deformation process of bionic soft lenses, dielectric elastomer (DE) actuators are separated from a convex lens, and data-driven eye-controlled motion technology is investigated. According to the DE properties, which are consistent with the deformation characteristics of hydrogel electrodes, the motion and deformation effect of eye-controlled lenses under film prestretching, lens size, and driving voltage, is studied. The results show that when the driving voltage increases to 7.8 kV, the focal length of the lens, whose prestretching λ is 4, and the diameter d is 1 cm, varies in the range of 49.7 mm and 112.5 mm. And the maximum focal-length change could reach 58.9%. In the process of eye controlling design and experimental verification, a high DC voltage supply was programmed, and eye movement signals for controlling the lens were analyzed by MATLAB software (R2023b). Eye-controlled interactive real-time motion and tunable imaging of the lens were realized. The response efficiency of soft lenses could reach over 93%. The adaptive lens system developed in this research has the potential to be applied to medical rehabilitation, exploration, augmented reality (AR), and virtual reality (VR) in the future.

In this investigation, we develop basic methods for the multi-scale analysis of problems in thin porous layers. More precisely, we provide tools for the homogenization of “tangentially” periodic structures, and dimensional reduction letting the layer thickness tend to zero prop ortional to the scale parameter ϵ . A crucial point is the identification of scale limits of sequences v ϵ characterized by uniform a priori estimates with respect to ϵ , arising as solutions of differential equations, like Navier–Stokes system, linear elasticity, or fluid-structure interaction problems, in media with thin layers. Often in such problems, in a first step, the symmetric gradients can be controlled, and Korn’s inequality in porous layers is required to estimate the gradients. We construct controllable pore-filling extensions and use them for the proof of the required Korn-inequalities in L p -spaces. These results are the basis for the derivation of compactness results with respect to two-scale convergence and the characterization of the scale limits. To illustrate the range of application of the developed multi-scale methods, a semi-linear elastic wave equation in a thin periodically perforated layer with an inhomogeneous Neumann boundary condition on the surface of the elastic substructure is treated and a homogenized, reduced system is derived.