Search Results (612)

The integration of Generative Artificial Intelligence (GAI) into software design tools has transformed the early stages of mobile application development, particularly prototype creation from natural-language prompts. This study evaluates the usability and effectiveness of GAI-assisted prototyping tools for generating high-fidelity mobile application prototypes. A controlled laboratory usability study was conducted in which undergraduate Information Technology Engineering students used and evaluated four widely adopted prototyping platforms: Figma, Uizard, Visily, and Stitch. Participants employed these tools to recreate mobile interfaces corresponding to the interaction model of the Duolingo application. The System Usability Scale (SUS) was used to assess perceived usability and effectiveness from the users’ perspective. The results indicate that all evaluated tools enabled rapid prototype generation; however, significant differences emerged in usability, structural fidelity, and perceived control. Figma and Stitch achieved the highest usability scores and demonstrated greater alignment with the reference prototype (82.86 and 80.36, respectively). Visily achieved a favorable usability score (78.57), while Uizard obtained a moderate score (67.14). Although Uizard and Visily exhibited strong automation capabilities and faster initial generation, their outputs required additional manual refinement to achieve higher fidelity and customization. Participant feedback emphasized the importance of output quality, responsiveness, and foundational design knowledge in achieving satisfactory results. Overall, the findings suggest that current GAI-based prototyping tools are effective and valuable in real-world software development contexts. However, their effectiveness appears closely related to the degree of user control, responsiveness, and the ability to iteratively refine AI-generated interface components. Full article

Electroencephalogram (EEG) signals serve as the core information carrier for brain–computer interfaces (BCIs); however, their highly non-stationary nature, extremely low signal-to-noise ratio, and significant inter-individual variability pose considerable challenges for signal decoding. Existing deep learning methods struggle to strike a balance between multi-scale, fine-grained feature extraction and efficient long-range temporal modeling. To overcome this limitation, this study proposes a novel deep cascaded architecture, MSResBiMamba, which deeply integrates multi-scale spatiotemporal feature learning with cutting-edge long-sequence modeling techniques. The model first utilizes an enhanced multi-scale spatiotemporal convolutional network (MS-CNN) combined with a SE-channel attention mechanism to adaptively extract local multi-band features and dynamically suppress redundant artefacts. Subsequently, it innovatively introduces an enhanced bidirectional Mamba (Bi-Mamba) module to efficiently capture non-causal long-range temporal dependencies with linear computational complexity, whilst cascading multi-head self-attention mechanisms to establish global higher-order feature interactions. Extensive experiments on the BCI Competition IV-2a dataset demonstrate that MSResBiMamba achieves outstanding classification performance in multi-class motor imagery tasks, significantly outperforming traditional methods and existing state-of-the-art neural networks. Ablation studies and t-SNE visualisations further confirm the model’s robustness in feature decoupling and cross-subject applications, providing a high-precision, high-efficiency decoding solution for BCI systems. Full article

With the shortage of natural aggregates and the massive accumulation of iron tailings (ITs) solid waste restricting the sustainable development of asphalt pavement engineering, replacing natural aggregates with ITs has become a promising low-carbon solution with prominent economic and social benefits. However, the poor interfacial adhesion between ITs and asphalt severely restricts the engineering application of tailings, and the micro-interaction mechanism at their interface still lacks systematic clarification, which is the key research gap addressed in this work. Different from conventional macro road performance tests, this study innovatively combined molecular dynamics (MD) simulation with microscopic characterization, including Fourier transform infrared spectroscopy (FT-IR) and atomic force microscopy (AFM), to comprehensively reveal the interfacial interaction mechanism between ITs and asphalt at the molecular and microscales. The results indicate that asphalt molecules exhibit higher aggregation concentration and diffusivity on Al₂O₃ and Fe₂O₃ surfaces than on SiO₂ surfaces, proving stronger interfacial interaction between asphalt and iron-rich oxide minerals. Moderate temperature optimizes the adhesion performance of asphalt with Al₂O₃ and Fe₂O₃, while the interfacial bonding of asphalt on CaCO₃ and SiO₂ weakens as temperature rises. The silane coupling agent KH-550 can effectively react with acidic minerals, SiO₂ minerals in ITs, which significantly increases the concentration, diffusion coefficient, and distribution uniformity of asphalt molecules at the interface. FT-IR results verify that the combination of ITs and asphalt mainly relies on physical adsorption without generating new chemical bonds. AFM tests further confirm that alkaline minerals improve the surface roughness of asphalt mastic, and KH-550 greatly enhances the micro-adhesion force of the interface. The novelty of this work lies in clarifying the mechanism of typical mineral components in ITs and revealing the modification enhancement law of silane coupling agent and alkali minerals at the micro level. This study provides a scientific theoretical support for the high-value engineering utilization of ITs in asphalt pavement, and offers a reference for optimizing the interfacial modification design of solid waste aggregate. Full article

Anthocyanins are naturally occurring flavonoid pigments widely distributed in plants and are recognized for their antioxidant and anti-inflammatory activities. However, the molecular mechanisms underlying their potential immunomodulatory effects remain poorly characterized, particularly regarding their direct interactions with key signaling cytokines. In this study, a set of selected anthocyanins was investigated using a hierarchical computational workflow targeting three major pro-inflammatory cytokines: interleukin-2 (IL-2), interleukin-17 (IL-17), and tumor necrosis factor-α (TNF-α). Molecular docking analyses identified primulin and antirrhinin as the most favorable binders, forming stabilizing hydrogen bonds and hydrophobic interactions within predicted cytokine interaction interfaces. To further assess the stability of these complexes, molecular dynamics simulations were performed under near-physiological conditions. Trajectory analyses demonstrated stable ligand–protein interactions and persistent intermolecular contacts throughout the 100 ns simulation period. These findings provide molecular-level insights into anthocyanin–cytokine interactions and highlight their potential relevance for modulating inflammatory signaling pathways. Full article

Concrete structures are highly vulnerable to fire exposure, which accelerates the degradation of mechanical properties and may lead to partial or total structural failure. Externally bonded carbon fiber-reinforced polymer (CFRP) systems are widely used for post-fire strengthening; however, the bond behavior at the interfaces between CFRP and fire-damaged concrete, particularly under different cooling conditions, is not yet fully understood. In this study, the bond behavior was investigated experimentally and theoretically. Double-lap joint tests of thirty-nine specimens were conducted, including three unheated control specimens and thirty-six specimens exposed to temperatures of 200 °C, 400 °C, and 600 °C for durations of one and two hours. Two cooling methods, natural air cooling and water cooling, were applied prior to CFRP bonding. The results indicated that bond strength increased under exposure conditions of no more than 400 °C, whereas a significant reduction was observed at 600 °C. Water cooling resulted in lower bond strength compared with air cooling, while longer exposure durations improved bond performance under certain thermal conditions. The reasons behind the phenomena were analyzed in detail. Based on the experimental results, an analytical model for predicting the bond strength at the interfaces between fire-damaged concrete and CFRP sheets was developed. The model can account for the effects of peak temperatures, exposure durations, and cooling methods, and demonstrated high predictive accuracy (R² = 0.94). The findings provide valuable insight into CFRP–concrete interaction after fire exposure and offer practical guidance for the assessment and rehabilitation of fire-damaged concrete structures. Full article

Understanding the genetic changes that allow avian influenza A viruses (IAVs) to switch their natural hosts and establish productive infection in humans is important for pandemic risk assessment. Adaptations in the IAV polymerase are required to overcome species-specific restrictions imposed by host ANP32 proteins. Notably, avian virus polymerase is generally only poorly supported by human ANP32 proteins due to species-specific differences. Consequently, efficient polymerase adaptation to the binding interface of human ANP32 requires distinct amino acid changes, such as PB2 E627K. A separate adaptation, PB2 M631L, has recently been reported in mammalian-adapted IAV; however, its functional role across divergent viral lineages and its relationship to host ANP32-dependent adaptation remain incompletely defined. Here, we examine PB2 M631L in the polymerases of a 1918 pandemic strain, a recombinant contemporary H1N1pdm09, and a recent clade 2.3.4.4b H5N1 virus. Using polymerase activity and protein-interaction assays, we show that PB2 M631L enhances polymerase activity and ANP32 binding in human—but not avian—contexts, and that this effect is conserved across multiple viral backgrounds. In H1N1pdm09, PB2 M631L also increased virus replication in mammalian cells. These findings indicate that PB2 M631L contributes to enhanced polymerase compatibility with human ANP32 proteins and are consistent with a role in adaptation across multiple influenza virus lineages. Our results highlight how analysis of historical pandemic strains can inform risk assessment for future emerging viruses. Full article

This paper presents a theoretical approach based on the FSDT to study the acoustic vibration performance of rib-stiffened PVC foam sandwich structures with reinforcing and weakening phases when submerged in water. The complex core layer with reinforcing and weakening phases is homogenized to an equivalent orthotropic layer. Building upon this framework, the governing equations of motion for rib-stiffened PVC foam sandwich structures under the boundary conditions of a simply supported type are derived, incorporating the coupling interaction between the reinforcing ribs and the sandwich plates. Considering the influence of the underwater environment, with the Helmholtz equation governing the continuity of the acoustic pressure field and the Euler equation regulating the fluid–structure interaction interface continuity, the Navier method is subsequently employed to solve for the natural frequencies and acoustic vibration responses. For the purpose of verifying the proposed approach, the predicted results are contrasted with both the literature-derived data and numerical simulation results. Finally, parametric research is further conducted to explore the effect of the parameters of the rib and core layers on the underwater acoustic vibration characteristics. The conclusions drawn from this study can provide meaningful guidance for engineering design and optimization of such rib-stiffened sandwich structures, incorporating both reinforcing and weakening phases in underwater engineering applications. Full article

Transmission towers, as critical infrastructure in power systems, are frequently threatened by multiple hazards such as strong winds and flood scour. Traditional structural health monitoring methods face limitations in data feedback timeliness and mechanical interpretation, making real-time condition awareness and early warning under disaster scenarios challenging. To address these issues, this paper proposes a digital twin framework for transmission tower structures, integrating Building Information Modeling (BIM), Internet of Things (IoT) technology, and the Finite Element Method (FEM) for structural health monitoring and visual warning under wind loads and flood scour effects. The framework achieves cross-platform collaboration through the FEM Open Application Programming Interface (OAPI) and Python scripts. In the physical domain, fluctuating wind loads are simulated based on the Davenport spectrum, flood scour depth is modeled using the HEC-18 formulation, and foundation constraint degradation is represented through nonlinear spring stiffness reduction. In the FEM domain, dynamic time-history analyses are conducted to obtain structural responses. In the BIM domain, a three-level warning mechanism based on stress change rate (ΔR) is established to achieve intuitive rendering and dynamic feedback of structural damage. A 44.4 m high latticed angle steel tower is employed as the case study for validation. Results demonstrate that the simulated wind spectrum closely matches the theoretical target spectrum, confirming the validity of the load input. A critical scour evolution threshold of 40% is identified, beyond which the first two natural frequencies exhibit nonlinear decay with a maximum reduction of 80.9%. Non-uniform scour induces significant load transfer, with axial forces at leeside nodes increasing from 27 kN to 54 kN. During the 0–60 s wind loading process, BIM visualization accurately captures the full stress evolution from the tower base to the upper structure, showing excellent agreement with FEM results. The proposed framework establishes a closed-loop interaction mechanism of “physical sensing–digital simulation–visual warning”, effectively enhancing the timeliness and interpretability of structural health monitoring for transmission towers under multiple hazards, providing an innovative approach for intelligent disaster prevention in power infrastructure. Full article

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining. Full article

Background/Objectives: Thymic stromal lymphopoietin (TSLP) is central to the pathogenesis of atopic dermatitis (AD) and a promising therapeutic target. However, developing small-molecule TSLP inhibitors is challenging due to the difficulty in disrupting the TSLP-TSLPR interface. This study aimed to explore naturally sourced blockers of the TSLP-TSLPR interaction and identify novel candidate compounds for AD treatment. Methods: HuT78 cells were stimulated with PMA, ionomycin, and TSLP to establish an AD model. Inflammatory cytokines were measured by qRT-PCR and ELISA. JAK/STAT signaling was analyzed by Western blot. In female BALB/c mice, DNCB-induced AD-like skin lesions were topically treated with test compounds, followed by histopathological and immunohistochemical assessment. Results: Eight compounds were screened, and their key structural features were elucidated via structure–activity relationship (SAR) analysis. Among them, kaempferol-7-O-glucoside (K-7-G) emerged as the most potent candidate. It interfered with the TSLP-TSLPR interaction, selectively inhibited TSLP-mediated JAK2/STAT5 phosphorylation, and significantly downregulated IL-4 (p < 0.0001) and IL-13 (p < 0.001) levels. Topical application of 1% K-7-G significantly alleviated AD-like symptoms in a mouse model, decreasing dorsal skin thickness, dermatitis score, and scratching frequency while restoring the expression of filaggrin, loricrin, and occludin (p < 0.0001). Meanwhile, it significantly reduced key inflammatory mediators in a concentration-dependent manner, including TSLP, IL-4, IL-13, TNF-α, IFN-γ, and IgE. Conclusions: This study demonstrates that K-7-G is a novel natural TSLP inhibitor capable of blocking the TSLP-TSLPR signaling pathway and effectively improving AD symptoms. Further research may explore its therapeutic potential in other inflammatory diseases. Full article

Understanding how geometry governs interfacial contact and material removal is central to designing efficient bioinspired surface systems. Gastropod radular teeth form natural arrays of microscale cutting elements optimized for repeated interaction with compliant and semi-rigid substrates, yet experimentally validated shape–performance relationships remain limited. Here, we isolate geometric effects on interfacial mechanics using stereolithography-printed biomimetic tooth arrays inspired by the taenioglossan radula of the hard-substrate grazer Spekia zonata. Two morphologically distinct tooth types (central and marginal) were systematically varied in cusp and stylus geometry (four variants each), while array configuration, material, and boundary conditions were kept constant. Tooth stiffness was quantified in bending tests as load-induced height reduction. Interfacial performance was assessed using a controlled pull-through assay in agarose substrates of two stiffness levels (0.4% and 0.8%), with continuous force recording and measurement of removed mass. Marginal-tooth geometries were stiffer and consistently removed more substrate than central variants. Although work increased substantially in stiffer gels, removal did not scale proportionally and declined for central teeth, revealing a decoupling between mechanical input and yield. Performance correlated with active engagement rather than work alone, indicating geometry-limited contact regimes. These findings establish geometry-controlled stiffness and engagement as key parameters for efficient abrasive interfaces. Full article

The increasing integration of artificial intelligence (AI) in educational decision-making raises a critical question: how to design AI systems that can effectively support teachers while maintaining an appropriate level of trust. Addressing this question requires not only continuous improvements in the technical capabilities of AI systems but also an examination from a human-AI interaction perspective of how different system designs influence users’ cognitive performance and affective responses, thereby providing guidance for system optimization and design. Therefore, this study conducted a randomized controlled experiment with 120 pre-service teachers to investigate how AI-assisted decision-making paradigms and AI explainability jointly influence teachers’ task performance and trust in AI, and whether these effects transfer to subsequent independent tasks. The results indicate that the effect of explanatory interface on task performance is context dependent and yields an immediate positive impact. Under the concurrent paradigm, the explanatory interface of the AI system significantly improves immediate task performance, whereas no significant effect is observed under the sequential paradigm. Moreover, this improvement is confined to the task execution stage and does not transfer to subsequent independent tasks. In contrast, the effect of explanatory interface on trust exhibits a delayed and negative pattern. The explanatory interface has no significant impact on situational trust, while it exerts a negative effect on learned trust and suppresses the natural development of both cognitive trust and emotional trust. In addition, different AI-assisted decision-making paradigms exhibit distinct patterns of influence on task performance and trust. Although the concurrent paradigm performs worse than the sequential paradigm in terms of immediate task performance, it is more effective in promoting users’ emotional trust. Overall, these findings extend the theoretical understanding of the mechanisms of explainability in human-AI interaction and provide empirical evidence for the joint design of explainable AI systems and human-AI collaboration paradigms. Full article

Peptide binders, serving as a critical drug modality bridging small-molecule compounds and protein macromolecules, can effectively mimic the secondary structural elements of natural proteins. Peptides exhibit unique physicochemical advantages when targeting protein protein interaction (PPI) interfaces, which are typically characterized by flat surfaces and extensive contact areas. Recently, diffusion models represented by RFdiffusion have established a new computational paradigm for protein backbone generation by defining a denoising process over the rigid-body transformation group. However, in the de novo design of binders targeting “undruggable” PPI targets, this general paradigm encounters significant adaptability bottlenecks. First, its underlying rigid-body assumption struggles to accurately describe the dynamic induced-fit process of peptides at the binding interface. Second, it lacks sufficient robustness to the experimental resolution heterogeneity inherent in training data. Furthermore, the decoupled two-stage generation of sequence and structure severs the synergy of physicochemical properties, leading to backbones with idealized, singular secondary structures that lack authentic spatial binding capacity and reasonable side-chain physicochemical features. To address these challenges, this study proposes PPI-Diff, a novel generative framework. While preserving the generative capability of diffusion models, PPI-Diff introduces three core mechanisms: (1) a resolution-aware constraint mechanism that maps the measurement precision of experimental data into explicit contextual constraints to dynamically suppress geometric noise from low-resolution samples; (2) an internal-coordinate-driven manifold diffusion model that performs conformational evolution on a Riemannian manifold constructed by dihedral angles, balancing local stereochemical validity with the precise capture of flexible peptide conformations; and (3) a geometry-semantic synergistic modeling mechanism that leverages the evolutionary embeddings of a pre-trained protein language model (ESM-2) as latent variables to align structure generation with biophysical functions. Systematic benchmarking demonstrates that, on a strictly non-homologous test set, the binders generated by PPI-Diff significantly outperform existing baseline models in terms of interface contact density, stereochemical validity, and sequence novelty. Full article

In concrete dam foundations, failure mechanisms are primarily influenced by natural rock discontinuities, the dam foundation interface, or weaker strata. This paper proposes a large displacement contact model (LDCM) based on spherical particle/surface interactions, which is computationally more robust and simpler than contact models that adopt the real block polyhedral geometry. To reduce computational costs, whenever possible, the contact interaction is defined in small displacements. The proposed LDCM is applied to a masonry arch under static loading and to the stability analysis of both a gravity dam and an arch dam. The results presented validate the proposed LDCM, and the numerical predictions are close to results obtained experimentally and closely match those obtained with a more complex polyhedral-based model. The advantages of the LDCM are highlighted, namely the decoupling of contact refinement from block refinement, which significantly reduces the computational costs for the masonry arch example. The relevance of adopting a LDCM to predict a physically accepted failure mode is emphasized for dam safety. Finaly, it is shown that the LDCM contact model can be readily adopted to assess the stability of complex dam foundation systems, with reasonable computational running times if a hybrid contact approach is used. Full article

Classical contact mechanics typically relies on simplifying assumptions such as linear elasticity and frictionless interfaces. A notable example is the Westergaard model, a rigorous theoretical solution for the contact between a rigid sinusoidal surface and an elastic half-space with a flat surface. This configuration captures the features of surface roughness at a single characteristic scale. Such modeling is particularly relevant since most natural and engineered surfaces exhibit roughness, significantly influencing their contact behavior. In this work, we present a nonlinear analytical contact model, which overcomes the main limitations of the Westergaard solution. Specifically, we formulate the contact problem within a finite elasticity framework and include interfacial friction. The analytical model is derived from the results of dedicated finite element simulations and subsequently validated against experimental data from the literature, demonstrating improved predictive accuracy in estimating the contact area as a function of the applied mean pressure. This work lays the foundation for the development of weakly nonlinear multiscale models, where solutions for single-scale roughness can be superimposed to approximate the behavior of more complex, fractal surface geometries. Such an approach holds promise for applications in areas such as tactile human–device interactions, soft robotics, and the design of bioinspired surfaces. Full article