Search Results (5,052)

The centrifuge method serves as an efficient and rapid approach for determining the soil–water characteristic curve (SWCC). However, soil shrinkage during centrifugation remains overlooked and prior modified methods may suffer from complex operations, high costs, time consumption, and limited applicability. To address these issues, this study introduces a simple correction scheme (G3) for determining drying SWCCs using the centrifuge method based on high matric suction calibration points. The performance of the proposed G3 method was systematically evaluated against a modified method considering soil shrinkage (G1) and the conventional uncorrected method (G2). Results revealed significant soil linear shrinkage post-centrifugation, accompanied by a reduction in total soil porosity and an increase in soil bulk density. SWCCs from all methods exhibited strong consistency at low matric suction ranges but diverged markedly at high matric suction segments. High matric suction data dominated the SWCC fitting. The G1 method achieved the highest fitting accuracy, while the G3 method performed the worst yet maintained acceptable reliability. The G2 method yielded optimal SWCC for simulating saturated soil water content, field capacity, and permanent wilting point. Conversely, Hydrus-1D simulations revealed superior performance of the G3 method in simulating farmland soil moisture dynamics during the dehumidification process. Values of R² across methods followed G3 > G1 > G2, while mean absolute error, mean absolute percentage error, and root mean square error exhibited the opposite trend. These findings highlight that the previous modified approaches are more suitable for low and medium matric suction ranges. The proposed correction method enhances drying SWCC performance across the full matric suction range, offering a practical refinement for the centrifuge method. This advancement could enhance the reliability in soil hydraulic characterization and contribute to a better understanding of the hydraulic–mechanical–chemical behavior in soils. 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

In the manufacture of single-crystal blades for aero-engines, the problem of eutectic aggregation on the upper surface of the blades has long been restricting the casting performance improvement. To investigate this phenomenon, this paper employs a simplified blade-like shape casting and focuses a 3rd generation nickel-based single-crystal superalloy as the research material. A systematic analysis is conducted to elucidate the distribution of γ/γ’ eutectic during solidification. Experimental results show distinct spatial variations in γ/γ’ eutectic distribution. Pronounced eutectic aggregation is observed on the upper surface of the blade but with sparse eutectic dispersion‌ on the lower regions of the casting. Relatively uniform eutectic distribution‌ dominates the mid-section of the specimen. To unravel the underlying mechanisms, this paper utilized a ‌multiphase volume-averaged solidification model‌, developed in prior work, to numerically simulate the γ/γ’ eutectic evolution during directional solidification. This computational framework enabled a comprehensive ‌quantitative analysis‌ of spatial and temporal variations in the eutectic volume fraction along the solidification direction. The integration of experimental and modeling approaches provides critical insights into the interplay between thermal gradients, alloy composition, and microstructural heterogeneity. Full article

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that significantly increases the risk of stroke, with prediabetes serving as an intermediate stage marked by similar pathophysiological mechanisms such as inflammation and vascular dysfunction. This study investigated the relationship between prediabetes and stroke-related biomarkers in individuals aged 25–45 years in Durban, South Africa. After obtaining ethical approval, a retrospective analysis was performed on blood samples from 100 participants recruited from King Edward Hospital and Inkosi Albert Luthuli Central Hospital. Participants were classified as non-prediabetic (n = 30), prediabetic (n = 35), or type 2 diabetic (n = 35) according to ADA criteria. Plasma concentrations of C-reactive protein (CRP), interleukin-6 (IL-6), fibrinogen, D-dimer, calcium binding protein (S100B), glial fibrillary acidic protein (GFAP), and neuron-specific enolase (NSE) were measured using enzyme-linked immunosorbent assay (ELISA). It is important to note that none of the participants had confirmed stroke events; these biomarkers were assessed as surrogate indicators of stroke risk. Statistical analyses included one-way ANOVA with Tukey–Kramer tests and Pearson’s correlations. Biomarker concentrations were significantly elevated in prediabetic individuals compared to non-prediabetic controls, with levels further increasing in T2DM. Strong positive correlations were observed between S100B and both HbA1c (r = 0.75, p < 0.0001) and fasting glucose (r = 0.75, p < 0.0001). These findings suggest that inflammatory, coagulation, and neurovascular biomarkers, particularly S100B, may indicate early stroke risk in prediabetes. Further investigation into these biomarkers could improve early detection strategies and stroke prevention efforts in at-risk populations. Full article

Background: Patients with chronic lymphocytic leukemia (CLL) who were not receiving treatment were included in this experimental prospective correlation study. We aimed to elucidate the complex relationship between smudge cells, surface CD20, and soluble CD20 in CLL patients. Methods: We created blood smears from blood samples collected from our patients using a manual technique consistently performed by the same technician. The May–Grunwald Giemsa dye was used to stain all of the slides. The B-cell phenotypic was analyzed using the FacsCanto II flow cytometer (Becton Dickinson, CA, USA) at the time of diagnosis. Competitive Enzyme-Linked Immunoassay (ELISA) was used to quantitatively assess the amounts of soluble CD20/MS4A1. Results: The percentage of smudge cells and soluble CD20 antigen levels were shown to be significantly inversely correlated, suggesting a considerable link (correlation coefficient (r) = −0.51, p = 0.006). Similarly, a significant inverse relationship (r = −0.36, p = 0.04) was found by the Spearman correlation test between the smudge cell ratio and CD20 median fluorescence intensity (MFI) on cell surfaces. Soluble CD20/MS4A1 and surface CD20 MFI were shown to have a weakly positive association that was almost statistically significant (Spearman’s rho = 0.34, p = 0.064). With a sensitivity of 69% and specificity of 86%, we discovered that a cut-off value of 2.2 ng/dL for soluble CD20 predicted higher smudge cells (area under the curve (95% confidence interval (CI)): 0.75 (0.57 to 0.93), p = 0.021). Conclusions: We found a significant inverse association between smudge cells and both surface CD20 and soluble CD20/MS4A1 in our study examining the correlation between smudge cells, soluble CD20, and CD20/MS4A1 in CLL patients. Our findings indicate that soluble CD20 may contribute to understanding the pathophysiology of smudge cells and could be further investigated as a potential prognostic marker in CLL. Full article

Children with attention deficit hyperactivity disorder (ADHD) often exhibit different oculomotor behavior compared to their typically developing peers. Research shows that eye movement patterns can provide important information about attention mechanisms. While eye movements have been examined in various cognitive contexts, this study investigated their role in a task designed to assess their potential as indicators of attention functioning in children with ADHD. Specifically, we assessed tonic attention, attentional focus, and selective attention. Seventy participants aged 9–12 years with DSM-5 ADHD-I and ADHD-C types participated in our research. We then included the results of 57 participants in our study. We used the d2-R attention test and the Reaction alertness test to determine the specifics we were looking for. We used Eye Tracking Glasses (ETG) 2w to capture eye movements. The results show that quiet eye (QE) duration does not reliably predict visuomotor performance in this population. Our findings further suggest that in children with ADHD, the QE phase is not the primary period for acquiring visual information important for movement planning; rather, relevant information is gathered earlier in the process. Conversely, prolonged onset and duration of QE were associated with poorer attentional efficiency, suggesting that in ADHD, longer QE may reflect slower or less efficient cognitive processing rather than increased control. Full article

Photovoltaic/Thermal (PV/T) technology efficiently harnesses solar energy by co-generating electricity and hot water. Unlike conventional PV systems, PV/T systems improve thermal utilization, cool PV modules, and prevent performance degradation caused by high temperatures. Among the various PV/T configurations, micro-channel heat pipe (MCHP) systems are prominent due to their ability to enhance heat transfer through the use of vacuum-filled, refrigerant-sealed MCHPs. This study explores how factors such as working fluid type, evaporation section heat flux, fill ratio, and condensation section length impact system performance. A 3D steady-state CFD model simulating phase-change heat transfer was developed to analyze thermal and electrical efficiencies. The results reveal that R134a outperforms acetone in heat transfer, with thermal resistance showing a significant decrease (from 0.5 °C·W⁻¹ at a 30% fill rate to 0.3 °C·W⁻¹ at a 70% fill rate) under varying heat source powers. The optimal fill ratio depends on the heat flux; for powers up to 70 W, the fill ratio ranges from 30% to 50%, while above 70 W, it shifts to 60–80%. Additionally, a longer condensation section reduces thermal resistance by up to 30% and enhances heat transfer efficiency, improving the overall system performance by 10%. These findings offer valuable insights into optimizing MCHP PV/T systems for increased efficiency. Full article

This study investigates the effects of rumen-protected methionine and lysine (RPML) on the fecal microbiota of Leizhou goats, focusing on growth performance and fecal microbial community composition. A total of 10 three-month female Leizhou goats (9.90 ± 0.08 kg) were randomly assigned to one of two dietary treatments: a CON group fed a basal diet and an RPML group receiving the basal diet supplemented with 1.5 g/d/head of rumen-protected methionine and 4.5 g/d/head of rumen-protected lysine. Results indicated that RPML significantly enhanced average daily gain (ADG) and final body weight (FBW), as well as significantly decreased the ratio of dry matter intake (DMI) to ADG (p < 0.001). Fecal microbiota composition showed a decrease in abundance of UCG-005, Phascolarctobacterium, and norank_f__Bacteroidales_RF16_group and an increase in others like Christensenellaceae R-7 and unclassified_c__Clostridia (p < 0.05). Moreover, the correlations between the abundance of certain bacterial genera and the concentrations of short-chain fatty acids (SCFAs) suggest that the modulation of the gut microbiota is associated with improved growth performance and feed efficiency in Leizhou goats, indicating that RPML supplementation can modulate the gut microbiota to improve growth performance and feed efficiency in Leizhou goats. Full article

Stand density is one among a multitude of factors impacting the growth of trees and their responses to climatic variables, but its effect on wood quality at the scale of anatomical structure is hardly investigated. Therefore, we analyzed the radial growth and wood structure of Siberian spruce (Picea obovata Ledeb.) and Scots pine (Pinus sylvestris L.) in an experimental conifer plantation with a wide gradient of stand density in the Siberian southern taiga. The measured and indexed chronologies of the tree-ring width (TRW), number of tracheid cells per radial row in the ring produced in the cambial zone (N), cell radial diameter (D), and cell wall thickness (CWT) demonstrated the influence of the planting density. The TRW and N have a negative allometric dependence on the stand density (R² = 0.75–0.88), likely due to competition for resources. The consistent negative dependence of the D on the stand density (R² = 0.85–0.97) is log-linear and also seems to be related to tree size, while the CWT is not significantly dependent on the stand density. These findings can be used as insights in regulating cellular structure and procuring desired wood quality by silvicultural means. Both conifer species have similar climatic reactions. We observed significant suppression of TRW and D related to water deficit in May–July (both species), as well as frosty (more for pine) and low-snow (for spruce) conditions in winters, as shown by both dendroclimatic correlation and pointer year analysis. Temporal shifts in the climatic responses indicate later transition to latewood and growth cessation in sparse stands, especially in spruce. Better performance was observed in sparce and medium-density stands for both species. Full article

As a systematic project, corporate green innovation involves technological, organizational, and environmental dimensions. Therefore, its effective functioning is contingent on guidance from internal leadership. STEM represents an integration of science, technology, engineering, and mathematics education. A STEM CEO is a chief executive officer holding a degree in science, engineering, agriculture, or medicine. However, research on the impact of STEM CEOs on green innovation is limited. Using data from Chinese listed manufacturing firms from 2010 to 2023, panel fixed effects models reveal that STEM CEOs positively influence corporate green innovation. Further analysis indicates that alleviating financing constraints, fostering external collaboration, increasing R&D investment, and improving the efficiency of innovation resource allocation are key pathways through which STEM CEOs enhance green innovation output. Furthermore, this impact is positively moderated by the level of green finance development and the intensity of market competition. Finally, heterogeneity tests demonstrate that these positive effects are more pronounced for firms with high public environmental concern, in non-heavily polluting industries, with strong ESG performance, and in highly competitive industries. These findings underscore the role of STEM leaders in enhancing the output of green innovation systems, offering actionable insights into the interaction between STEM CEOs and the external environment. Full article

This study investigated the individual and synergistic impacts of arbuscular mycorrhizal fungi (AMF) inoculation and foliar-applied silicon nanoparticles (SiNPs) on the yield parameters, volatile profile, and phenolic composition of two Rosa damascena genotypes (D231 and C193). Experiments were conducted using a split–split–plot design, involving AMF inoculation (main plot), three SiNPs concentrations (subplot), and two rose genotypes (sub-subplot). The results demonstrated that AMF, SiNPs, and genotype all had significant and interactive effects on flower yield parameters. Foliar application of SiNPs, particularly when combined with AMF inoculation, consistently enhanced flowering parameters, including flower size, number, and weight across both genotypes. High-performance liquid chromatography (HPLC) further confirmed that phenolic acids (vanillic acid and rutin) increased following foliar application of SiNPs and AMF root colonization, particularly in genotype C193. SPME-Arrow analysis revealed that alcohols, ketones, and terpenes were the predominant volatile constituents. Phenethyl alcohol was the most abundant compound, accounting for approximately 84.69% of the total aroma content and contributing significantly to the ‘rose’ aroma. Other major volatiles included 2-undecanone (4.42%), benzyl alcohol (2.97%), and citronellol (1.95%); however, their levels varied depending on treatment and genotype. These findings highlight that the combined application of AMF and SiNPs offers a sustainable approach to enhancing both the quantitative yield and qualitative phytochemical composition (essential oil components and phenolic compounds) of R. damascena, providing a scientific foundation for optimizing its production in organic farming systems. Full article

Background: Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality, yet its pathophysiology is not fully understood. Recent studies suggest that dysregulated maternal immune responses, particularly involving decidual Natural Killer (dNK) cells and immune checkpoint molecules such as B7-H3, may play a role in the pathogenesis of this heterogeneous syndrome, particularly in the development of early-onset preeclampsia (EOP). Objective: The aim of this study was to investigate the expression patterns of B7-H3 on extravillous trophoblasts (EVTs) and the abundance of dNK cells in preeclamptic versus normotensive pregnancies and to analyze the relationship between these two immune parameters. Methods: A cross-sectional study was conducted using 42 placental samples (21 preeclampsia, 21 controls). Immunohistochemistry (IHC) was performed to detect CD56 (dNK cells) and CD276 (B7-H3) expression. Expression was semi-quantitatively evaluated using the Remmele Immunoreactive Score (IRS). Statistical comparisons and correlation analyses were conducted. Results: Preeclamptic placentas exhibited significantly higher dNK cell expression (IRS 7.19 ± 2.16) and significantly lower B7-H3 expression (IRS 2.63 ± 0.90) compared to controls (p < 0.001 and p = 0.002, respectively). A positive correlation was found between B7-H3 and dNK cell expression in both groups, with a stronger correlation in normotensive pregnancies (r = 0.605; p = 0.004) and preeclampsia (r = 0.465; p = 0.034). Conclusions: The inverse expression pattern and reduction in B7-H3 expression compared to dNK cells in preeclampsia suggest a loss of immune tolerance at the maternal–fetal interface. These findings highlight the potential of B7-H3 as a biomarker and immunoregulatory target for early prediction and prevention of preeclampsia. Full article

Compaction quality control in earthworks and pavements still relies mainly on density-based acceptance referenced to laboratory Proctor tests, which are costly, time-consuming, and spatially sparse. Lightweight dynamic cone penetrometer (LDCP) provides rapid indices, such as $q_{d0}$ and $q_{d1}$, yet acceptance thresholds commonly depend on ad hoc, site-specific calibrations. This study develops and validates a supervised machine learning framework that estimates $q_{d0}$, $q_{d1}$, and $Z_c$ directly from readily available soil descriptors (gradation, plasticity/activity, moisture/state variables, and GTR class) using a multi-campaign dataset of $n=360$ observations. While the framework does not remove the need for the standard soil characterization performed during design (e.g., W, ${\gamma }_{d,\mathrm{field}}$, and $R{C}_{\mathrm{SPC}}$), it reduces reliance on additional LDCP calibration campaigns to obtain device-specific reference curves. Models compared under a unified pipeline include regularized linear baselines, support vector regression, Random Forest, XGBoost, and a compact multilayer perceptron (MLP). The evaluation used a fixed 80/20 train–test split with 5-fold cross-validation on the training set and multiple error metrics ($R^2$, RMSE, MAE, and MAPE). Interpretability combined SHAP with permutation importance, 1D partial dependence (PDP), and accumulated local effects (ALE); calibration diagnostics and split-conformal prediction intervals connected the predictions to QA/QC decisions. A naïve GTR-average baseline was added for reference. Computation was lightweight. On the test set, the MLP attained the best accuracy for $q_{d1}$ ($R^2=0.794$, RMSE $=5.866$), with XGBoost close behind ($R^2=0.773$, RMSE $=6.155$). Paired bootstrap contrasts with Holm correction indicated that the MLP–XGBoost difference was not statistically significant. Explanations consistently highlighted density- and moisture-related variables (${\gamma }_{d,\mathrm{field}}$, $R{C}_{\mathrm{SPC}}$, and W) as dominant, with gradation/plasticity contributing second-order adjustments; these attributions are model-based and associational rather than causal. The results support interpretable, computationally efficient surrogates of LDCP indices that can complement density-based acceptance and enable risk-aware QA/QC via conformal prediction intervals. Full article

Background/Objectives: Families of ICU patients endure intense psychological strain. While anxiety and depression are well documented, less attention has been given to hostility—expressed both outwardly as anger and inwardly as guilt or self-criticism. Hostility, however, often shapes the climate of the ICU waiting room and the collaboration between families and staff. This study examined the levels and forms of hostility among ICU relatives, focusing on demographic predictors that may influence extrapunitive and intropunitive reactions. Methods: A cross-sectional study was conducted between June 2018 and December 2019 with 215 family members of ICU patients. Hostility was assessed using the Hostility and Direction of Hostility Questionnaire (HDHQ). Descriptive statistics, t-tests, ANOVAs, and multivariate regression analyses were performed to examine the effects of age, gender, and education on hostility subscales. Results: Male relatives exhibited higher acting-out hostility (M = 4.80, SD = 2.63) compared with female relatives (M = 4.12, SD = 2.21; t(216) = 1.96, p = 0.05, Cohen’s d = 0.28). Relatives with lower educational attainment showed significantly higher total hostility (β = −1.23, 95% CI [−1.78, −0.67], p < 0.001) and greater self-criticism (β = −0.44, 95% CI [−0.84, −0.03], p = 0.037). Younger age was associated with increased acting-out hostility (β = −0.029, 95% CI [−0.055, −0.002], p = 0.035). The regression models explained 12–26% of the variance across subscales (R² range = 0.12–0.26). These findings suggest two vulnerability trajectories: an externalized (extrapunitive) pattern in younger men and a broad internalized (intropunitive) pattern in relatives with lower education. Conclusions: Hostility in ICU families emerges in two distinct trajectories: externalized anger among young men and broad hostility in relatives with lower education. Recognizing these patterns is vital for preventing conflict, addressing hidden guilt and self-blame, and developing subgroup-sensitive interventions. The ICU waiting room is a space not only of fear and uncertainty but also of anger, guilt, and fragile attempts at psychological survival—dimensions that deserve systematic attention in both clinical practice and research. Full article

Enhancing interlayer bond strength remains a critical challenge in the extrusion-based 3D printing of cementitious materials. This study investigates the optimisation of interlayer bond strength in extrusion-based 3D-printed cementitious materials through a combined application of Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs). Using a concise yet comprehensive dataset, RSM provided interpretable main effects, curvature, and interactions, while the ANN captured non-linearities beyond quadratic forms. Comparative analysis revealed that the RSM model achieved higher predictive accuracy ($R^2=0.95$) compared to the ANN model ($R^2=0.87$). Desirability-based optimisation confirmed the critical importance of minimising casting delays to mitigate interlayer weaknesses, with RSM suggesting a water-to-cement (W/C) ratio of approximately 0.45 and a minimal time gap of less than 5 min, while ANN predicted slightly lower optimal W/C values but with reduced reliability due to the limited dataset. Sensitivity analysis using partial dependence plots (PDPs) further highlighted that ordinary Portland cement (OPC) content and W/C ratio are the dominant factors, contributing approximately 2.0 and 1.8 MPa respectively to the variation in predicted bond strength, followed by superplasticiser dosage and silica content. Variables such as water content, viscosity-modifying agent, and time gap exhibited moderate influence, while sand and fibre content had marginal effects within the tested ranges. These results demonstrate that RSM provides robust predictive performance and interpretable optimisation guidance, while ANN offers flexible non-linear modelling but requires larger datasets to achieve stable generalisation. Integrating both methods offers a complementary pathway to advance mix design and process control strategies in 3D concrete printing. Full article