Liquefied dimethyl ether-alcohol systems for enhanced saponins recovery from Gac seed kernels: Systematic HSP theory and experimental evaluation

Structure-guided identification of a PrPC-binding peptide derived from Brucella abortus Hsp60 for receptor-mediated mucosal targeting.

Experimental evaluation on bearing performance of tire cell-geogrid reinforced subgrades

Graph Neural Networks (GNNs) have made significant strides in the analysis and modeling of complex network data, particularly excelling in graph and node classification tasks. However, the "closed box" nature of GNNs impedes user understanding and trust, thereby restricting their broader application. This challenge has spurred a growing focus on demystifying GNNs to make their decision-making processes more transparent. Traditional methods for explaining GNNs often rely on selecting subgraphs and employing combinatorial optimization to generate understandable outputs. However, these methods are closely linked to the inherent complexity of GNNs, leading to higher explanation costs. To address this issue, we introduce a lower-complexity proxy model to explain GNNs. Our approach leverages knowledge distillation with inter-layer alignment, specifically targeting the challenge of over-smoothing and its detrimental impact on model explanation. Initially, we distill critical insights from complex GNN models into a more manageable proxy model. We then apply an inter-layer alignment-based distillation technique to ensure alignment between the proxy and the original model, facilitating the extraction of node or edge-level explanations within the proxy framework. We theoretically prove that the explanations derived from the proxy model are faithful to both the proxy and the original model. Additionally, we show that the upper bound of unfaithfulness between the proxy and the original model remains consistent when the distillation error is infinitesimal. This inter-layer alignment knowledge distillation technique enables the proxy model to retain the knowledge learning and topological representation capabilities of the original model to the greatest extent. Experimental evaluations on numerous real-world datasets confirm the effectiveness of our method, demonstrating robust performance.

Experimental evaluation and theoretical prediction of single hardwood dowel connections in softwood and hardwood

Experimental evaluation on the shear performance of grouted bolt connections in steel-CLT and steel-CLBT composite structures

Experimental and numerical evaluation of convective heat transfer correlations in a packed bed of iron ore pellets

Experimental and numerical evaluation of the drop-weight impact performance of three-dimensional woven composite structures

Deep eutectic solvent–mediated solubilization enhancement of quercetin: Experimental, molecular dynamics, and biological evaluation

Toward energy-efficient extraction of aromatics from light cycle oil using deep eutectic solvents: Insights from experimental and process evaluation

Impact and post-impact performance of RC beams strengthened with near-surface mounted CFRP strips: Experimental and numerical evaluation

Experimental and analytical evaluation of enhanced interface efficiency with geocell anchor cages

Understanding the group behavior of semi-rigid soil–cement piles remains a major challenge in deep-mixed foundation engineering. Unlike conventional displacement piles, soil–cement columns exhibit transitional stiffness and composite interaction with the surrounding ground, leading to settlement-dependent mechanisms that are not captured by existing group-efficiency approaches. This study integrates rare full-scale load tests, carefully calibrated three-dimensional finite-element analysis (3D FEA), and systematic analytical benchmarking to establish a mechanistic basis for evaluating group efficiency in soil–cement pile groups. Instrumented field tests on single, three-pile, and five-pile groups (S/D = 2) reveal pronounced stress overlap, non-uniform shaft mobilisation, and significant reductions in per-pile capacity. A high-fidelity 3D FEA model, incorporating a physically justified transitional zone and enhanced interface stiffness, reproduces both the load–settlement response and axial force transfer with high accuracy. Parametric analyses over a wide range of spacings and group sizes demonstrate that group efficiency is not a constant parameter but increases with settlement due to progressive mobilisation of shaft resistance and pile–cap–soil interaction. Benchmarking against eight widely used empirical equations confirms that traditional rigid–pile formulations systematically misrepresent the behavior of semi–rigid pile groups. Motivated by these findings, a new settlement–dependent analytical expression for group efficiency is proposed, combining a geometry-based interaction term with a nonlinear mobilisation function. The model reproduces numerical trends with an average error of only 6.8 % and captures the physical behavior observed in both field and numerical results. The study provides a unified, experimentally validated framework for interpreting soil–cement pile group behavior and offers improved guidance for serviceability-based design of deep-mixed foundations.

Integrated experimental and in-silico molecular docking evaluations of the antimicrobial activity of secondary metabolites from the seeds of Piper capense

Experimental evaluation of seismic retrofitting strategies for slab-integrated exterior RC beam–column joints: Comparative performance of BSAC, GFRP-BSB, and GFRP-NSM

MAGED: Multimodal attentive graph learning with gene expression dynamics on knowledge graphs for TCM target prediction.

Adaptive differential privacy mechanism for enhanced deep learning model utility and privacy.

Comparison of rate models for gradient elution chromatography and experimental evaluation for IEC, HIC, and RPC systems.

Experimental evaluation of thermal and hydraulic performance in multi-layer microchannel heat sinks with various flow configurations

Development and experimental evaluation of low-cost natural fabric reinforced elastomeric isolators for seismic applications