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Enhanced strength and chemical stability of Salix tetrasperma wood through thermal modification
Integration of corpus linguistics and deep learning techniques for enhanced semantic-driven emotion detection on textual data
Evaluation of geological hazards susceptibility along a key railway based on machine learning
Influence of adjacent undercutting line positions on surrounding rock stability in block caving
Feasibility, accuracy and prognostic value of fully automated speckle tracking analysis-derived left ventricular ejection fraction and global longitudinal strain
Abstract Left ventricular ejection fraction (LVEF) has been widely used for LV systolic assessment. However, it has drawbacks including large observer variability and unsatisfactory detectability of subclinical dysfunction. LV global longitudinal strain (LVGLS) by speckle-tracking on two-dimensional echocardiography (2DE) has been reported to be superior to LVEF. However, it may be influenced by the different methodology (manual versus semi-automated analyses) and biased study cohorts. A fully automated analysis can alleviate drawbacks of the LVEF and allow fair comparison between LVEF and LVGLS. We sought to evaluate the feasibility, accuracy of LVEF, and LVGLS measurements by the novel fully automated 2DE software against manual analysis and cardiac MRI feature-tracking (CMR-FT). Additionally, we tested prognostic utility of LVEF and LVGLS. In consecutive 436 patients undergoing CMR and 2DE, the fully automated analysis had excellent feasibility (97%). The correlation in LVEF and LVGLS between the fully automated analysis and the other two techniques was high ( r = 0.82–0.95). During a median 26-month follow-up, 65 patients experienced cardiac events. In 422 patients successfully analyzed by the fully automated software (63 patients with cardiac events), both LVEF and LVGLS by the fully automated analysis were associated with cardiac events and their prognostic utility was not inferior to manual analyses. Nested Cox proportional hazard models and net reclassification analyses revealed fully automated analysis-derived LVEF and LVGLS did not have an incremental prognostic value over each other. A novel, fully automated analytical software demonstrated excellent applicability to clinical practice by providing reliable LVEF and LVGLS with comparable prognostic utility.
Macro- and meso- shear mechanical properties of rock-grout composite structures under different stress level and initial hydration damage
Abstract This study investigates the shear mechanical behavior of soft rock—grout coupled structures through triaxial shear tests and particle flow simulations on sandstone-resin composite specimens under varying normal stresses and immersion times. Key findings include: (1) Intensified hydration damage compromises shear resistance in both rock and interfaces, amplifies deformation, and degrades bearing capacity. (2) Elevated normal stress constrains microcrack coalescence, enhances bearing capacity. Failure modes of specimens—classified as interfacial shear sliding, mixed shear failure, or shear failure occurs exclusively within rock—depending on stress levels and hydration duration. (3) Prolonged immersion reduces energy thresholds for bond rupture, shifting crack propagation from abrupt surges to gradual increments. Weak zones migrate from interfaces to external rock. (4) Increased normal stress raises energy storage limits, suppresses microcrack coalescence, and strengthens weak zones. This study revealing the critical control of hydration damage and stress confinement on energy thresholds at the rock-grout interface, providing a theoretical basis for long-term stability prediction of anchorage structures.
Yielding brace system as a next-generation lateral load mechanism for seismic resilient cities
Higher-order sonification of the human brain
Influence of layer thickness and extrusion ratio on strand morphology, porosity, surface roughness, and anisotropic mechanical properties in FDM
Channel modeling and capacity optimization for optical RIS aided NOMA in indoor multiuser visible light communication IoT systems
Myocardial reprogramming by HMGN1 underlies heart defects in trisomy 21
Strontium/copper doped nano-bioactive glass for biomedical applications
Abstract This work is reported for the preparation and characterization of nano bioactive glass doped with copper and strontium in the system (SiO 2 -CaO-P 2 O 5 -CuO-SrO).Compared to traditional procedures, the Sol-gel approach offers a number of advantages for glass formation, including improved control over size and morphology. At the expense of CaO, CuO and SrO were added to the glass compositions. CuO and SrO were used for their highly antibacterial effects and bioactivity enhancement. To confirm the bioactivity of all the glass samples, scanning electron microscopy (SEM), Brunauer Emmett Teller (BET) surface area measurements, Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) were used to assess the morphological and structural characteristics of the produced glasses. The particles that developed on the glass samples’ surface during immersion in the simulated body fluid (SBF) were analysed to detect the glasses’ bioactivity. The hydroxyapatite (HAp) layer’s development was verified by EDX analysis. ICP (Inductively Coupled Plasma) Spectroscopy was used to analyse the ion release in the SBF solution. Additionally, the impact of the antibacterial activity on several types of bacteria (including Gram-positive , Gram-negative , and antifungal bacteria ) was examined. Moreover, Ciprofloxacin was used as a model medication to test the drug loading efficiency. The Korsmeyer-Peppas kinetic model and zero order models were used to study the drug release mechanisms. The cytotoxicity of the nano-bioactive glass against human bone osteosarcoma cells (MG-63) and bone marrow stromal cells was also evaluated using the MTT assay.
Revisiting convolutional design for efficient CNN architectures in edge-aware applications
Analysis of factors affecting the academic performance of university students using machine learning
Investigating asphalt aggregate bonding degradation through aging and temperature effects using multiscale testing methods
Abstract Moisture damage in asphalt mixtures is a critical durability concern influenced by interfacial adhesion between asphalt and aggregates. This study systematically evaluates adhesion performance using digital image analysis and surface energy theory across four asphalt types (OR50, OR70, OR90, ORSBS) and five aggregates (limestone, basalt, granite, diabase, diorite). Key findings reveal that limestone exhibits superior adhesion stability (100% adhesion rate under aging) due to chemical bonding, while acidic aggregates (e.g., granite) show significant sensitivity to moisture and aging. A novel quantitative method combining water immersion tests and pixel-based stripping area analysis demonstrated high correlation (R 2 > 0.9) with surface energy-derived ER values, validating its reliability. Temperature and aging effects were further quantified, showing adhesion loss under thermal/oxidative aging, with SBS-modified asphalt outperforming base binders. These results provide a robust framework for material selection in pavement design, emphasizing aggregate alkalinity and polymer modification as key factors for moisture resistance.