Shaoxing University

Light-Burned Magnesia (LBM) activated Ground Granulated Blast Furnace Slag (GGBS) is established as a promising and robust binder for soil stabilization. However, its durability in saline environments subjected to freeze-thaw (F-T) cycles lacks systematic investigation. To validate its potential for subgrade engineering in seasonally frozen regions, this study evaluates the mechanical and microscopic properties of LBM-GGBS solidified saline soil under F-T cycling. The effects of LBM and GGBS on the unconfined compressive strength (UCS), permeability coefficient, Cl- leaching and microstructures of solidified saline soil after different F-T cycles (0, 2, 4, 6, 8, and 10) were examined. The results showed that increasing the LBM-GGBS content significantly enhanced the soil's resistance to F-T cycles. With a 12% LBM-GGBS content and a GGBS/LBM ratio of 7 (determined as the optimal mix proportion), the solidified soil reached a residual strength of 3MPa after 10F-T cycles, which was four times the strength required for the upper base layer of highway pavement subgrade. Microscopic analysis revealed that the LBM-GGBS solidified soil exhibited a dense structure with calcium silicate hydrate (C-S-H), magnesium silicate hydrate (M-S-H), hydrotalcite, and Kuzel salt as the primary reaction products. The formation of these hydration products significantly densified the structure, thereby increasing the strength and improving the F-T resistance of the solidified soil. Furthermore, ~ 75% of Cl- in the original saline soil could be stabilized even after multiple F-T cycles. These findings elucidate the micro-mechanism of chloride stabilization under freezing conditions and provide a robust theoretical foundation for utilizing LBM-GGBS to mitigate saline soil hazards in seasonally frozen regions.

We introduce a comprehensive computational framework to simulate the complex electro-mechanical response of flexible, porous piezoresistive sensors. Existing simulation approaches face a critical bottleneck: they either rely on homogenized material properties that ignore the determinative porous micro-architecture, or they employ simplified, idealized geometries that fail to capture the stochastic nature of real foams. Most critically, prior models have overwhelmingly neglected the intrinsic viscoelasticity of the polymer matrix, a fundamental property governing the material's time-dependent mechanical response. Our framework overcomes these limitations by integrating three key elements within a Finite Element (FE) environment: (1) A stochastic 2D Representative Volume Element (RVE) based on a novel, computationally-stable abstracted pore geometry; (2) A Generalized Maxwell model to capture the experimentally-verified viscoelastic stress relaxation of the polyurethane (PU) substrate; and (3) A "segmented quasi-static" (SQS) solution strategy, developed to circumvent the documented limitations of commercial FE software in handling fully-coupled dynamic, conductive-contact simulations. The model is built by generating an ensemble of over 200 RVEs, each with randomly-distributed internal pore geometries, to represent the foam's statistical heterogeneity. By performing an ensemble average of the simulation results, our model successfully generates a macroscopic resistance-strain curve that is in high agreement with experimental observations. Crucially, this work provides a unified, structure-based origin for the two distinct sensing regimes widely reported in the literature: the initial, high-sensitivity "crack effect" (GF > 25) is shown to be a statistical emergent property of the initial contact events of the most vulnerable pores in the stochastic distribution, while the stable, high-strain "contact effect" corresponds to the progressive, large-scale pore collapse and contact area growth across the ensemble. This framework bridges the gap between stochastic microstructure and predictable macroscopic performance, opening a pathway for the inverse design of foam-based sensors.

Microscopic imaging serves as a crucial method for assessing the quality of selective laser melting (SLM). Traditional approaches rely on manual inspection, which limits their efficiency and reproducibility. To address the demand for defect detection and analysis, this paper proposes a synergistic method for analyzing pore defects in microscopic images, integrating image segmentation with polynomial fitting. We designed a high-performance image segmentation model. Its capabilities are enhanced through an adaptive curved learning rate adjustment strategy, an attention-based feature extraction module, and a lightweight feature fusion network. Additionally, the model automatically calculates and quantifies the pixel proportion of pore defects within micrographs. Experiments conducted on a constructed SLM pore defect microscopic image dataset demonstrated excellent performance, enabling effective calibration and quantification of defect information. Chebyshev polynomials are employed to fit the nonlinear relationship between key process parameters and porosity. Based on these results, we conducted an in-depth analysis of how different process parameters influence pore defect formation, revealing the intrinsic correlation between process parameters and defects. This study provides an effective automated detection and analysis tool for SLM quality assessment and analysis.

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with elusive pathogenesis and lack of targeted therapies. While exercise can ameliorate ASD-like behaviors, its underlying mechanisms remain unclear. Recent studies have identified dysbiosis of gut microbiota and altered levels of short-chain fatty acids (SCFAs), as critical contributors to ASD-associated behavioral abnormalities. This study investigated the potential role of the gut-brain axis, specifically the vagal pathway, in mediating the therapeutic effects of voluntary wheel running exercise in a valproic acid (VPA)-induced ASD-like rat models. We demonstrated that six weeks of voluntary wheel running exercise attenuated ASD-like behavioral deficits. Exercise restructured gut microbial communities and elevated SCFA levels, notably butyrate, in feces and plasma. Concurrently, exercise normalized imbalances of neuroactive substances in the hippocampus and prefrontal cortex and suppressed neuroinflammation, evidenced by reduced microglial/astrocytic reactivity and a shift in microglial polarization toward an anti-inflammatory phenotype. Critically, subdiaphragmatic vagotomy attenuated these exercise-induced improvements, including the restoration of neuroactive substance homeostasis, resolution of neuroinflammation, and the amelioration of behavioral deficits. Our findings suggest that intact vagal signaling plays a critical role in coordinating gut-derived microbial and metabolic signals with central neuroadaptations to mediate the benefits of voluntary exercise on ASD-like behaviors.