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Entropy-Enabled Stabilization and Activity Enhancement of Ruthenium Oxides for Acidic Oxygen Evolution
A decision-making framework based on complex fuzzy bipolar soft sets for investment strategy optimization
Excited-State Symmetry-Breaking Dynamics in a Centrosymmetric Quadrupolar Emitter: Cascaded Relaxation Pathways
Children’s attitudes to release outdoors and abandon pet cats
Dynamic Molecular Crystal Triggered by Near-Ambient Intramolecular Twisting for Switchable Luminescence and Two-Photon Absorption
Study on microstructural evolution of soft clay under one-dimensional consolidation
Abstract Undisturbed marine soft clay from Daya Bay in Shenzhen poses severe geotechnical challenges owing to its high sensitivity and low shear strength. In this study, the microstructural evolution and underlying mechanical mechanisms of this soft clay under one-dimensional consolidation were systematically investigated. A multi-scale analytical approach was adopted, integrating Mercury Intrusion Porosimetry (MIP), dual-energy synchrotron X-ray Micro-Computed Tomography (Micro-CT) with phase recovery technology, and Discrete Element Method (DEM) simulations implemented in PFC3D 6.0. The results show that one-dimensional consolidation induces a distinct structural transition of the clay’s pore size distribution from bimodal to unimodal, with the connectivity of macropores being completely destroyed under high consolidation pressure. Two structural yield stresses were identified at 62 kPa and 676 kPa, corresponding to the structural failure of inter-cluster aggregates and intra-cluster particles, respectively, which characterizes the mechanical transformation of the clay from aggregate rearrangement to intra-aggregate compression. Numerical simulation results further demonstrate that the increase in consolidation pressure leads to a gradual rise in the particle coordination number and a significant decrease in the contact-sliding ratio, thereby enhancing the structural stability of the clay skeleton. These findings establish a quantitative correlation between the microscopic pore structure reorganization and macroscopic mechanical response of soft clay, and provide a robust mechanistic basis for accurate settlement prediction and ground improvement design in coastal geotechnical engineering.
Comparative adsorption performance of bare and chitosan-coated La-Zn-Fe spinel/perovskite nanocomposites for efficient dye removal
Abstract Effective and affordable treatment of dye-contaminated wastewater remains essential for environmental protection. This study reports the synthesis of La 0.5 Zn 0.5 Fe 2 O 4 (LZNF) nanoparticles via coprecipitation and their in situ polymerization with Chitosan to form LZNF/CS nanocomposites. The materials were characterized using EDX, FT-IR, VSM, XRD, TEM, SEM, and BET analyses. XRD confirmed nanocrystalline cubic spinel La 0.5 Zn 0.5 Fe₂O₄ as the main phase, with orthorhombic perovskite LaFeO₃ as a secondary component. LZNF showed high adsorption efficiency toward the anionic dyes indigo carmine (IC) and Acid Blue 25, achieving removals of 97.7% and 92%, respectively, while LZNF/CS achieved 92% and 69% under acidic conditions. Adsorption of both dyes followed a pseudo-second-order kinetic model and was spontaneous and exothermic. IC and AB 25 adsorption on LZNF fitted the Langmuir model, indicating monolayer adsorption, whereas adsorption on LZNF/CS followed the Freundlich model, suggesting multilayer uptake due to Chitosan modification. Electrostatic interactions dominated the adsorption mechanism, with hydrogen bonding and electrostatic interactions contributing secondarily. Both materials retained strong performance over five regeneration cycles, supporting their potential as efficient adsorbents for wastewater purification.
Regional differences in physical fitness profiles of preschool children in Chongqing China revealed by machine learning
Pyridinium(Boron) Fluorophores as Versatile Photocatalysts
Prediction of shear strength in exterior reinforced concrete joints using kernel-based Gaussian Regression
Maternal and obstetric determinants of prematurity and term low birth weight in Afghanistan: a hospital-based case–control study
Extended follow-up of ultrastructural remodeling and functional recovery after lamellar macular hole surgery using autologous platelet-rich plasma
Abstract This prospective interventional case study aimed to describe the anatomical and functional outcomes of pars plana vitrectomy (PPV) with adjunct highly concentrated autologous platelet-rich plasma (PRP) in patients with symptomatic, progressive lamellar macular hole (LMH) over an extended follow-up period. 20 eyes of 20 patients were included with a minimum follow-up of 24 months (mean 40.2 ± 11.8 months) and a morphological diagnosis of LMH. 23-/25-gauge PPV was conducted with peeling of the internal limiting membrane (ILM) and epiretinal proliferation (ERP; if present) with intraoperative application of autologous PRP under air or gas tamponade. Combined phacovitrectomy was performed in phakic eyes. Foveal anatomy was restored in all cases at first follow-up. Best-corrected visual acuity (BCVA) significantly improved from 0.36 ± 0.16 logMAR to 0.19 ± 0.24 logMAR at final follow-up (mean 0.17 logMAR, p = 0.005). Central retinal thickness (CRT) increased significantly ( p < 0.001), indicating retinal restoration. Ellipsoid Zone (EZ) defects, initially present in 80% of patients, fully resolved in 62.5% with a significant reduction of defect width ( p = 0.005). Microperimetry was stable throughout the entire follow-up. Three patients developed recurrent defects possibly attributable to insufficient peeling or postoperative position. No other significant intra- or postoperative complications were recorded. Autologous PRP in LMH surgery is safe and might be an effective adjuvant, potentially offering long-term anatomical stability and functional recovery while reducing the risk of postoperative complications. Those observed effects are exploratory as the cohort size is small and a control group is missing. A main strength of our study is the extended follow-up period which is essential to capture all possible regeneration mechanism.
A novel student dataset for ML based effective career growth recommendation
Abstract Educational Data Mining (EDM) techniques are increasingly employed to analyze student data for predicting optimal career paths and providing tailored recommendations. A major challenge, however, is the lack of a benchmark dataset that effectively supports this objective, along with the difficulty of identifying the most relevant student attributes for career growth decision support. This study addresses the need for a comprehensive and well-structured dataset to facilitate research on personalized career growth recommendations for engineering students. It presents the methodology used to curate and preprocess a novel benchmark dataset encompassing student demographics, academic background, technical and soft skills, and stress-related factors. Challenges such as data heterogeneity, sparsity, and noise were managed through rigorous data cleaning, feature engineering, and dimensionality reduction techniques.