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In civil engineering, with the increasing demand for structural reinforcement and renovation projects, polymer-rich anchoring adhesives have attracted much attention due to their performance advantage of having high strength and have become a key factor in ensuring the safety and durability of buildings. In this study, polymer-rich anchoring adhesives underwent three artificial aging treatments (alkali medium, hygrothermal, and water bath) to evaluate their aging performance. Alkali treatment reduced bending strength by up to 70% (sample 5#) within 500 h before stabilizing, while hygrothermal and water-curing treatments caused reductions of 16–51% and 15–77%, respectively, depending on adhesive composition. Dynamic thermomechanical analysis revealed significant loss factor decreases (e.g., epoxy adhesives dropped from >1.0 to stable lower values after 500 h aging), indicating increased rigidity. Infrared spectroscopy confirmed chemical degradation, including ester group breakage in vinyl ester resins (peak shifts at 1700 cm−1 and 1100 cm−1) and molecular chain scission in unsaturated polyesters. The three test methods consistently demonstrated that 500 h of aging sufficiently captured performance trends, with alkali exposure causing the most severe degradation in sensitive formulations (e.g., samples 5# and 6#). These results can be used to establish quantitative benchmarks for adhesive durability assessment in structural applications.

This review systematically examined the transformative role of machine learning in predicting polymer aging lifetime, addressing critical limitations of conventional methods such as the Arrhenius model, time–temperature superposition principle, and numerical fitting approaches. The primary objective was to establish a comprehensive framework that integrates multi-mechanism coupling with dynamic data-driven modeling to enhance prediction accuracy across complex aging scenarios. Four key machine learning categories demonstrate distinct advantages: support vector machines effectively capture nonlinear interactions in multi-stress environments; neural networks enable cross-scale modeling from molecular dynamics to macroscopic failure; decision tree models provide interpretable feature importance quantification; and hybrid approaches synergistically combine complementary strengths. These methodologies have shown significant success in critical industrial applications, including building trades, photovoltaic systems, and aerospace composites, creating an integrated predictive system that bridges molecular-level dynamics with service-life performance. By transforming life prediction from empirical extrapolation to mechanism-based simulation, this machine-learning-driven paradigm offers robust methodological support for engineering safety design in diverse polymer applications through its capacity to model complex environmental interactions, adapt to real-time monitoring data, and elucidate underlying degradation mechanisms.

By applying the Schmidt hammer rebound (SHR) method and the Leeb hardness method for nondestructive testing (NDT) and localized sampling analysis of stone materials, this study carefully selected suitable experimental parameters to assess the mechanical properties of the stone materials. Based on these findings, a bridge was established between field testing and laboratory analysis, leading to a comprehensive set of on-site NDT techniques for evaluating the strength of stone materials. Under simulated real-world conditions, the load-bearing capacity of the simulated components in the dry-hanging stone system was assessed and compared with the technical specifications outlined in current standards by analyzing the bending test results of the stone materials, the flexural and shear strengths were determined, thus validating the intrinsic link between the hardness of the stone materials and the load-bearing capacity of the dry-hanging stone system. Consequently, a systematic approach for inspecting and assessing the load-bearing capacity of the dry-hanging stone system was formulated.

Temperature and pressure sensors currently encounter challenges such as slow response times, large sizes, and insufficient sensitivity. To address these issues, we developed tetraphenylethylene (TPE)-doped polyvinylidene fluoride (PVDF) nanofiber membranes using electrospinning, with process parameters optimized through a convolutional neural network (CNN). We systematically analyzed the effects of PVDF concentration, spinning voltage, tip–to–collector distance, and flow rate on fiber morphology and diameter. The CNN model achieved high predictive accuracy, resulting in uniform and smooth nanofibers under optimal conditions. Incorporating TPE enhanced the hydrophobicity and mechanical properties of the nanofibers. Additionally, the fluorescent properties of the TPE-doped nanofibers remained stable under UV exposure and exhibited significant linear responses to temperature and pressure variations. The nanofibers demonstrated a temperature sensitivity of −0.976 gray value/°C and pressure sensitivity with an increase in fluorescence intensity from 537 a.u. to 649 a.u. under 600 g pressure. These findings highlight the potential of TPE-doped PVDF nanofiber membranes for advanced temperature and pressure sensing applications.

ObjectivesThe aims of the study were to assess the management of low-density lipoprotein cholesterol (LDL-C) and the goal achievement, as well as to investigate the association between baseline LDL-C level, lipid-lowering treatment (LLT), and stroke recurrence in patients with ischaemic stroke or transient ischaemic attack (TIA).DesignOur study was a post hoc analysis of the Third China National Stroke Registry (CNSR-III).SettingWe derived data from the CNSR-III - a nationwide clinical registry of ischaemic stroke and TIA based on 201 participating hospitals in mainland China.Participants15,166 patients were included in this study with demographic characteristics, etiology, imaging, and biological markers from August 2015 to March 2018.Primary and secondary outcome measuresThe primary outcome was a new stroke, LDL-C goal (LDL-C<1.8mmol/L and LDL-C<1.4mmol/L, respectively) achievement rates, and LLT compliance within 3, 6, and 12 months. The secondary outcomes included major adverse cardiovascular events (MACE) and all caused death at 3 and 12 months.ResultsAmong the 15,166 patients, over 90% of patients received LLT during hospitalization and 2 weeks after discharge; the LLT compliance was 84.5% at 3 months, 75.6% at 6 months, and 64.8% at 12 months. At 12 months, LDL-C goal achievement rate for 1.8mmol/L and 1.4mmol/L was 35.4% and 17.6%, respectively. LLT at discharge was associated with reduced risk of ischemic stroke recurrence (HR=0.69, 95% CI: 0.48-0.99, p=0.04) at 3 months. The rate of LDL-C reduction from baseline to 3-month follow-up was not associated with a reduced risk of stroke recurrence or major adverse cardiovascular events (MACE) at 12 months. Patients with baseline LDL-C ≤1.4mmol/L had a numerically lower risk of stroke, ischemic stroke and MACE at both 3 months and 12 months.ConclusionsThe LDL-C goal achievement rate has increased mildly in the stroke and TIA population in mainland China. Lowered baseline LDL-C level was significantly associated with a decreased short- and long-term risk of ischemic stroke among stroke and TIA patients. LDL-C<1.4mmol/L might be a safe standard for this population.

BackgroundSmall single subcortical infarction (SSSI) may be classified as parent artery disease-related or only branch involved according to the stenosis of parent artery. The study aimed to evaluate short-term and long-term prognoses and the effectiveness of antiplatelet therapy in SSSI.MethodsWe prospectively enrolled 2890 patients with SSSI from the Third China National Stroke Registry (CNSR-III) database from August 2015 to March 2018. We assessed clinical outcomes and antiplatelet treatment effects in patients with SSSI with and without parent artery stenosis (PAS) identified by magnetic resonance angiography.ResultsAmong 2890 patients with SSSI in the perforator territory of the middle cerebral artery and the basilar artery, there were 680 (23.53%) patients with PAS and 2210 (76.47%) patients without PAS, respectively. After adjusting for potential confounders, the PAS group had a greater initial stroke severity (OR 1.262, 95% CI 1.058 to 1.505; p=0.0097) and a higher risk of ischaemic stroke recurrence at 3 months (OR 2.266, 95% CI 1.631 to 3.149; p<0.0001) and 1 year (OR 2.054, 95% CI 1.561 to 2.702; p<0.0001), as well as composite vascular events at 3 months (OR 2.306, 95% CI 1.674 to 3.178; p<0.0001) and 1 year (OR 1.983, 95% CI 1.530 to 2.570; p<0.0001), compared with the non-PAS group. In both groups, dual antiplatelet therapy was not superior to single antiplatelet therapy in preventing stroke recurrence, composite vascular events and disability.ConclusionPAS related to significantly higher rates of short-term and long-term stroke recurrence and composite vascular events, suggesting heterogeneous mechanisms in SSSI subgroups. The effectiveness of antiplatelet therapy for SSSI needs further investigation.

Single-atom catalysts not only maximize metal atom efficiency, they also display properties that are considerably different to their more conventional nanoparticle equivalents, making them a promising family of materials to investigate. Herein we developed a general host–guest strategy to fabricate various metal single-atom catalysts on nitrogen-doped carbon (M1/CN, M = Pt, Ir, Pd, Ru, Mo, Ga, Cu, Ni, Mn). The iridium variant Ir1/CN electrocatalyses the formic acid oxidation reaction with a mass activity of 12.9 AmgIr−1 whereas an Ir/C nanoparticle catalyst is almost inert (~4.8 × 10−3 AmgIr−1). The activity of Ir1/CN is also 16 and 19 times greater than those of Pd/C and Pt/C, respectively. Furthermore, Ir1/CN displays high tolerance to CO poisoning. First-principle density functional theory reveals that the properties of Ir1/CN stem from the spatial isolation of iridium sites and from the modified electronic structure of iridium with respect to a conventional nanoparticle catalyst.Single-atom catalysts maximize metal atom efficiency and exhibit properties that can be considerably different to their nanoparticle equivalent. Now a general host–guest strategy to make various single-atom catalysts on nitrogen-doped carbon has been developed; the iridium variant electrocatalyses the formic acid oxidation reaction with high mass activity and displays high tolerance to CO poisoning.

Hepatocellular carcinoma (HCC) usually occurs on the basis of chronic liver inflammatory diseases and cirrhosis. The liver microenvironment plays a vital role in the tumor initiation and progression. Exosomes, which are nanometer-sized membrane vesicles are secreted by a number of cell types. Exosomes carry multiple proteins, DNAs and various forms of RNA, and are mediators of cell-cell communication and regulate the tumor microenvironment. In the recent decade, many studies have demonstrated that exosomes are involved in the communication between HCC cells and the stromal cells, including endothelial cells, macrophages, hepatic stellate cells and the immune cells, and serve as a regulator in the tumor proliferation and metastasis, immune evasion and immunotherapy. In addition, exosomes can also be used for the diagnosis and treatment HCC. They can potentially serve as specific biomarkers for early diagnosis and drug delivery vehicles of HCC. Chinese herbal medicine, which is widely used in the prevention and treatment of HCC in China, may regulate the release of exosomes and exosomes-mediated intercellular communication. In this review, we summarized the latest progresses on the role of the exosomes in the initiation, progression and treatment of HCC and the potential value of Traditional Chinese medicine in exosomes-mediated biological behaviors of HCC.

Under the combined effects of climate change, overexploitation, and intense grazing, temperate steppe in northern China is experiencing increasing deterioration, which is typified by a shift from structural degradation to functional disruption. Accurately tracking steppe degradation using remote sensing technology has emerged as a crucial scientific concern. Prior research failed to integrate ecosystem structure and function and lacked reference baselines, relying only on individual indicators to quantify degradation. To resolve these gaps, this study established a novel degradation evaluation index system integrating ecosystem structure and function, incorporating vegetation community distribution and proportions of degradation-indicator species to define reference states and quantify degradation severity. Analyzed spatiotemporal evolution and drivers across the temperate typical steppe (2013–2022). Key findings reveal (1) non-degraded and slightly degraded areas dominated (75.57% mean coverage), showing an overall fluctuating improvement trend; (2) minimal transitions between degradation levels, with stable conditions prevailing (59.52% unchanged area), indicating progressive degradation reversal; and (3) natural factors predominated as degradation drivers. The integrated structural–functional framework enables more sensitive detection of early degradation signals, thereby informing more effective steppe restoration management.

Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions.