Search Results (288)

The irreducible water saturation of reservoirs seriously restricts the efficient drainage of unconventional energy sources. NMR logging can be used to determine parameters such as total porosity, effective porosity, irreducible water saturation, and permeability, which play an important role in oil and gas identification. T_{2 cut off} value identification using the NMR T₂ spectrum is the key to clarifying the irreducible water saturation of unconventional reservoirs. In this paper, saturation and centrifugal T₂ spectra of sandstone and coal samples are used to study and calculate the T_{2 cut off} value, with methods including single fractal dimension, multi-fractal dimension, and spectrum morphological discrimination; in addition, the applicability of these three methods in characterizing T_{2 cut off} is discussed. According to the morphological difference of the saturated T₂ spectrum, relationships between morphological parameters and the T_{2 cut off} of four types of sample are described. The parameters related to T_{2 cut off} can be divided into two types: (1) the first type includes morphological parameters main peak position (T_M) and smaller-pore volume percentage (SPVP); with an increase of T_{2 cut off}, T_M increases linearly and SPVP decreases exponentially, and the correlation between SPVP and T_{2 cut off} is stronger than that of T_M. (2) The other type includes fractal parameters D₂ (fractal dimension of larger pore), D₋₁₀ – D₁₀, and D₋₁₀/D₁₀; with the increase of T_{2 cut off}, single and multi-fractal dimensions all increase linearly, and the correlation between D₂ and T_{2 cut off} is stronger than that of the multi-fractal dimension. When calculating the T_{2 cut off} of samples with macro-pores developed, spectrum morphological methods should be used preferentially, while the fractal dimension discrimination methods need be used for the T_{2 cut off} of samples with developed micro-pores. Then, the T_{2 cut off} value prediction and evaluation system are described. The overall results of this work can provide a theoretical basis for the inversion of bound water content in the original formation. Full article

Point-of-Interest (POI) recommendation predicts users’ future check-ins based on their historical trajectories and plays a key role in location-based services (LBS). Traditional approaches such as collaborative filtering and matrix factorization model user–POI interaction matrices fail to fully leverage spatio-temporal information and semantic attributes, leading to weak performance on sparse and long-tail POIs. Recently, Graph Neural Networks (GNNs) have been applied by constructing heterogeneous user–POI graphs to capture high-order relations. However, they still struggle to effectively integrate spatio-temporal and semantic information and enhance the discriminative power of learned representations. To overcome these issues, we propose Spatio-Temporal and Semantic Dual-Channel Contrastive Alignment for POI Recommendation (S²DCRec), a novel framework integrating spatio-temporal and semantic information. It employs hierarchical relational encoding to capture fine-grained behavioral patterns and high-level semantic dependencies. The model jointly captures user–POI interactions, temporal dynamics, and semantic correlations in a unified framework. Furthermore, our alignment strategy ensures micro-level collaborative and spatio-temporal consistency and macro-level semantic coherence, enabling fine-grained embedding fusion and interpretable contrastive learning. Experiments on real-world datasets, Foursquare NYC, and Yelp, show that S²DCRec outperforms all baselines, improving F1 scores by 4.04% and 3.01%, respectively. These results demonstrate the effectiveness of the dual-channel design in capturing both sequential and semantic dependencies for accurate POI recommendation. Full article

The placenta, a temporary organ that connects mother and child for nutrient and metabolite exchange, becomes medical waste after birth but can provide valuable metabolic insights. Thirty-three placenta samples were analyzed using ICP-OES to determine concentrations of ten elements, including macro-, micro-, trace, and heavy metals. Results were compared with maternal and neonatal data, including Apgar scores, maternal age, and blood parameters. Correlations were found between elements (e.g., Ca–Mg, Fe–Zn, and Mn–Cu) and between mineral levels and maternal or infant parameters (e.g., Ca–RBC, Mn–Hb, Cu–PLT, and Cu–UA Pi). No quantifiable heavy metals were detected, nor associations with smoking, gestational diabetes, preterm birth, birth weight, or Apgar scores. Findings suggest that maintaining proper blood morphology and preventing anemia in pregnancy requires attention not only to iron but also to Ca²⁺, Mg²⁺, and Mn²⁺ levels. Manganese and copper assessment may be beneficial for diagnostic purposes in pregnant women. Further large-scale tissue studies are recommended, including comprehensive maternal–fetal health data such as Doppler velocimetry of placental vessels. Full article

Rockfall typically involves repeated impacts that induce progressive damage and fragmentation in rock masses. To investigate the mechanism governing this process under different impact velocities, a series of controlled impact tests were conducted using a newly developed compressed gas-driven rock impact apparatus. This study systematically examined the effect of impact velocities and number on rock damage, distinguishing between internal damage (<10.0 m/s) and local failure (10.0 m/s–20.0 m/s). At the internal damage level, uniaxial compression tests with acoustic emission monitoring were employed to analyze the macro-mechanical properties and micro-failure processes of granite. At the local failure level, the repeated impact number required to transition from localized to complete failure was recorded, and polarizing microscopy was used to characterize microstructural evolution. The results show that damage and failure mechanisms are strongly influenced by both impact velocity and repeated impact number. Specifically, higher impact velocities and repeated impacts promote a shift toward brittle failure, with threshold behaviors observed at 5.0 m/s (fourth impact) and 7.5 m/s (third impact). A quantitative analysis further correlates impact conditions with mechanical degradation and energy evolution, providing insight into the underlying processes controlling rockfall fragmentation. Full article

Purpose: This study proposes a novel magnetic resonance (MRI)-based classification of cortical tubers (CTs) in tuberous sclerosis complex (TSC) patients that incorporates intralesional calcifications. We evaluated prevalence, temporal evolution, and genotype correlation of intra-tuberal calcifications in pediatric TSC patients, emphasizing susceptibility-weighted imaging (SWI) for detection. Materials and Methods: We retrospectively analyzed MRI scans of 57 unrelated pediatric TSC patients followed between 2014 and 2024 at a tertiary care center. Inclusion criteria included longitudinal imaging on the same 1.5T scanner, with T1w, T2w/FLAIR, and SWI sequences. CTs were classified into four MRI-based categories (A–D), with calcified tubers subdivided into micro-calcified and macro-calcified. Descriptive statistics, binomial tests, and Chi-square analyses were performed. Results: Calcified CTs were more prevalent than cystic ones. At baseline MRI, 63% of patients had calcified tubers (19% of all CTs), increasing to 77% at follow-up MRI (24% of all CTs). Micro-calcifications predominated at baseline MRI evaluation, though a significant proportion progressed to macro-calcifications over time. Calcified CTs always progressed from lower-grade lesions. Cystic tubers were rare (<1%). Longitudinal analysis showed significant variation in CTs with inner calcification count (p = 0.0000023), but not in CTs with cystic components (p = 0.42072). No significant genotype–radiological phenotype association emerged. Conclusions: Intralesional calcifications in CTs are dynamic and detectable with SWI. The inclusion of calcification patterns in CT classification could offer insights that may prove useful for future prognostic and risk-stratification frameworks in pediatric TSC. Full article

The rapid growth of construction and demolition (C&D) waste calls for sustainable recycling in concrete production. However, most studies address single-source recycled aggregates, leaving the behavior of mixed recycled systems insufficiently understood. To address this gap, this study investigates concrete in which natural aggregates are fully replaced by recycled concrete aggregate (RCA), and part of the RCA is further substituted with waste ceramic tile (WCT) or recycled crushed brick (RCB) at controlled proportions. Aggregate type and blending ratio were systematically examined, and Vickers microhardness was used to characterize the interfacial transition zone (ITZ). This study quantitatively establishes a micro–macro correlation between ITZ microhardness profiles and mechanical performance in fully recycled concretes with hybrid aggregate systems. Results show that high replacement levels (100% WCT and 100% RCB) reduced density by 5.6% and 7.0%, respectively, and increased water absorption, reaching 17.0% for 100% RCB. Optimal ultrasonic pulse velocity (UPV) occurred at intermediate blending ratios—50% for WCT and 75% for RCB. Mechanically, a 25% WCT substitution enhanced splitting tensile strength by 50%, whereas a 100% RCB substitution reduced compressive strength by 10.6%. Microhardness profiling revealed ITZ widths of about 70 μm for RCA and WCT, and 80 μm for RCB, consistent with corresponding macro-scale trends. These quantitative findings demonstrate that the rational blending of recycled aggregates can fine-tune the microstructure and performance of fully recycled concrete, providing insight into the high-value recycling of C&D waste. Full article

The study aimed to identify mineral determinants of potential therapeutic effects of Olea europaea L. leaves from introduced cultivars of the Southern Coast of Crimea and to assess their suitability for functional aqueous tisanes. Using ICP-OES, eighteen macro- and micro-elements were identified in dried leaves, with K and Ca predominant (>10.0 g/kg), followed by P, S, and Mg (>1.0 g/kg). Maximum values occurred in ‘Nikitskaya’ (K 15.6 g/kg; S 2.05 g/kg; and P 1.97 g/kg) and ‘Tlemcen’ (Ca 18.6 g/kg and Mg 1.46 g/kg). Extractability into infusion (2 g/100 mL, 60 min) reached 325 mg/L for K, 26 mg/L for Ca, 48 mg/L for S, 18 mg/L for P, and 9 mg/L for Mg. Potentially toxic elements were below detection limits, indicating the safety of both the raw material and beverage. Principal component, correlation, and Ward clustering analyses highlighted ‘Nikitskaya’, ‘Lomashenskaya’, and ‘Coregiolo’ as having the highest cumulative mineral value among the tested six cultivars. Overall, the findings support the feasibility of olive-leaf tisanes as accessible sources of K, Ca, S, P, and Mg, with potential contributions to antioxidant defense, blood-pressure regulation, and lipid and carbohydrate metabolism. Full article

Filling mining offers significant technical advantages in controlling rock mass movement and preventing disasters. Investigating the correlation between the macro- and micro-scale characteristics of filling materials will help optimize this process. The paper analyzes the variation patterns and mechanisms of the pore structure and mechanical strength characteristics of the filling body based on low-field nuclear magnetic resonance (NMR) technology and fractal theory, exploring the relationship between microstructure and macroscopic features. Results indicate that as the cement-to-sand ratio or mass concentration decreases, the total pore structure count in the filling material increases, predominantly consisting of micropores that account for over 76%. The complexity of total pores, micropores, mesopores, and macropores progressively decreases. Mechanical strength exhibits a positive correlation with both the cement-to-sand ratio and mass concentration. A reduced cement-to-sand ratio diminishes hydration products, lowering the cohesive strength of tailings particles. As mass concentration increases, the internal structure of the filling body becomes denser, enhancing its mechanical properties. An increase in pore number progressively improves pore connectivity, reducing fluid flow resistance. The porosity of the pore structure exhibits a strong correlation with fractal dimension, mechanical strength, and permeability coefficient, with a coefficient of determination ranging from 0.631 to 0.996. The strength prediction model constructed using mesopore porosity and material intrinsic characteristics also demonstrated excellent accuracy. Full article

Urban green spaces (UGS) are pivotal to urban sustainability, yet their morphology—patch size, shape, and configuration—remains insufficiently linked to institutional drivers. We investigate how land tenure strength shapes UGS morphology across 36 cities in nine countries. Using OpenStreetMap data, we delineate UGS and compute landscape metrics (AREA, PARA, SHAPE, FRAC, PAFRAC) via FRAGSTATS; we develop a composite index of land tenure strength capturing ownership, use-right duration, expropriation compensation, and government land governance capacity. Spearman’s rank correlations indicate a scale-dependent coupling: stronger tenure is significantly associated with micro-scale patterns—smaller patch areas and more complex, irregular boundaries—consistent with fragmented ownership and higher transaction costs, whereas macro-scale indicators (e.g., overall green coverage/connectivity) show weaker sensitivity. These findings clarify an institutional pathway through which property rights intensity influences the physical fabric of urban nature. Policy implications are twofold: in high-intensity contexts, flexible instruments (e.g., transferable development rights, negotiated acquisition, ecological compensation) can maintain network connectivity via embedded, fine-grain interventions; in low-intensity contexts, one-off land assembly can efficiently deliver larger, regular green cores. The results provide evidence-based guidance for aligning green infrastructure design with diverse governance regimes and advancing context-sensitive sustainability planning. Full article

The texture of asphalt pavement wears down over time due to traffic polishing, which leads to polished pavement surfaces with lower skid resistance. Three-dimensional (3D) texture parameters can be used to describe the evolution of pavement texture and establish predictive models for skid resistance. In this study, a high-resolution 3D laser scanner and a pendulum friction tester were used to collect 3D texture data and the corresponding friction values of dense-graded asphalt pavement over a period of four years. Fourier transformer and Butterworth filters were applied to decompose the 3D texture data into micro-texture and macro-texture components. Twenty different 3D texture parameters from five categories (height, spatial, hybrid, functional, and feature parameters) were calculated from pavement micro- and macro-textures and optimized using correlation methods to derive an independent set of texture parameters. The performance of a multiple linear regression model and neural network predictive model for predicting skid resistance via selected texture parameters was compared through training and testing. The results indicate that pavement micro-texture contributes more significantly to skid resistance than macro-texture, and neural network models can effectively predict the temporal evolution of skid resistance based on texture data. The neural network model achieves R² values of 0.92 and 0.89 on the training and testing sets, respectively, with RMSE values of 3.37 and 5.45, significantly outperforming the multiple linear regression model (R² = 0.50). Full article

To overcome the limitations imposed by the anisotropy and heterogeneity of natural loess, this study establishes a novel quantitative macro–micro correlation framework for investigating the deformation mechanisms of artificial structural loess (ASL). ASL samples were prepared by mixing remolded loess with cement (0–4%) and NaCl (0–16%), followed by static compaction (95% degree) and 28-day curing (20 ± 2 °C, >90% RH) to replicate the structural properties of natural loess under controlled conditions. An integrated experimental methodology was employed, incorporating consolidation/collapsibility tests, particle size analysis, X-ray diffraction (XRD), and mercury intrusion porosimetry (MIP). A three-dimensional nonlinear model was proposed. The findings show that intergranular cementation, particle size distribution, and pore architecture are the main factors influencing loess’s compressibility and collapsibility. A critical transition from medium to low compressibility was observed at cement content ≥1% and moisture content ≤16%. A strong correlation (Pearson |r| > 0.96) was identified between the mesopore volume ratio and the collapsibility coefficient. The innovation of this study lies in the establishment of a three-dimensional nonlinear model that quantitatively correlates key microstructural parameters (fractal dimension value (D), clay mineral ratio (C), and large and medium porosity (n)) with macroscopic deformation indicators (porosity ratio (e) and collapsibility coefficient (δ_s)). The measured data and the model’s output agree quite well, with a determination coefficient (R²) of 0.893 for porosity and 0.746 for collapsibility, verifying the reliability of the model. This study provides a novel quantitative tool for loess deformation prediction, offering significant value for engineering settlement assessment in controlled cementation and moisture conditions, though its application to natural loess requires further validation. Full article

A student’s active thinking state directly affects their learning experience in the classroom. To help teachers understand students’ active thinking states in real-time, this study aims to construct a model which characterizes their active thinking states. The main research objectives are as follows: (1) to achieve accurate classification of the cognitive levels of in-class exercises; (2) to effectively quantify the active thinking state of students through analyzing the correlation between student cognitive levels and exercise cognitive levels. The research methods used in this study to achieve these objectives are as follows: First, LSTM and Chinese-RoBERTa-wwm models are integrated to extract sequential and semantic information from plain text while TBCC is used to extract the semantic features of code text, allowing for comprehensive determination of the cognitive level of exercises. Second, a cognitive diagnosis model—namely, the QRCDM—is adopted to evaluate students’ real-time cognitive levels with respect to knowledge points. Finally, the cognitive levels of exercises and students are input into a self-attention mechanism network, their correlation is analyzed, and the thinking activity state is generated as a state representation. The proposed text classification model outperforms baseline models regarding ACC, micro-F1, and macro-F1 scores on two sets of exercise datasets in Chinese containing mixed code texts, with the highest ACC, micro-F1, and macro-F1 values reaching 0.7004, 0.6941, and 0.6912, respectively. This proves the proposed model’s effectiveness in classifying the cognitive level of exercises. The accuracy of the thinking activity state characterization model reaches 61.54%. In particular, this is higher than the random baseline, thus verifying the model’s feasibility. Full article

Screw piles are widely used in infrastructure, such as railways, highways, and ports, etc., owing to their large pile resistance compared to unthreaded piles. While most screw pile research focuses on single pile behavior under rotational installation using torque-capacity correlations. Limited studies investigate group effects under alternative installation methods. In this study, the load-transfer mechanism of screw piles and soil displacement under vertical installation was explored using laboratory model tests combined with digital image correlation techniques. In addition, numerical simulations using the discrete element method were performed. Based on both lab tests and numerical simulation results, it is discovered that the ultimate bearing capacity of a single screw pile was approximately 50% higher than that of a cylindrical pile with the same outer diameter and length. For pile groups, the group effect coefficient of a triple-pile group composed of screw piles was 0.64, while that of cylindrical piles was 0.55. This phenomenon was caused by the unique thread-soil interaction of screw piles. The threads generated greater side resistance and reduced stress concentration at the pile tip compared with cylindrical piles. Moreover, the effects of pile type, pile number, embedment length, pile spacing, and thread pitch on pile resistance and soil displacement were also investigated. The findings in this study revealed the micro–macro correspondence of screw pile performance and can serve as references for pile construction in practice. Full article

This research evaluates the stabilization of a clay collected from the northern expansion zone of Cartagena de Indias, Colombia. Laboratory analyses, including particle size distribution, Atterberg limits, compaction, specific gravity, and XRF/XRD, classified the soil as a highly plastic clay (CH) with moderate dispersivity, as confirmed by pinhole and crumb tests. The soil was treated with 3–9% lime, with and without the addition of NaCl (0% and 2%), and tested for unconfined compressive strength (q_u), small-strain stiffness (G_o), and microstructural properties under curing periods of 14 and 28 days at two compaction densities. Results showed that lime significantly improved mechanical behavior, while the inclusion of NaCl further enhanced q_u (up to 185%) and G_o (up to 3-fold), particularly at higher lime contents and curing times. Regression models demonstrated that both q_u and G_o follow power-type relationships with the porosity-to-lime index, with consistent exponents (−4.75 and −5.23, respectively) and high coefficients of determination (R² > 0.79). Normalization of the data yielded master curves with R² values above 0.90, confirming the robustness of the porosity-to-lime framework as a predictive tool. The G_o/q_u ratio obtained (3737.4) falls within the range reported for cemented geomaterials, reinforcing its relevance for comparative analysis. SEM observations revealed the transition from a porous, weakly aggregated structure to a dense matrix filled with C–S–H and C–A–H gels, corroborating the macro–micro correlation. Overall, the combined use of lime and NaCl effectively converts dispersive clays into non-dispersive, mechanically improved geomaterials, providing a practical and sustainable approach for stabilizing problematic coastal soils in tropical environments. Full article

Tight sandstone reservoirs generally exhibit poor physical properties and characterization of microscopic pore structure is crucial for evaluating reservoir quality and fluid flow behavior. Fractal dimension provides an effective means to quantify the complexity and heterogeneity of pore structures in such reservoirs. This study investigates tight sandstone reservoirs of the Kongdian Formation in the Nanpi Slope, Cangdong Sag, using cast thin sections, scanning electron microscopy (SEM), high-pressure mercury injection (HPMI), and constant-rate mercury injection (CRMI) experiments. We establish a full-range fractal model to characterize pore-throat distributions and elucidate the correlation between fractal dimensions and reservoir properties, alongside factors influencing pore-structure heterogeneity. Key findings include that (1) pore types are predominantly residual intergranular pores, intergranular dissolution pores, and clay mineral intercrystalline pores, with throats primarily consisting of sheet-like and curved sheet-like types, exhibiting strong pore-structure heterogeneity; (2) full-range fractal dimensions D₁, D₂ and D₄ effectively characterize the heterogeneity of pore structure, where higher D₁ and D₂ values correlate with increased macro–mega pore and micro-fine throat abundance, respectively, indicating enhanced pore connectivity and superior flow capacity, while elevated D₄ reflects greater nano throat complexity, degrading reservoir properties and impeding hydrocarbon flow; (3) compared to conventional methods splicing HPMI and CRMI data at 0.12 μm, the fractal-derived integration point more accurately resolves full-range pore-throat distributions, revealing significant disparities in pore-throat size populations; (4) the fractal dimensions D₁, D₂, and D₄ are collectively governed by clay mineral content, average throat radius, displacement pressure, and tortuosity. Full article