Search Results (1,157)

Stabilized/solidified (S/S) sediments are eroded by dry–wet cycles (DWs) when applied in an atmospheric environment. The microporous structures of S/S sediments, including their size, shape, and distributions, are sensitive to DWs and closely related to their macro properties. Therefore, multiscale micropore measurements, including scanning electron microscopy (SEM), mercury intrusion porosimetry (MIP), and nitrogen adsorption porosimetry (NAP), were conducted on S/S sediment samples subjected to different DWs to elucidate the micro-damage mechanisms of S/S sediments under DWs, in conjunction with unconfined compression strength (UCS) tests. The results indicated that, as DWs increased, the strength of the S/S sediments decreased, and pores/cracks developed due to the expansion of the calcium silicate hydrate (CSH) skeleton pore structures and the shrinkage of the sediment aggregate pore structures. Pores that accounted for most of the volume were those in the sediment aggregates in the range of 10² nm < d < 10⁴ nm (d represents the pore diameter), while smaller pores in the range of d < 10² nm contributed 90% of the pore area. Pores in the CSH skeletons (d > 10⁴ nm) increased with DWs, while those in the sediment aggregates decreased with DWs due to the expansion and shrinkage forces originating from the sediment particles with pore sizes of d < 10 nm. The plastic deformation of the pores in the CSH skeletons and sediment aggregates jointly controlled the strength of S/S sediments, and the adjustment of those pores gradually reduced the decrease rate of UCS. The revealed damage mechanisms of S/S during DWs provide theoretical foundations for optimizing S/S additives and expanding the engineering applications of the S/S sediments. Full article

The monorail tourism transit system (MTTS) is a large-scale amusement facility. Currently, there is limited theoretical research on the vehicle–bridge coupling vibration and dynamic amplification factor (DAFs) of this system. The values specified in relevant standards are not entirely reasonable; for instance, the calculated value of the DAFs in the “Large-scale amusement device safety code (GB 8408-2018)” only takes speed into account and is set at 0.44 when the speed is between 20 and 40 km/h. This is overly simplistic and obviously too large. This paper aims to establish a reasonable expression of the DAFs for the MTTS and improve the design code of the industry. Firstly, using on-site trials of the project and the dynamics numerical simulation method, the dynamic response characteristics of the MTTS and the influencing factors of the DAFs were systematically analyzed. The rationality and accuracy of the model were verified. Secondly, combined with the joint simulation model, the dynamic influence mechanism of multifactor coupling on the DAFs was revealed. On this basis, the key regression parameters were selected by using the Pearson correlation coefficient method and the random forest algorithm, and the DAFs prediction model was constructed based on the least absolute shrinkage and selection operator (LASSO) regression theory. Finally, through cross-comparison of simulation data and specification verification, a recommended calculation expression of the DAFs for the MTTS was proposed. The research results show that the established prediction model can predict 94.50% of the variation information of the DAFs of the MTTS and pass the 95% confidence level and 0.05 significance test. The accuracy is high and relatively reasonable and can provide a reference for the design of the MTTS. Full article

In order to improve the injection molding quality of the car lamp shell, orthogonal test, signal-to-noise ratio, gray correlation analysis, and CRITIC weight method were used to analyze the influence of mold temperature, melt temperature, injection time, velocity to pressure control, pressure holding pressure and pressure holding time on the shrinkage index and the total deformation of warpage, and fully consider the difference and correlation between the evaluation parameters. The multi-objective optimization is transformed into single-objective optimization, and the optimal parameter set is obtained. The experimental results show that, compared with the initial analysis results, the indentation index of the headlight shell is reduced by 33.95%, the total warpage deformation is reduced by 13.99%, and the forming quality of the headlight shell is improved. The research results provide a theoretical reference value for multi-objective optimization of plastic injection molding process parameters. Full article

Background: The pathogenesis of neuropsychiatric lupus erythematosus (NPSLE) is very complex and is associated with neuroinflammation and blood–brain barrier compromise. Experimental investigations of NPSLE have classically relied on spontaneous models. Recently, TLR7 agonist-induced lupus has been shown to exhibit similar neuropsychiatric manifestations to spontaneous ones. Cinnamon is a widespread spice and natural flavoring agent. It has been proven to modulate vascular endothelial tight junctions, neuroinflammation, and autoimmunity pathways, but it has never been tested in relation to lupus. Hypothesis/Purpose: In this pilot study, we aimed to explore the disease-modifying effect of Cinnamomum cassia on NPSLE in a TLR7 agonist-induced model. Study Design: An experimental design was followed in this study. Methods: Lupus was induced in C57BL/6J female mice via the direct application of imiquimod, a TLR7 agonist (5% imiquimod cream, 1.25 mg three times weekly), to the skin. Mice were divided into five groups (n = 8 per group): a sham group (S), a sham group supplemented with cinnamon (SC), an imiquimod-treated group (L), an imiquimod-treated group supplemented with cinnamon starting from induction (LC), and an imiquimod-treated group supplemented with cinnamon beginning two weeks prior to induction (CLC). This protocol was followed for six consecutive weeks. Cinnamomum cassia powder was administered orally at 200 mg/kg, 5 days per week. Results: Behavioral alterations were significantly ameliorated in the CLC group compared to lupus mice. Neuronal shrinkage and nuclear chromatin condensation were visible in the hippocampal cornu ammonis and dentate gyrus zones of lupus mice, with an increased expression of TLR7 and NLRP3, versus significantly less neurodegeneration and TLR7 and NLRP3 expression in the CLC group. In addition, the expression of the blood–brain barrier endothelial cell tight junction proteins claudin-1, occludin, and ZO-1 was abnormally modified in lupus mice and was restored in the CLC group. Moreover, while the cell–cell border delocalization of claudin-1 was documented in cultured blood–brain barrier endothelial cells treated with the plasma of lupus mice to a punctate intracytoplasmic fluorescence pattern, only cells treated with the plasma of the CLC group exhibited a complete reversal of this redistribution of claudin-1. Finally, cinnamaldehyde seemed to interact with TLR7 at multiple sites. Conclusions:Cinnamomum cassia seems to alleviate the pathogenesis of NPSLE. Supplementation with Cinnamomum cassia could be of great interest to modulate the activity and severity of the disease. Full article

Background/Objectives: Hospital readmissions are a key quality metric impacting both patient outcomes and healthcare costs. Traditional logistic regression models, including the LACE index (Length of stay, Admission type, Comorbidity index, and recent Emergency department visits), are commonly used for readmission risk stratification, though their accuracy may be limited by non-linear interactions with other clinical variables. This study compared the predictive performance of non-linear machine learning (ML) models with stepwise logistic regression (LR) and the LACE index for predicting 30-day general medicine readmissions. Methods: We retrospectively analysed adult general medical admissions at a tertiary hospital in Australia from 1 July 2022 to 30 June 2023. Thirty-two variables were extracted from electronic medical records, including demographics, comorbidities, prior healthcare use, socioeconomic status (SES), laboratory data, and frailty (measured by the Hospital Frailty Risk Score). Predictive models included stepwise LR and four ML algorithms: Least Absolute Shrinkage and Selection Operator (LASSO), random forest, Extreme Gradient Boosting (XGBoost), and artificial neural networks (ANNs). Performance was assessed using the area under the curve (AUC), with comparisons made using DeLong’s test. Results: Of 5371 admissions, 1024 (19.1%) resulted in 30-day readmissions. Readmitted patients were older and frailer and had more comorbidities and lower SES. Logistic regression (LR) identified the key predictors of outcomes, including heart failure, alcoholism, nursing home residency, and prior admissions, achieving an AUC of 0.62. LR’s performance was comparable to that of the LACE index (AUC = 0.61) and machine learning models: LASSO (AUC = 0.63), random forest (AUC = 0.60), and artificial neural networks (ANNs) (AUC = 0.60) (p > 0.05). However, LR significantly outperformed XGBoost (AUC = 0.55) (p < 0.05). Conclusions: About one in five general medicine patients are readmitted within 30 days. Traditional LR performed as well as or better than ML models for readmission risk prediction. Full article

Mineral admixtures exhibit significant enhancement effects on the seawater corrosion resistance of sulfoaluminate cement (SAC). This study systematically investigates the influence mechanisms of fly ash (FA), silica fume (SF), and slag powder (SP) on the physicochemical properties of SAC-based materials. Experimental results demonstrate that FA effectively enhances the fluidity of fresh SAC paste while mitigating drying shrinkage. Under standard curing conditions, the compressive strength of SAC mortar decreases with increasing FA content, reaching optimal performance at a 5% replacement level. However, in seawater immersion environments, FA undergoes chemical activation induced by seawater ions, leading to a positive correlation between mortar strength and FA content, with the 10% replacement ratio demonstrating maximum efficacy. SF addition reduces workability but significantly suppresses shrinkage deformation. While exhibiting detrimental effects on flexural strength under standard curing (optimal dosage: 7.5%), a 5.0% SF content manifests superior seawater resistance in marine environments. SP incorporation minimally impacts mortar rheology but exacerbates shrinkage behavior, showing limited improvement in both standard-cured compressive strength and seawater corrosion resistance. Orthogonal experimental analysis reveals that SF exerts the most pronounced influence on SAC mortar fluidity. Both standard curing and seawater immersion conditions indicate FA as the dominant factor affecting mechanical strength parameters. The optimal composite formulation, determined through orthogonal combination testing, achieves peak compressive strength with 5% FA, 5% SF, and 5% SP synergistic incorporation. Full article

Clear cell renal cell carcinoma (ccRCC) is distinguished by metabolic irregularities and unique immunological profiles. Nevertheless, the comprehensive examination of immune and metabolic attributes within the tumor microenvironment of ccRCC remains inadequately elucidated. In this study, we identified two distinct molecular subtypes (C1 and C2) of ccRCC using the non-negative matrix factorization (NMF) algorithm. Utilizing univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we developed a prognostic signature comprising eight immune- and metabolism-related genes (IMRGs) associated with the tumor microenvironment. The validation of this signature was performed using both testing and entire datasets. A nomogram was developed using IMRGs prognostic signature and various clinical parameters, including age and TNM stage. We also performed the in vitro experiments to validate the carcinogenic role of PYCR1 in ccRCC cells. Subtype C1 exhibited a more favorable prognosis and higher levels of immune cell infiltration compared to subtype C2. The AUCs of the nomogram at 1-, 3-, and 5-year intervals (AUC = 0.874, 0.820, and 0.794) were slightly higher than those of the IMRGs signature alone (AUC = 0.773, 0.755, and 0.764). The association between risk score and immune checkpoint expressions, immunophenoscore (IPS), and microsatellite instability (MSI) collectively predicted treatment efficacy accurately. Additionally, in vitro experiments confirmed the involvement of PYCR1 in promoting the aggressive behaviors of ccRCC cells, as evidenced by reduced proliferation, invasion, and enhanced apoptosis upon PYCR1 knockdown. In conclusion, the IMRGs signature shows promise in predicting prognostic risk, assessing the effectiveness of immunotherapy, and tailoring treatment for ccRCC patients. Full article

Background/Objectives: Cirsium setosum (commonly known as thistle) is a traditional Chinese medicinal plant with significant therapeutic potential, exhibiting hemostatic, antioxidant, and wound-healing properties. Electrospinning offers a versatile platform for fabricating nanoscale scaffolds with tunable functionality, making them ideal for drug delivery and tissue engineering. Methods: In this study, a bioactive extract from thistle was obtained and incorporated into a thermosensitive triblock copolymer (PNNS) and polycaprolactone (PCL) to develop a multifunctional nanofibrous scaffold for enhanced wound healing. The prepared nanofibers were thoroughly characterized using Fourier-transform infrared spectroscopy (FTIR), contact angle measurements, thermogravimetric analysis (TGA), and tensile fracture testing to assess their physicochemical properties. Results: Notably, the inclusion of PNNS imparted temperature-responsive behavior to the scaffold, enabling controlled deformation in response to thermal stimuli—a feature that may facilitate wound contraction and improve scar remodeling. Specifically, the scaffold demonstrated rapid shrinkage at a physiological temperature (38 °C) within minutes while maintaining structural integrity at ambient conditions (20 °C). In vitro studies confirmed the thistle extract’s potent antioxidant activity, while in vivo experiments revealed their effective hemostatic performance in a liver bleeding model when delivered via the composite nanofibers. Thistle extract and skin temperature-responsive contraction reduced the inflammatory outbreak at the wound site and promoted collagen deposition, resulting in an ideal wound-healing rate of above 95% within 14 days. Conclusions: The integrated strategy that combines mechanical signals, natural extracts, and electrospinning nanotechnology offers a feasible design approach and significant technological advantages with enhanced therapeutic efficacy. Full article

Superabsorbent polymers have been introduced into cementitious materials to solve issues related to early-age cracking, caused by shrinkage, and manual repair. A general improvement of autogenous healing is noticed, while the extent and effectiveness depend on the type of hydrogel and the amount included. To evaluate the self-healing effectiveness, the regain of mechanical performance needs to be assessed. However, such evaluation requires destructive testing, meaning that the healing progress cannot be followed over time. As a solution, air-coupled ultrasonic testing was used within this study, adopting a novel laser interferometer as a receiver, to estimate the regained properties of cementitious mixtures with and without superabsorbent polymers. The sensitivity of ultrasonic waves to the elastic properties of the material under study allows us to monitor the crack healing progress, while the semi-contactless nature of the procedure enables an easy and reliable measurement. Up to 80% recovery in ultrasonic velocity was achieved with reference concrete, while SAP concrete demonstrated up to 100% recovery after wet–dry curing. Following microscopic analysis, up to 19% visual crack closure was obtained for reference concrete, compared to a maximum of 50% for SAP mixtures, for average crack widths between 250 µm and 450 µm. Full article

The stabilization of expansive soils is crucial for the construction projects to mitigate swelling, shrinkage, and bearing capacity issues. This study investigates the synergistic effects of cement and clinoptilolite zeolite on stabilizing high-plasticity clay (CH) soil from Kano State, Nigeria. A total of 30 admixture combinations—cement (0–8%) and zeolite (0–15%)—were tested via standardized laboratory methods to evaluate their free swell index (FSI), swell percentage, swell pressure, shrinkage, and California Bearing Ratio (CBR). Principal component (Lasso) “least absolute shrinkage and selection operator” regression modeled interactions between admixtures and soil properties. The key results include the following: (1) 6% cement + 12% zeolite reduced the FSI by 60% (45 → 18); (2) 8% cement + 15% zeolite decreased the swell percentage by 47.8% (22.5% → 11.75%); (3) 6% cement + 12% zeolite lowered swell pressure by 54.2% (240 kPa → 110 kPa); (4) 8% cement + 12% zeolite reduced shrinkage by 50% (5.6% → 2.8%); and (5) 6% cement + 9% zeolite achieved an unsoaked CBR of 80.01% and soaked CBR of 72.79% (resilience ratio: 0.8010). PCLR models explained 93.5% (unsoaked) and 75.0% (soaked) of the CBR variance, highlighting how zeolite’s mediation analysis indicates that zeolite improves the bearing capacity mainly by reducing the free swell index (path coefficient = −0.91429, p < 0.0001), while conditional process modeling provided greater explanatory power (R² = 0.745) compared to moderation-only analysis (R² = 0.618). This study demonstrates that zeolite–cement blends optimize strength and resilience in expansive soils, with implications for sustainable infrastructure in arid and semi-arid regions. Full article

Fractional cointegration has been extensively examined in time series analysis, but its extension to heterogeneous panel data with unobserved heterogeneity and cross-sectional dependence remains underdeveloped. This paper develops a robust framework for testing fractional cointegration in heterogeneous panel data, where unobserved heterogeneity, cross-sectional dependence, and persistent shocks complicate traditional approaches. We propose the Bayesian Tapered Narrowband Least Squares (BTNBLS) estimator, which addresses three critical challenges: (1) spectral leakage in long-memory processes, mitigated via tapered periodograms; (2) precision loss in fractional parameter estimation, resolved through narrowband least squares; and (3) unobserved heterogeneity in cointegrating vectors (${\theta }_i$) and memory parameters ($\nu ,\delta$), modeled via hierarchical Bayesian priors. Monte Carlo simulations demonstrate that BTNBLS outperforms conventional estimators (OLS, NBLS, TNBLS), achieving minimal bias (0.041–0.256), near-nominal coverage probabilities (0.87–0.94), and robust control of Type 1 errors (0.01–0.07) under high cross-sectional dependence ($\rho =0.8$), while the Bayesian Chen–Hurvich test attains near-perfect power (up to 1.00) in finite samples. Applied to Purchasing Power Parity (PPP) in 18 fragile Sub-Saharan African economies, BTNBLS reveals statistically significant fractional cointegration between exchange rates and food price ratios in 15 countries ($p<0.05$), with a pooled estimate ($\widehat{\theta }=0.33$, $p<0.001$) indicating moderate but resilient long-run equilibrium adjustment. These results underscore the importance of Bayesian shrinkage and spectral tapering in panel cointegration analysis, offering policymakers a reliable tool to assess persistence of shocks in institutionally fragmented markets. Full article

Background: Heart failure (HF) ranks among the foremost causes of mortality globally, exhibiting particularly high prevalence and significant impact within intensive care units (ICUs). This study sought to develop, validate, and deploy a time-dependent machine learning model aimed at predicting the one-year all-cause mortality risk in ICU patients diagnosed with HF, thereby facilitating precise prognostic evaluation and risk stratification. Methods: This study encompassed a cohort of 8960 ICU patients with HF sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (version 3.1). This latest version of the database added data from 2020 to 2022 on the basis of version 2.2 (covering data from 2008 to 2019); therefore, data spanning 2008 to 2019 (n = 5748) were designated for the training set, while data from 2020 to 2022 (n = 3212) were reserved for the test set. The primary endpoint of interest was one-year all-cause mortality. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to select predictive features from an initial pool of 64 candidate variables (including demographic characteristics, vital signs, comorbidities and complications, therapeutic interventions, routine laboratory data, and disease severity scores). Four predictive models were developed and compared: Cox proportional hazards, random survival forest (RSF), Cox proportional hazards deep neural network (DeepSurv), and eXtreme Gradient Boosting (XGBoost). Model performance was assessed using the concordance index (C-index) and Brier score, with model interpretability addressed through SHapley Additive exPlanations (SHAP) and time-dependent Survival SHapley Additive exPlanations (SurvSHAP(t)). Results: This study revealed a one-year mortality rate of 46.1% within the population under investigation. In the training set, LASSO effectively identified 24 features in the model. In the test set, the XGBoost model exhibited superior predictive performance, as evidenced by a C-index of 0.772 and a Brier score of 0.161, outperforming the Cox model (C-index: 0.740, Brier score: 0.175), the RSF model (C-index: 0.747, Brier score: 0.178), and the DeepSur model (C-index: 0.723, Brier score: 0.183). Decision curve analysis validated the clinical utility of the XGBoost model across a broad spectrum of risk thresholds. Feature importance analysis identified the red cell distribution width-to-albumin ratio (RAR), Charlson Comorbidity Index, Simplified Acute Physiology Score II (SAPS II), Acute Physiology Score III (APS III), and the age–bilirubin–INR–creatinine (ABIC) score as the top five predictive factors. Consequently, an online risk prediction tool based on this model has been developed and is publicly accessible. Conclusions: The time-dependent XGBoost model demonstrated robust predictive capability in evaluating the one-year all-cause mortality risk in critically ill HF patients. This model offered a useful tool for early risk identification and supported timely interventions. Full article

This study developed a high-performance composite mortar with a low water-to-binder (W/B) ratio to improve the mechanical strength and volumetric stability of preplaced aggregate concrete-filled steel tubes (PACFST). Silica fume was incorporated to optimize the interfacial transition zone (ITZ) between the matrix and coarse aggregates. The effects of the sand-to-binder (S/B) ratio, water-to-binder (W/B) ratio, and expansive agent content on the flowability, compressive strength, and volume stability of the composite mortar were systematically analyzed. Experimental tests were conducted using vibration-free molded specimens, and the influence of different S/B ratios (0.8–1.4), W/B ratios (0.26–0.32), and expansive agent dosages (0–8%) on mortar properties was evaluated. The results indicate that an optimal S/B ratio of 1.2 significantly enhances flowability and strength, whereas further increases offer limited improvement. Reducing the W/B ratio enhances strength, with a decrease from 0.32 to 0.28 leading to a 23.4% increase in 28-day compressive strength. Additionally, a 6% expansive agent dosage reduces 90-day shrinkage by 13.1% while maintaining high compressive strength. The optimized PAC achieved a 28-day compressive strength of 115.9 MPa, with an 11.6% increase in 7-day strength and a 51.2% reduction in 90-day shrinkage compared to conventional C100 concrete. These findings provide theoretical guidance for designing high-strength, low-shrinkage PAC, offering insights for bridge, tunnel, and high-rise building applications. Full article

The durability of concrete is compromised by early-age cracking, which provides a pathway for harmful ions and water to penetrate the material. Early-age cracking, however, is most commonly caused by concrete shrinkage. This study investigates strategies for minimizing the shrinkage of concrete by optimizing aggregate gradation via the Tarantula Curve, reducing cement content, and incorporating lightweight fine aggregates (LWFA) as internal curing agents. The commercially adopted mix design was used as a reference, with the cementitious materials-to-aggregate (C/A) ratio reduced from 0.21 (reference) to 0.15 (proposed), incorporating 0–15% LWFA replacement levels. Workability (ASTM C143), mechanical performance (ASTM C39, ASTM C78), durability (AASHTO TP 119-21), and dimensional stability (ASTM C157) were evaluated through ASTM standard tests. The results highlight that optimizing the C/A ratio cannot only improve both compressive and flexural strengths in regular concrete but also mitigate the total shrinkage by 12.68%. The introduction of LWFA further reduced shrinkage, achieving a 19.72% shrinkage reduction compared to regular concrete. In addition, the sustainability of the developed mix designs is enhanced by the reduced cement usage. A Life Cycle Assessment (LCA) based on the TRACI method confirmed the sustainability advantages of cement reduction. The optimized mix designs resulted in a 30% decrease in CO₂ emissions, emphasizing the role of mix design in developing environmentally responsible concrete. Overall, lowering the cement amount and the addition of LWFA provide an optimal combination of shrinkage control, strength retention, and sustainability for applications. Full article

Using coal gangue in highway base construction provides a sustainable and high-value solid waste recycling approach. This research focused on the mechanical and durability properties of coal gangue from tunneling operations. Six experimental tests, such as unconfined compressive strength (UCS), flexural–tensile strength (FTS), etc., were carried out. The impact of aggregate gradation on coal gangue mixtures’ performance was systematically evaluated. XRD and SEM were used to explore the microstructural mechanisms in cement-stabilized coal gangue–gravel mixtures (CGM). An improved evaluation model, the Delphi–entropy weight–TOPSIS (DET) method, integrating Delphi and entropy weighting, was proposed. Together with an advanced radar chart, it evaluates eight performance criteria, including mechanical, durability, economic, and environmental aspects. The results show that increasing the coal gangue content in mixtures decreases UCS, dynamic compressive rebound modulus (DCRM), FTS, fatigue life, and drying shrinkage performance. Coarse aggregates relieve drying shrinkage, while fine ones improve long-term mechanical properties. Gradation T1~3 promotes the formation of C–S–H gel and AFt crystals, enhancing compactness. Based on the DET model’s quantitative evaluation, T1~3 was determined as the optimal mix for expressway bases, achieving a balance between mechanical performance, durability, and sustainability. Full article