Search Results (18,060)

As a zero-carbon fuel, ammonia holds significant potential for achieving the “dual carbon” strategic goals. However, its extremely low laminar burning velocity (LBV) limits its direct application in combustion systems. This work systematically reviews the research progress on the LBV of ammonia flames, focusing on three key aspects: measurement methods, effects of combustion conditions, and reaction kinetic models. In terms of measurement methods, the principles, applicability, and limitations of the spherical outwardly propagating flame method, Bunsen-burner method, counter-flow flame method, and heat flux method are discussed in detail. It is pointed out that the heat flux method and counter-flow flame method are more suitable for the accurate measurement of ammonia flame LBV due to their low stretch rate and high stability. Regarding the effects of combustion conditions, the LBV characteristics of pure ammonia flames under ambient temperature and pressure are summarized. The influence patterns of three factors on LBV are analyzed systematically: blending high-reactivity fuels (e.g., hydrogen and methane), oxygen-enriched conditions, and variations in temperature and pressure. This analysis reveals effective approaches to improve ammonia combustion performance. Furthermore, the promoting effect of high-reactivity fuel blending on liquid ammonia combustion was also summarized. For reaction kinetic models, various chemical reaction mechanisms applicable to pure ammonia and ammonia-blended fuels (ammonia/hydrogen, ammonia/methane, etc.) are sorted out. The performance and discrepancies of each model in predicting LBV are evaluated. It is noted that current models still have significant uncertainties under specific conditions, such as high pressure and moderate blending ratios. This review aims to provide theoretical references and data support for the fundamental research and engineering application of ammonia combustion, promoting the development and application of ammonia as a clean fuel. Full article

The present study examines the influence of material and structural parameters on the fit and air permeability of textile face masks, surgical masks, and certified respirators. Nine samples were tested using the AccuFIT 9000 for quantitative fit factor (FF) measurements and the FX-3340 MinAir for air permeability in both airflow directions. Results show that increased thickness moderately improves FF, supporting better facial sealing. However, mass per unit area and bulk density show weak or no correlation with FF. Air permeability correlates weakly and negatively with FF, especially during exhalation, but remains essential for wearer comfort. Notably, some textile masks outperformed certified respirators in terms of fit, highlighting the importance of design, elasticity, and edge sealing. The findings suggest that effective mask performance depends on more than filtration materials or certification levels. A balanced design combining breathability, structural optimisation, and ergonomic fit is essential for both comfort and protection. These insights can guide the development of more effective reusable and disposable face coverings, particularly in aerosol-rich environments. Full article

Wheat (Triticum aestivum L.) is a key crop in Spain, especially in Castilla and León Region. However, there are few studies evaluating predictive models based on spectral indices and multivariate analysis to estimate yield in direct seeding (DS) and conventional seeding (CS) systems. This study addresses this need by implementing a split-plot experimental design in the city of Palencia, Spain, analyzing crop physiological data and nine spectral indices derived from multispectral aerial images captured by drones. The analysis included multivariate techniques such as Principal Component Analysis (PCA) and Random Forest (RF), supplemented with statistical tests, ROC curves, and prediction analysis. The results showed that the RF model successfully classified treatments with 93.75% accuracy and a Kappa index of 0.875, highlighting performance, nitrogen, and protein as key variables. Among the vegetation indices, the Soil-Adjusted Vegetation Index (SAVI) and the Advanced Vegetation Index (AVI) were the most relevant in the flowering stage, with ROC curve values of 0.7778 and 0.8025, respectively. Spearman’s correlations confirmed a significant relationship between these indices and key physiological variables, allowing to distinguish between DS and CS systems. The RF-based prediction model for performance showed R² values above 91% in the indices with the highest correlation. However, predictive capacity was higher in DS, suggesting that conditions inherent in non-mechanized handling significantly influence model performance. This highlights the importance of using non-destructive procedures to estimate production, enabling the development of adaptive and sustainable strategies that contribute to efficient agricultural production, since it is possible to anticipate crop yields before harvest, optimizing resources such as fertilizers and water. Full article

This work presents an experimental study of the electromagnetic behavior of water and its interaction with gasoline in the frequency range of 1.9 to 2.6 GHz, corresponding to the UHF band. This interval lies within the dielectric relaxation region of water, where significant absorption and reflection phenomena occur. The results show qualitative differences in the electromagnetic responses of water, gasoline, and their mixtures, particularly in the stability of amplitudes and phase variability. The mixtures exhibit an intermediate behavior between the pure liquids, highlighting the direct influence of the dielectric properties of the medium on the reflected signal. Furthermore, it was identified that the band between 2400 and 2550 MHz presents a more predictable amplitude response, making it a promising frequency range for the non-invasive detection of gasoline as a contaminant in aquatic environments. Full article

The mechanical characteristics of soil–rock mixtures (SRMs) are significantly influenced by its material composition, composition ratio, water content, and a variety of other influencing factors. A total of 120 SRMs samples with different stone contents and water contents, sourced from typical cohesive soil and crushed stone in the Wuyishan region of Fujian Province, were prepared and subjected to large-scale direct shear tests. This research investigated how stone content, water content, and normal stress impact the shear stress–shear displacement behavior of the SRMs, as well as their influence on internal friction angle, cohesion, and shear strength. The results indicated that the shear stress–shear displacement curves of the SRMs exhibit similar patterns across different conditions, with shear stress increasing with shear displacement. The increase rate correlated closely with water and rock content, as well as normal stress. The shear strength, which adheres to the Mohr–Coulomb criterion, increased with rising stone content. It initially increased and then decreased as water content rose and reached a peak at the optimal water content. Higher stone content reduced shear strength sensitivity to water content changes. The internal friction angle diminished in response to elevated water content, while conversely, it experienced an augmentation with a heightened concentration of stone. It was less affected by water content at higher stone contents. The cohesion decreased as rock content increased, and it initially increased before decreasing with rising water content. Furthermore, as stone content grew, the effect of water content on cohesion became less pronounced. Full article

Accurate prediction of blast-induced air overpressure (AOp) is vital for environmental management and safety in mining and construction. Traditional empirical models are simple but fail to capture complex meteorological effects, while accurate black-box machine learning models lack interpretability, creating a significant dilemma for practical engineering. This study resolves this by applying explainable AI (XAI) to develop a transparent, “white-box” model that explicitly quantifies how meteorological parameters, wind speed, direction, and air temperature influence AOp. Using a dataset from an urban excavation site, the methodology involved comparing a standard USBM empirical model and a Multivariate Non-linear Regression (MNLR) model against a Symbolic Regression (SR) model implemented with the PySR tool. The SR model demonstrated superior performance on an independent test set, achieving an R² of 0.771, outperforming both the USBM (R² = 0.665) and MNLR (R² = 0.698) models, with accuracy rivaling a previous “black-box” neural network. The key innovation is SR’s ability to autonomously generate an explicit, interpretable equation, revealing complex, non-linear relationships between AOp and meteorological factors. This provides a significant engineering contribution: a trustworthy, transparent tool that enables engineers to perform reliable, meteorologically informed risk assessments for safer blasting operations in sensitive environments like urban areas. Full article

Background: This study developed a Causal Graphical Model (CGM) to analyze Bacterial Vaginosis (BV), a condition caused by an imbalance in the vaginal microbiota, whose bacterial composition varies among women. While previous studies used variable selection, clustering, and association rules to identify BV-associated bacteria, these approaches lack visual tools to explore causal relationships and determine which are the most relevant. In contrast, the CGM generated in this study allows visualization of associated bacteria and their causal links, thereby identifying those most influential. Methods: Path Analysis (PA), a statistical structural equation modeling method, was used to construct the CGM, with emphasis on observable variables and to assess direct and indirect effects through correlations and covariances. PA was applied to an already-collected third-party dataset related to BV diagnosis, consisting of data from 132 pregnant women between 4 and 24 weeks of gestation. Results: The CGM, built using a theoretical model based on the Spearman correlation matrix, was validated through statistical metrics and by a clinical-biological expert. The resultant model highlights bacteria influencing BV diagnosis, specifically Mycoplasma hominis (Mh), Atopobium vaginae (Av), Gardnerella vaginalis (Gv), Megasphaera Type 1 (MT1), and Bacteria Associated with Bacterial Vaginosis Type 2 (BVAB2). Among them, MT1 and BVAB2 showed the strongest association with BV. Conclusions: The CGM effectively identifies causal associations among bacteria related to BV. Full article

This paper investigates the dynamics of quantum and classical geometric correlations in the hyperfine structure of the hydrogen atom under pure dephasing noise, focusing on the interplay between entangled initial states and environmental effects. We employ the Lindblad master equation to model dephasing, deriving differential equations for the density matrix elements to capture the evolution of the system. The study explores various entangled initial states, characterized by parameters $a_1$, $a_2$, and $a_3$, and their impact on correlation dynamics under different dephasing rates $\Gamma$. A trace distance approach is utilized to quantify classical and quantum geometric correlations, offering comparative insights into their behavior. Numerical analysis reveals a transition point where classical and quantum correlations equalize, followed by distinct decay and stabilization phases, influenced by initial coherence along the z-axis. Our results reveal a universal sudden transition from classical to quantum decoherence, consistent with observations in other open quantum systems. They highlight how initial state preparation and dephasing strength critically influence the stability of quantum and classical correlations, with direct implications for quantum metrology and the development of noise-resilient quantum technologies. By focusing on the hyperfine structure of hydrogen, this study addresses a timely and relevant problem, bridging fundamental quantum theory with experimentally accessible atomic systems and emerging quantum applications. Full article

Chronic pain conditions are debilitating and have an enormous impact on quality of life, yet underlying biological mechanisms remain poorly understood, hindering the development of diagnostic tools and effective treatments. Emerging evidence suggests a role for immune dysfunction in chronic pain. Among the various forms of immune dysfunction, T cell exhaustion and senescence, well-characterized in cancer and chronic infections, remain largely unexplored in chronic pain research. At the same time, lifestyle factors such as sleep, stress, physical activity, and diet are increasingly recognized as modulators of both pain and immune function. This review explores the potential interplay between these behavioural factors, immune exhaustion/senescence, and chronic pain. Critical gaps in current knowledge are identified, and future directions are outlined to clarify immune dysfunction and the influence of lifestyle factors in chronic pain conditions. Full article

Globally, the rise in car ownership and usage has intensified parking challenges, particularly within central business districts (CBDs) of many developed cities. Scarce parking infrastructure and escalating land values have further exacerbated these issues, leading to heightened competition among business owners, residents, shoppers, and clients for the limited available paid and free on-street parking spaces. Against this backdrop, the present study sought to model the determinants of motorists’ parking challenges using a recursive structural equation model (RSEM), drawing on empirical evidence from Greater Kumasi, Ghana. Primary data were collected through a structured survey involving 1000 drivers within the designated catchment area, employing cluster and systematic sampling techniques to ensure representativeness. The findings reveal that four out of five structural paths of the constructs exerted significant influences on the structural model components. Both time-related indices and parking costs demonstrated direct and indirect effects on parking challenges, with vehicle type serving as a mediating variable. Furthermore, most of the measurement models significantly impacted the latent factors, either positively or negatively, highlighting the complex interrelationships between parking behavior and underlying determinants. Overall, this study makes several contributions: it provides localized empirical evidence from a developing-country context, offers theoretical refinements to existing models, demonstrates methodological rigor through the application of RSEM, and proposes practical policy insights to address urban parking challenges in rapidly growing African cities such as Kumasi. Full article

The Ohon Continuous Casting is the main method for preparing single crystal copper wire, and it is also the research hotspot at present, but it is difficult to directly cast ultrafine single crystal copper wire (diameter < 0.05 mm). The copper wire obtained by continuous casting must be drawn and deformed before it can be used in practice, but this will bring a series of problems such as single crystal structure destruction and conductivity deterioration. Directional heat treatment technology can control the direction of heat flow at a low temperature, realize the directional migration of grain boundaries in the recrystallization process, and form columnar crystals or single crystals, which is of great significance for improving electrical conductivity. In this paper, the directional heat treatment method was used to investigate the microstructure evolution and influencing factors of pure copper wire, the process parameters were optimized, and the conductivity of pure copper wire was measured. It was found that the conductivity of pure copper wire increased by 5% when the heating temperature was 750 °C and the withdrawing velocity was 15 μm/s, which laid a foundation for the improvement of conductivity of pure copper wire. Full article

Sensing represents one of the most rapidly developing areas of modern life sciences, spreading from the detection of pathogenic microorganisms in living systems, food, and beverages to hazardous substances in liquid and gaseous environments. However, the development of efficient and low-cost multimodal sensors with easy-to-read functionality is still very challenging. In this paper, stable aqueous colloidal suspensions (ζ-potential was between −30 and −40 mV) of ultrasmall (~7 nm) plasmonic Si-Au and SiC-Au nanocomposites were formed. Two variants of pulsed laser ablation in liquids (PLAL)—direct ablation and laser co-fragmentation—were used for this purpose. The co-fragmentation approach led to a considerable decrease in hydrodynamic diameter (~78 nm) and bandgap widening to approximately 1.6 eV. All plasmonic nanocomposites exhibited efficient multi-band blue emission peaking at ~430 nm upon Xe lamp excitation. Co-fragmentation route considerably (~1 order of magnitude) increased the PL efficiency of the nanocomposites in comparison with the laser-ablated ones, accompanied by a negligible amount of dangling bonds. These silicon-based nanostructures significantly affected the optical response of rhodamine 6G, depending on the synthesis route. In particular, directly ablated nanoparticles revealed a stronger influence on the optical response of dye molecules. The observed findings suggest using such types of semiconductor-plasmonic nanocomposites for multimodal plasmonic and colorimetric sensing integrated with luminescent detection capability. Full article

The escalating exposure of young children to digital gaming necessitates a critical examination of its behavioral impacts. However, evidence regarding its influence on aggressive behavior remains limited. This study investigated the relationship between digital play addiction tendency and our dimensions of aggression: physical aggression, relational aggression, self-directed aggression, and aggression against objects. This study employed a cross-sectional design, gathering data through parent assessments. The sample consisted of 744 children aged 4 to 6 years. The average age of the participants was 33.5, with 82% of the sample being female. The participants came from a lower (27%), middle (37%), and high (36%) socioeconomic background. The data were analyzed using a Structural Equation Modeling (SEM) approach to test the hypothesized relationships. The main findings from the SEM analysis indicated that a higher digital play addiction tendency was a significant positive predictor of all four dimensions of aggression. These results highlight the potential adverse effects of digital play addiction tendency on the development of maladaptive behaviors in early childhood. This study underscores the urgent need to develop strategies that foster healthier digital media consumption and mitigate the adverse effects of digital gaming on children’s developmental outcomes. Full article

In the era of digital economy, computing power centers, serving as core infrastructure that aggregates computing resources and supports digital transformation, have seen their competitiveness formation mechanism and evaluation methods become important research directions in the field of economics and management. Breaking away from fragmented analyses, this study, based on a complex systems perspective, dissects the formation mechanism of computing power center competitiveness and extracts key influencing factors. Utilizing the entropy weight-TOPSIS-gray correlation method, a fully quantifiable evaluation system for computing power center competitiveness is developed, effectively enhancing the practicality, reusability, and comparability of the evaluation approach. Through an empirical analysis of 35 computing power centers in China, the research found that computing power is the primary influencing factor of competitiveness and pointed out that due to different resource advantages, there are also significant differences in the competitiveness level and development path of computing power centers. Based on these findings, and centered on the dual-wheel drive of technology and cost, four development pathways for computing power centers are proposed: strengthening technological advantages, optimizing cost structures, implementing targeted government policies, and fostering industrial ecosystem synergy. This provides a methodological framework and policy toolkit for enhancing the competitiveness and achieving sustainable development of computing power centers in various countries and regions. Full article

The field of hard milling has recently witnessed growing interest in environmentally sustainable machining practices. Among these, Minimum Quantity Lubrication (MQL) has emerged as an effective strategy, offering not only reduced environmental impact but also economic benefits and enhanced cooling performance compared to conventional flood cooling methods. In hard milling operations, cutting temperature is a critical factor that significantly influences the quality of the finished component. Proper control of this parameter is essential for producing high-precision workpieces, yet measuring cutting temperature is often complex, time-consuming, and costly. These challenges can be effectively addressed by predicting cutting temperature using advanced Machine Learning (ML) models, which offer a faster and more efficient alternative to direct measurement. In this context, the present study investigates and compares the performance of Conventional Minimum Quantity Lubrication (CMQL) and Graphene-Enhanced MQL (GEMQL), with sesame oil serving as the base fluid, in terms of their effect on cutting temperature. The experiments are structured using a Taguchi L36 orthogonal array, with key variables including cutting speed, feed rate, MQL jet pressure, and the type of cooling applied. Additionally, the study explores the predictive capabilities of various advanced ML models, including Decision Tree, XGBoost Regressor, K-Nearest Neighbor, Random Forest Regressor, and CatBoost Regressor, along with a Hybrid Stacking Machine Learning Model (HSMLM) for estimating cutting temperature. The results demonstrate that the GEMQL setup reduced cutting temperature by 36.8% compared to the CMQL environment. Among all the ML models tested, HSMLM exhibited superior predictive performance, achieving the best evaluation metrics with a mean absolute error of 3.15, root mean squared error (RMSE) of 5.3, mean absolute percentage error of 3.9, coefficient of determination (R²) of 0.91, and an overall accuracy of 96%. Full article