Search Results (4,982)

This study investigates the performance and microstructure evolution of high-ferrite Portland cement (HFC) concrete under the coupled action of abrasion and freeze–thaw cycles (CAA-FTC). The 3D surface morphology of deteriorated concrete was studied; abrasion depth and volume loss evolution data were collected, while analyzing the abrasion depth fractal dimension. The characteristics of hydration products were determined using mercury intrusion porosimetry and ²⁹Si nuclear magnetic resonance method. The ITZ’s micromechanical properties and thickness were investigated via nanoindentation and SEM-EDS. The results show that under the CAA-FTC conditions, concrete deterioration is significantly exacerbated, leading to increased abrasion depth and volume loss compared to single-factor abrasion. A significant inverse relationship between the abrasion depth fractal dimension and abrasion resistance was revealed. Under CAA-FTC conditions, CG1 and CD1 exhibit increased total porosity with enlarged large pore proportions and reduced medium pores, whereas HFC1 outperforms HFC2-based concrete, showing 8.2–26.4% higher abrasion resistance and 6.5–12.0% greater nanoindentation elastic modulus in the ITZ. Regarding the deterioration factors’ influence weight, abrasion time exhibits a deterioration weight 4.8 times to 10.0 times greater than freeze–thaw cycling, making the former a dominant factor and the latter a secondary contributor. Mechanistically, freeze–thaw cycles reduce the average molecular chain length of C-S-H gel, increase harmful pores and total porosity, and degrade the ITZ’s microstructure, while abrasion causes surface-to-core physical damage and freeze–thaw cycling induces core-to-surface expansive damage. This interaction results in surface scaling, mortar spalling, and structural loosening, significantly reducing physical and mechanical properties of the concrete under study. Full article

Conventional hydrochloric acid (HCl) acidizing in carbonate reservoirs is often limited by excessively rapid acid–rock reactions and preferential flow through high-permeability paths, resulting in shallow penetration and inefficient stimulation. Viscoelastic surfactant (VES)-based diverting acids have been widely applied to address these challenges; however, the intrinsic relationship between reaction retardation and diversion efficiency, particularly under varying shear conditions, remains insufficiently clarified. In this study, a VES-based diverting acid system formulated with erucamidopropyl hydroxysultaine (EH50) was systematically investigated through multiscale experiments, including rotating disk reaction kinetics, rheological characterization, porous core flooding, and fracture-scale plate flow tests. The results reveal a pronounced shear-dependent transition in the governing mechanism of the system. Under low-shear conditions, the VES system significantly reduces the apparent acid–rock reaction rate, with a maximum reduction of 77.3%, and exhibits a synergistic retardation effect in the presence of Ca²⁺, indicating mass transfer limitation. However, under high-shear porous media flow, the intrinsic retarding effect is substantially weakened due to partial disruption of the viscoelastic structure. Despite this attenuation of chemical retardation, effective diversion performance persists under dynamic flow conditions, manifested by pressure plateau behavior, enhanced flow redistribution, more distributed wormhole networks, and greater overall dissolution. Fracture-scale experiments further demonstrate that the diversion acid suppresses excessive inlet etching and promotes spatially distributed etching patterns favorable for fracture conductivity maintenance. These findings clarify that reaction retardation and diversion are distinct yet dynamically coupled mechanisms, whose relative dominance depends on shear intensity and ionic environment. The proposed shear-responsive mechanism framework provides new insight into the design and optimization of VES diverting acid systems for carbonate reservoir stimulation. Full article

This paper investigates earth pressure and load transfer of a novel Surrounding Pile Soil Coupling–Anti-slide Chord (SPSC-AC) structure for railway slope reinforcement under dynamic train loading through physical model experiments. The study systematically analyzes the synergistic effects of the connecting beam rise-to-span ratio (f/L) and anchoring ratio (η) on the structural load redistribution mechanism and pile–soil interaction. The results show that the SPSC-AC structure forms a three-dimensional (3-D) soil arch via the curved connecting beams. The inter-row earth pressure follows a pattern of rear row > middle row > front row, while the earth pressure on corner piles exhibits a reverse increase owing to the soil arching effect. The rear pile thrust sharing ratio δ (0.58–0.68) and the pile–soil stress ratio n (1.16–1.37) are defined as two key performance parameters reflecting load distribution efficiency, and quantitative δ–f/L and δ–η relationships are established. The bending moment distribution along the pile body corresponds closely with the earth pressure pattern. Based on these results, the present study proposes optimal parameter ranges (f/L ∈ [1/4, 1/3] and η ∈ [5/11, 7/13]) along with recommendations for corner pile strengthening and differential stiffness design. These findings provide a theoretical basis for optimal anti-slide structure design. Full article

Objective: The present study aimed to verify two key hypotheses: (1) whether the session rating of perceived exertion (sRPE) can serve as a reliable indicator for workload monitoring in cricket and (2) to compare the injury risk sensitivity of sRPE-derived indicators—including the coupled and uncoupled acute:chronic workload ratio (ACWR), exponentially weighted moving average (EWMA), and robust exponential decreasing index (REDI)—across three pre-specified latency periods (no latency, 7-day latency, and 14-day latency), and to identify the optimal indicator and latency period for cricket injury risk assessment. Material: Twenty-four elite female cricket athletes from the Chinese National Women’s Cricket Team were monitored during daily training throughout the Los Angeles Olympic Games preparation period. Methods: Correlation analysis, Kendall’s tau correlation coefficients, and Bland–Altman plots were employed to assess the relationships and consistency between sRPE and various workload indicators. ROC curves were constructed to compare the performance of sRPE-derived indicators for injury risk across the three pre-specified latency conditions. Results: sRPE and its derived indicators exhibited significant correlations with health status indicators (sleep, fatigue, delayed onset muscle soreness (DOMS), stress, mood, and resting heart rate (RHR)) and physiological and biochemical indicators (testosterone, cortisol), with the majority of these correlations reaching the 0.01 significance level (p < 0.01). Kendall’s tau correlation coefficients and Bland–Altman plots revealed that sRPE and ACWR based on EWMA (hereafter referred to as EWMA) had moderate correlations with health status indicators, while ACWR based on REDI (hereafter referred to as REDI) showed a strong correlation with such indicators. sRPE and its derived indicators were strongly correlated with physiological and biochemical indicators (Kendall’s tau > 0.8) with good consistency, as the majority of scattered points fell within the limits of agreement (mean difference ± 1.96 × standard deviation, MD ± 1.96 × SD). Analysis of injury risk sensitivity indicated that the 7-day latency model yielded the highest average area under the curve (AUC = 0.85). Among all indicators, REDI and EWMA achieved the highest AUC values (AUC = 0.665 and 0.667, respectively; p < 0.001). Notably, EWMA exhibited optimal performance in the 7-day latency time series (AUC = 0.859, >0.80), followed by REDI under the 7-day latency condition (AUC = 0.857). Conclusion: EWMA with 7-day latency is a more sensitive indicator for detecting injury risk. Full article

It is of great significance to clarify the evolution law and control mechanism of fracture conductivity in different production stages for the efficient development of coalbed methane. However, research on fracture conductivity in coal–rock remains limited, and the existing models are inadequate for predicting fracture conductivity with a consideration of staged proppant crushing. To address this gap, long-term conductivity tests were conducted on deep coal–rock under varying closure pressures and proppant gradation ratios. Within a coupled computational fluid dynamics and discrete element method (CFD-DEM) framework, a particle substitution scheme was integrated with the energy-based breakage model (Tavares breakage model) to develop a fracture conductivity predictor that incorporates proppant crushing and captures the time-dependent kinetics of proppant breakage during fracture conductivity evaluation. The model’s predictions align well with the experimental data, with an average error of less than 5%. The results indicate that fracture conductivity evolution can be delineated into three stages according to particle-breakage characteristics, (i) proppant pack compaction, (ii) the primary crushing of coarse proppant grains, and (iii) the secondary crushing of proppant fines, and the contributions of these three stages to the total conductivity loss are approximately 60%, 30%, and 10%, respectively. At a low closure pressure, fracture conductivity varies markedly among proppant packs with different particle sizes; once the closure pressure exceeds 40 MPa, the proppant pack enters the fines-breakage stage, and the conductivity differences among various particle size blends become marginal. Furthermore, a semi-empirical prediction model incorporating a composite crushing factor (CCF) was developed based on the Kozeny–Carman relationship, enabling a rapid evaluation of fracture conductivity in deep coal–rock fractures. Overall, these results provide a practical basis for fracture conductivity prediction and hydraulic fracturing parameter optimization in coal–rock reservoirs. Full article

Compared with previous analytical designs for deep UBHE, the present study is new in three aspects: (1) a segmented FLS model combined with the virtual heat source method is applied to the full U-shaped path (injection, horizontal, and production wells) in a unified formulation; (2) equivalent thermal conductivity is introduced to account for groundwater seepage in porous media, avoiding the need for separate CFD or coupled numerical solvers; (3) the relationship between production well depth and the maximum effective insulation length is quantified and discussed. Deep U-shaped borehole heat-exchangers (UBHE) systems boast high heat-exchange efficiency, yet most analytical models are too simplistic, causing inaccuracies. This study proposes a segmented finite line source (FLS) model for UBHE using the virtual heat source method. Introducing equivalent thermal conductivity (k_equ), it treats rock-soil as a groundwater-saturated porous medium, coupling seepage’s dynamic heat-transfer impact. By comparing the simulation results of the same type of research within 720 h, the average temperature difference between the models was found to be 1.31 °C, with an error rate of 5.31%, which is 40.87 percentage points lower than the existing achievements, thereby demonstrating the accuracy of this model. In addition, based on this model, the influence trends of five main factors such as seepage velocity and geothermal gradient on the system’s heat exchange were drawn and analyzed. Among them, the laying length of the insulation layer was analyzed in detail. The results show that its maximum laying length should be in line with the depth node where reverse heat exchange occurs with the production well. Under the set conditions of this study, when the depth of the production well is 2500 m, the maximum laying length of the insulation layer is 1900 m. Full article

Pulmonary hypertension (PH) is a complex condition in which early diagnosis remains challenging, particularly in patients with exertional symptoms and normal or borderline resting haemodynamics. Although right heart catheterisation is the diagnostic gold standard, transthoracic echocardiography is the recommended first-line non-invasive test. However, resting echocardiography provides only a static assessment and may underestimate disease severity in early or latent pulmonary vascular disease due to preserved pulmonary vascular compliance and adaptive right ventricular responses. Because pulmonary haemodynamics are intrinsically flow-dependent, pathological abnormalities may only emerge during increased cardiac output. Stress echocardiography, performed using exercise or pharmacological stress, enables dynamic evaluation of pulmonary pressure responses, cardiac output augmentation, right ventricular contractile reserve, and ventricular interaction. Increasing evidence indicates that stress echocardiography can unmask abnormal pulmonary pressure–flow relationships, impaired pulmonary vascular reserve, and reduced right ventricular–pulmonary arterial coupling that are not apparent at rest, thereby improving functional and haemodynamic characterisation in selected patients. This Diagnostic Review outlines the physiological basis for stress echocardiographic assessment of pulmonary circulation, proposes practical recommendations for patient selection and testing protocols, and provides a framework for interpretation centered on pressure–flow relationships rather than absolute pulmonary pressure thresholds. Particular attention is given to clinical scenarios with high diagnostic yield, including unexplained exertional dyspnoea, systemic sclerosis, suspected heart failure with preserved ejection fraction, at-risk relatives of patients with pulmonary arterial hypertension, selected athletes, and paediatric populations. Stress echocardiography should not be considered a standalone diagnostic test for PH but, when performed in experienced centers and integrated within structured diagnostic pathways, it represents a valuable non-invasive adjunct to guide referral for invasive haemodynamic confirmation. Full article

Moisture content varies continuously during aerobic composting, which changes material flowability and can limit the use of a single set of discrete element method (DEM) parameters. To address this issue for a multi-component corn straw–pig manure mixture, we developed a rapid calibration workflow covering a moisture content range of 29–80%. Angle of repose (AoR) images were obtained using a cylinder-lifting test. To improve robustness for irregular pile contours, we proposed an AoR extraction method that combines LOESS smoothing with least-squares line fitting. Key DEM contact parameters affecting AoR were screened using a Plackett–Burman design, and their effective ranges were refined using a steepest-ascent test. A Box–Behnken design was then used to establish a response surface linking AoR to the significant DEM parameters. In addition, a polynomial relationship between moisture content and AoR was fitted and coupled with the AoR-parameter response surface to predict key DEM parameters directly from moisture content. Validation results showed that the predicted AoR exhibited a relative error below 10% across the tested moisture contents. An independent baffle-lifting validation test yielded a relative error below 5%. Overall, this workflow provided a practical strategy for setting DEM simulations of composting feedstocks under variable moisture content and supports numerical analysis and structural optimization of composting-related machinery. Full article

Accurately characterizing the relationship between nighttime human activity intensity and population distribution is essential for understanding urban development. This study proposes an integrated analytical framework that combines multilevel coupling quantification, regional trend detection, and interpretable machine learning to examine the Nighttime Lights and Population Coupling Coordination Degree (NPCCD) across China from 2012 to 2022. Based on this framework, NPCCD is evaluated from grid to regional level, and the characteristics of effective, persistent, and newly added coupled regions are identified. Twelve socioeconomic indicators are further constructed as explanatory variables to model NPCCD using machine learning algorithms, and Shapley Additive Explanations (SHAP) is applied to interpret the outputs. The results show that 49.07% of China’s overall NPCCD experienced steady improvement during the study period. Significant regional disparities were observed: in the eastern and central regions, more than 60% of grids fell into the improving category, whereas nearly half of the grids in the western and northeastern regions remained unchanged. Newly emerging coupling areas exhibited an average NPCCD of 0.03, markedly lower than the 0.07 observed in persistent effective areas, reflecting a mismatch between infrastructure development and population growth. Population density, human capital, industrial upgrading, and fiscal decentralization jointly explained 58.4% of the model’s variance and were identified as the major driving forces, each showing pronounced nonlinear and interaction effects. This study provides a quantitative framework for evaluating the coordination between nighttime lights and population distribution and offers insights for sustainable and balanced regional development. Full article

Virtual Reality (VR) has emerged as a powerful medium for immersive human–computer interaction, where users’ emotional and experiential states play a pivotal role in shaping engagement and perception. However, existing affective computing approaches often model emotion recognition and immersion estimation as independent problems, overlooking their intrinsic coupling and the structured relationships underlying multimodal physiological signals. In this work, we propose a modality-aware multi-task learning framework that jointly models emotion recognition and immersion estimation from a graph-structured and symmetry-aware interaction perspective. Specifically, heterogeneous physiological and behavioral modalities—including eye-tracking, electrocardiogram (ECG), and galvanic skin response (GSR)—are treated as relational components with structurally symmetric encoding and fusion mechanisms, while their cross-modality dependencies are adaptively aggregated to preserve interaction symmetry at the representation level and introduce controlled asymmetry at the task-optimization level through weighted multi-task learning, without introducing explicit graph neural network architectures. To support reproducible evaluation, the VREED dataset is further extended with quantitative immersion annotations derived from presence-related self-reports via weighted aggregation and factor analysis. Extensive experiments demonstrate that the proposed framework consistently outperforms recurrent, convolutional, and Transformer-based baselines. Compared with the strongest Transformer baseline, the proposed framework yields consistent relative performance gains of approximately 3–7% for emotion recognition metrics and reduces immersion estimation errors by nearly 9%. Beyond empirical improvements, this study provides a structured interpretation of multimodal affective modeling that highlights symmetry, coupling, and controlled symmetry breaking in multi-task learning, offering a principled foundation for adaptive VR systems, emotion-driven personalization, and dynamic user experience optimization. Full article

Background/Objectives: Lower extremity peripheral artery disease (PAD) is recognized as a significant public health issue, particularly due to its strong association with adverse cardiovascular events. Despite this, little attention has been given to its influence on left ventricular (LV) and left atrial (LA) function in patients with chronic heart failure (CHF). This study aims to examine the relationship between femoral plaque burden and structural and functional properties of the LV and LA in patients with CHF. Methods: Study design: cross-sectional observational single-center study. A total of 89 patients with CHF underwent comprehensive assessments, including duplex ultrasonography of lower extremity arteries and two-dimensional echocardiography. Analysis focused on evaluating femoral plaque burden, left ventricular deformation, and ventricular–arterial coupling. Results: Findings indicated that increased femoral plaque burden was associated with reductions in LA deformation and increases in LA stiffness. Similarly, there was evidence of impaired LV mechanics and elevated arterial loading, suggesting impaired ventricular–arterial coupling in patients with CHF and significant lower extremity atherosclerosis. Conclusions: Femoral plaque burden is closely linked to detrimental changes in LA and LV function, as well as disturbances in ventricular–arterial coupling, underscoring the importance of addressing lower extremity atherosclerosis in managing CHF patients. Full article

As generative artificial intelligence (AI) technologies become increasingly present in creative and professional domains, examining public discourse surrounding these tools is important for understanding their broader social implications. This study conducts a two-part analysis of the initial public reaction to Sora, the generative video model developed by OpenAI, by analyzing 23,543 English-language comments posted on YouTube between February and April 2024. Rather than relying on traditional positive–negative sentiment classifications, this study integrates fine-grained emotion detection with topic modeling to examine the relationship between emotions and topics in the discourse. Based on the residual analysis, the overall association between topics and emotions was weak; however, certain topics were associated with specific emotions. For instance, ethical discussions were more likely to be associated with sadness and anger, artistic settings were associated with fear, and benchmark discussions were associated with joy. Methodologically, this study utilizes an emotion–topic coupling through residual deviation with a hierarchical LDA-BERTopic approach, bringing together computational modeling and theories of emotion. This study provides a structured and theory-based way to explore the affective and thematic patterns in the public’s discourse surrounding Sora. Full article

Magnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure to predict accurately across wide operational regimes. This paper introduces a physically interpretable, artificial intelligence (AI)-powered thrust model for Applied-Field Magnetoplasmadynamic Thrusters (AF-MPDTs), developed using symbolic regression (SR) to address the gap between data-driven prediction and physics-based understanding. The proposed method, an alternative to traditional black box AI methods, incorporates physics-aware composite-term operators, ensuring that the resulting analytical expressions are bounded by known physical behaviours while retaining the flexibility to discover previously overlooked nonlinear couplings. A comprehensive dataset of AF-MPDTs undergoes rigorous preprocessing to ensure dimensional consistency and noise robustness. The SR model then evolves candidate equations, balancing predictive accuracy with interpretability through Tree-Structured Parzen Estimator (TPE) optimisation. The results, closed-form surrogate correlations with 95.98% of accuracy as goodness of fit, root mean square error of 0.0199, mean absolute error of 0.0143, and mean absolute percentage error reduction of 28.91% against the benchmark model in the literature. A post-discovery protocol for numerical robustness and physical consistency is implemented, with Shapley Additive Explanations (SHAP) providing insight into the influence of each composite-term in the developed correlation, followed by a numerical robustness and physical consistency validation using a Monte Carlo (MC) envelope. A StabilityScore is calculated for all developed correlations, enabling explicit accuracy–complexity–stability comparisons. In doing so, we demonstrated that SR can systematically recover known physical relationships—such as the scaling of thrust with discharge current and applied magnetic field—while proposing interpretable higher-order corrections that improve fit quality. The resulting SR-based thrust models not only achieve competitive accuracy relative to state-of-the-art numerical and empirical methods but also offer more explainable and interpretable results capable of revealing compact formulations that capture essential acceleration mechanisms with transparency. Overall, this paper, using SR, advances explainable AI (XAI) methodologies capable of generating trustworthy, analytically transparent models for next-generation electric propulsion systems. Full article

Manual wheelchair users with spinal cord injury (SCI) rely heavily on upper-limb function for independent mobility, which often leads to cumulative musculoskeletal loading due to repetitive propulsion. To address limitations associated with conventional unidirectional pushrim propulsion, this study presents the design and development of a detachable bidirectional wheelchair propulsion system that enables mode-dependent push and pull inputs through a mechanically reconfigurable lever mechanism. The proposed system allows conventional forward propulsion through forward pushing, while enabling alternative propulsion patterns through lever mode switching. Depending on the selected mode, either pushing or pulling inputs can be mechanically coupled to forward or backward wheel rotation, without requiring powered actuation or permanent modification of the wheelchair structure. This design expands the range of feasible propulsion strategies by allowing a selectable relationship between propulsion input direction and wheelchair movement direction through mechanical mode switching via a purely mechanical transmission architecture. The system is designed as a modular add-on compatible with standard manual wheelchairs, incorporating a clamp-based detachable interface and a gear-driven bidirectional transmission mechanism. Design considerations emphasize mechanical simplicity, controllability, and compatibility with existing wheelchair configurations, while preserving baseline pushrim functionality. This design-focused study reports the engineering rationale, mechanical architecture, and feasibility of a detachable bidirectional propulsion concept for manual wheelchairs. By explicitly documenting the system configuration and mode-switching logic, this work aims to provide a transparent design framework that can support future experimental validation and user-centered evaluation of bidirectional propulsion strategies for manual wheelchair users with SCI. Full article

Background: Both chronic kidney disease (CKD) and the haemodialysis procedure can contribute to disturbances in mineral homeostasis, which can potentially result in cellular pathologies. Our study aims to investigate trace element levels in haemodialysis patients and evaluate their potential impact on cellular adhesion molecules. This will clarify the clinical significance of trace element imbalances in this population. Methods: The study included 84 haemodialysis patients and 42 healthy controls. Trace element levels in blood (Zn, Cu, Mn, Mo, V, Sb and Cr) were measured using inductively coupled plasma mass spectrometry (ICP-MS), and cellular adhesion markers ICAM-1 and VCAM-1 were analysed by ELISA. Data analysis was conducted using SPSS 20.00, with significance set at p < 0.005. Results: Manganese (Mn) levels were significantly higher in haemodialysis patients (p = 0.019). Copper (Cu), Molybdenum (Mo), Vanadium (V), Antimony (Sb) and Chromium (Cr) levels were higher in the control group. Zinc (Zn) and Cr levels differed significantly between the control group (p = 0.018; p = 0.007). Cu levels were lower in hypertensive patients (p = 0.011), while Zn and Mn levels were higher in diabetic patients (p = 0.048 and p = 0.004, respectively). Dialysis duration, however, correlated with Sb (r = 0.295; p = 0.01), and Kt/V correlated with Mn, Sb and Cr (r = 0.256, r = 0.272 and r = 0.259, respectively; p = 0.05). Mo levels showed a positive correlation with both pre-dialysis (r = 0.230) and post-dialysis (r = 0.281) creatinine levels, and a negative correlation with post-dialysis GFR (r = −0.294). ICAM-1 and VCAM-1 levels were significantly elevated in dialysis patients (p = 0.001 for both); however, it was not found to be related to variables in the vascular access route. Conclusions: The levels of trace elements and adhesion molecules were examined in haemodialysis patients. High Mn levels indicate a risk of accumulation, while low Cu, Mo, V, Sb and Cr levels may require monitoring for deficiency. ICAM-1 and VCAM-1 levels in haemodialysis patients are associated with some trace elements (Mn and Zn); however, this relationship requires further evidence. In conclusion, the levels of trace elements and adhesion molecules in haemodialysis patients indicate the need for regular monitoring and show that the relationships between creatinine and GFR can be applied to larger patient groups. Full article