Search Results (4,346)

External gear pumps are widely used in industrial hydraulic systems, but their volumetric efficiency can deteriorate significantly because of internal leakage, especially under high-pressure operating conditions. Conventional lumped parameter models typically assume fixed clearances and therefore cannot accurately capture the leakage behavior associated with pressure-induced deformation and wear. In this study, a dynamic clearance model for an external gear pump is developed and experimentally validated. Radial and axial clearances are measured in situ using eddy-current gap sensors over a range of operating conditions, and empirical correlation equations are identified as functions of pressure and rotational speed. These correlations are embedded into a tooth-space-volume-based lumped parameter model so that the leakage flow is updated at each time step according to the instantaneous dynamic clearances. The proposed model is validated against experimental measurements of volumetric efficiency obtained from a dedicated test bench. At 800 rev/min, the average prediction error of volumetric efficiency is reduced to 1.98% with the proposed dynamic clearance model, compared with 9.43% for a nominal static-clearance model and 3.35% for a model considering only static wear. These results demonstrate that explicitly accounting for dynamic clearance variations significantly improves the predictive accuracy of volumetric efficiency, and the proposed model can be used as a design tool for optimizing leakage paths and enhancing the energy efficiency of external gear pumps. Full article

Universal accessibility remains a critical challenge for effective public transport and urban equity. This study addresses the need for operational prioritisation tools by proposing a robust hybrid methodology to rank interventions at urban mobility nodes. The approach combines the Analytic Hierarchy Process (AHP) for integrating expert and participatory criteria weighting with four Multi-Criteria Decision-Making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, and ARAS) to ensure solution reliability. Empirical validation, conducted on 30 bus stops in Segovia, Spain, confirmed the methodological soundness, evidenced by near-perfect correlations (ρ = 0.99) among the compromise and additive ratio models (TOPSIS–VIKOR and COPRAS–ARAS) and stability across over 85% of sensitivity simulations. The findings validate that the methodology effectively guides resource allocation towards interventions yielding maximum social impact and demonstrate its transferability to complex urban supply chain contexts, such as logistics microhubs. Ultimately, this replicable and adaptable model supports the transition towards more equitable, resilient urban systems, aligning directly with Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

With the increasing requirements for “Green Mine” construction, Cemented Tailings Backfill (CTB) has emerged as the preferred strategy for solid waste management and ground pressure control in underground metal mines. However, full tailings, characterized by wide particle size distribution and high fine-grained content, exhibit complex physicochemical properties that lead to significant non-linear behavior in slurry rheology and strength evolution, posing challenges for accurate prediction using traditional empirical formulas. Addressing the issues of significant strength fluctuations and difficulties in mix proportion optimization in a specific lead–zinc mine, this study systematically conducted physicochemical characterizations, slurry sedimentation and transport performance evaluations, and mechanical strength tests. Through multi-factor coupling experiments, the synergistic effects of cement type, cement-to-tailings (c/t) ratio, slurry concentration, and curing age on backfill performance were elucidated. Quantitative results indicate that solids mass concentration is the critical factor determining transportability. Concentrations exceeding 68% effectively mitigate segregation and stratification during the filling process while maintaining optimal fluidity. Regarding mechanical properties, the c/t ratio and concentration show a significant positive correlation with Uniaxial Compressive Strength (UCS). For instance, with a 74% concentration and 1:4 c/t ratio, the 3-day strength increased by 1.4 times compared to the 68% concentration, with this increment expanding to 2.0 times by 28 days. Furthermore, a comparative analysis of four cement types revealed that 42.5# cement offers superior techno-economic indicators in terms of reducing binder consumption and enhancing early-age strength. This research not only establishes an optimized mix proportion scheme tailored to the operational requirements of the lead–zinc mine but also provides a quantitative scientific basis and theoretical framework for the material design and safe production of CTB systems incorporating high fine-grained full tailings. Full article

Optimizing heat treatment processes requires an understanding of the complex relationships between compositions, processing parameters, microstructures, and properties. Traditional experimental approaches are costly and time-consuming, whereas machine learning methods suffer from critical data scarcity. In this study, gradient boosting models were developed to predict microstructural phase fractions and mechanical properties using synthetic training data generated from an established metallurgical theory. A 400-sample dataset spanning eight AISI steel grades was created based on Koistinen–Marburger martensite kinetics, the Grossmann hardenability theory, and empirical property correlations from ASM handbooks. Following systematic hyperparameter optimization via 5-fold cross-validation, gradient boosting achieved R² = 0.955 for hardness (RMSE = 2.38 HRC), R² = 0.949 for tensile strength (RMSE = 87.6 MPa), and R² = 0.936 for yield strength, outperforming the Random Forest, Support Vector Regression, and Neural Networks by 7–13%. Feature importance analysis identified the tempering temperature (38.4%), carbon equivalent (15.4%), and carbon content (13.0%) as the dominant factors. Model predictions demonstrated physical consistency with the literature data (mean error of 1.8%) and satisfied the fundamental metallurgical relationships. This methodology provides a scalable and cost-effective approach for heat treatment optimization by reducing experimental requirements based on learning curve analysis while maintaining prediction accuracy within the measurement uncertainty. Full article

Grounded in the United Nations Sustainable Development Goal 4 (SDG4), specifically addressing the urgent need to increase relevant skills for decent work (Target 4.4) while ensuring inclusive access and quality (Targets 4.3, 4.5, 4.c), this study develops a province-level indicator system for the “talent chain” and “industry chain” and integrates entropy-weighted composite evaluation, a coupling coordination model, correlation tests, and mismatch typology classification to systematically assess the alignment between higher education talent formation and industrial demand across 31 Chinese provinces during 2000–2022. The analysis aims to characterize China’s phase-specific progress in SDG4-consistent development at the education–industry interface and to provide a theoretical and empirical basis for improving graduate job matching. The results show that (1) overall talent–industry matching improved steadily from 2000 to 2022, yet pronounced regional disparities persist, with eastern provinces generally outperforming central and western regions; (2) educational quality and structural inputs—such as faculty capacity, per-student expenditure, and the composition of human capital—are the primary drivers of talent-chain performance, whereas expansion-oriented indicators exhibit limited marginal contributions, implying that sustainable graduate job matching hinges more on quality upgrading and supply-structure optimization than on quantitative expansion alone; (3) industry-chain advancement is jointly driven by industrial scale, structural upgrading, and employment absorptive capacity, with the tertiary sector playing a particularly prominent role in shaping demand for higher-skilled labor; and (4) a divergence in driving mechanisms—quality- and structure-oriented on the education side versus scale- and structure-oriented on the industry side—combined with regional heterogeneity produces stage-specific mismatch typologies, suggesting remaining scope for structural alignment between higher education systems and industrial upgrading. Overall, strengthening regional coordination, integration, quality, and upgrading drives synergistic development, advancing SDG 4 targets by validating that quality-driven education reform is the key lever for sustainable employment in China. Full article

The article examines the implementation of 3D printing in civil protection and crisis management with a focus on the educational process, while 3D printing technology enables the creation of various teaching aids that streamline teaching and enrich theoretical knowledge. The empirical part of the study is based on a quantitative questionnaire survey among students of the Faculty of Safety Engineering of the University of Žilina in Žilina, with hypotheses set in advance and forming the basis for the construction of the questionnaire. The questionnaire collected data on the subjective evaluation of 3D printing through continuous, nominal, and ordinal responses and was completed by 277 students. Statistical methods of simple and group classification, as well as t-test, ANOVA, Kruskal–Wallis and Pearson’s correlation analysis were used to evaluate the data. Statistical significance was used to determine whether observed differences and relationships were unlikely to have arisen by chance. In addition, effect size measures were used in correlation and regression analyses to assess the strength and practical relevance of statistically significant relationships. The results of the study show that 3D printing significantly contributes to improving education and preparedness in civil protection, as it allows for more material-efficient and flexible production of educational aids compared to traditional custom production. Thus, it supports the development of more resilient communities and contributes to long-term sustainability. The findings confirmed that 3D printing is a suitable tool for improving public preparedness for emergencies. Full article

Flooding remains one of the most disruptive and costly natural hazards worldwide. Conventional approaches for estimating flood damage cost rely on empirical loss curves or historical insurance data, which often lack spatial resolution and predictive robustness. This study develops a data-driven framework for estimating flood damage costs across the contiguous United States, where comprehensive hydrologic, climatic, and socioeconomic data are available. A database of 17,407 flood events was compiled, incorporating approximately 38 parameters obtained from the National Oceanic and Atmospheric Administration (NOAA), the National Water Model (NWM), the United States Geological Survey (USGS NED), and the U.S. Census Bureau. Data preprocessing addressed missing values and outliers using the interquartile range and Walsh tests, followed by partitioning into training (70%), testing (15%), and validation (15%) subsets. Four modeling configurations were examined to improve predictive accuracy. The optimal hybrid regression–classification framework achieved correlation coefficients of 0.97 (training), 0.77 (testing), and 0.81 (validation) with minimal bias (−5.85, −107.8, and −274.5 USD, respectively). The findings demonstrate the potential of nationwide, event-based predictive approaches to enhance flood-damage cost assessment, providing a practical tool for risk evaluation and resource planning. Full article

GNSS buoys are essential for real-time elevation monitoring in coastal waterways, yet the vertical coordinate time series are frequently contaminated by complex non-stationary noise, and existing denoising methods often rely on empirical parameter settings that compromise reliability. This paper proposes GVMD-NLM, a hybrid denoising framework optimized by an improved Grey Wolf Optimizer (GWO). The method introduces an adaptive convergence factor decay function derived from the Sigmoid function to automatically determine the optimal parameters (K and $\alpha$) for Variational Mode Decomposition (VMD). Sample Entropy (SE) is then employed to identify low-frequency effective signals, while the remaining high-frequency noise components are processed via Non-Local Means (NLM) filtering to recover residual information while suppressing stochastic disturbances. Experimental results from two datasets at the Dongguan Waterway Wharf demonstrate that GVMD-NLM consistently outperforms SSA, CEEMDAN, VMD, and GWO-VMD. In Dataset One, GVMD-NLM reduced the RMSE by 26.04% (vs. SSA), 17.87% (vs. CEEMDAN), 24.28% (vs. VMD), and 13.47% (vs. GWO-VMD), with corresponding SNR improvements of 11.13%, 7.00%, 10.18%, and 5.05%. In Dataset Two, the method achieved RMSE reductions of 28.87% (vs. SSA), 17.12% (vs. CEEMDAN), 18.45% (vs. VMD), and 10.26% (vs. GWO-VMD), with SNR improvements of 10.48%, 5.52%, 6.02%, and 3.11%, respectively. The denoised signal maintains high fidelity, with correlation coefficients (R) reaching 0.9798. This approach provides an objective and automated solution for GNSS data denoising, offering a more accurate data foundation for waterway hydrodynamics research and water level monitoring. Full article

With the advancement of digital technology, serious games have become an essential tool for disseminating and educating individuals about cultural heritage. However, systematic empirical research remains limited with respect to how visual elements influence users’ cognitive and emotional engagement through interactive behaviors. Using the “Macau Historic Centre Science Popularization System” as a case study, this mixed-methods study investigates the mechanisms by which visual elements affect user experience and learning outcomes in digital interactive environments. Eye-tracking data, behavioral logs, questionnaires, and semi-structured interviews from 30 participants were collected to examine the impact of visual elements on cognitive resource allocation and emotional engagement. The results indicate that the game intervention significantly enhanced participants’ retention and comprehension of cultural knowledge. Eye-tracking data showed that props, text boxes, historic buildings, and the architectural light and shadow shows (as incentive feedback elements) had the highest total fixation duration (TFD) and fixation count (FC). Active-interaction visual elements showed a stronger association with emotional arousal and were more likely to elicit high-arousal experiences than passive-interaction elements. The FC of architectural light and shadow shows a positive correlation with positive emotions, immersion, and a sense of accomplishment. Interview findings revealed users’ subjective experiences regarding visual design and narrative immersion. This study proposes an integrated analytical framework linking “visual elements–interaction behaviors–cognition–emotion.” By combining eye-tracking and information dynamics analysis, it enables multidimensional measurement of users’ cognitive processes and emotional responses, providing empirical evidence to inform visual design, interaction mechanisms, and incentive strategies in serious games for cultural heritage. Full article

The integration of Artificial Intelligence (AI) in higher education offers a potential solution to the scalability of personalized learning, yet empirical frameworks connecting diagnostic data with teacher-mediated interventions remain limited in developing contexts. This study adopts a sequential multi-phase research design to address this gap. Phase 1 comprised a diagnostic quantitative analysis of the National Survey on Access and Permanence in Education (ENAPE 2021), involving a representative sample of 3422 Mexican undergraduate students. Using Exploratory Factor Analysis (KMO = 0.96) and Pearson correlations, the study established a structural baseline. Phase 2 implemented a quasi-experimental exploratory pilot (N = 23) across two academic clusters (Civil Engineering and Nutrition) using “ActivAI”, a custom GPT configured with Retrieval-Augmented Generation (RAG). Results from Phase 1 revealed a strong, statistically significant correlation ($r=0.72$, $p<0.01$) between the perceived impact of education on daily life and the perception of equity, identifying “relevance” as a key driver of accessibility. Phase 2 results demonstrated high student satisfaction with AI-driven personalization (M = 4.49, SD = 0.64), although disciplinary variations in engagement were observed (SD = 0.85 in Nutrition versus 0.45 in Engineering). The study concludes by proposing the Dynamic Integration Model, which leverages AI not as a replacement for instruction but as a scalability toolkit for teacher-led orchestration, ensuring that personalization addresses dynamic student needs rather than static learning styles. Full article

We propose a unified theoretical framework grounded in a Primordial Quantum Field (PQF)—a continuous, non-local informational substrate that precedes space-time and matter. The PQF is represented by a wave functional evolving in an abstract configuration space, where physical properties emerge through the self-organization of complexity. We introduce a novel physical quantity—complexity entropy Sc[ϕ]—which quantifies the structural organization of the PQF. Unlike traditional entropy measures (Shannon, von Neumann, Kolmogorov), Sc[ϕ] captures non-trivial coherence and functional correlations. We demonstrate how complexity gradients induce an emergent geometry, from which spacetime curvature, physical constants, and the arrow of time arise. The model predicts measurable phenomena such as entanglement waves and reinterprets dark energy as informational coherence pressure, suggesting empirical pathways for testing via highly correlated quantum systems. Full article

Digital technologies have transformed the spatial organization of finance. As a result, geographic and virtual agglomerations co-exist. In this paper, we model the synergistic integration of geographic and virtual agglomerations within China’s financial industry from a systems perspective. Using provincial panel data from 2011 to 2023, we develop an entropy-weighted coupling coordination model to measure the interaction between the two agglomerations. Furthermore, we employ spatial and convergence analyses to reveal their evolutionary characteristics. Our findings reveal three key results. First, financial geographic agglomeration shows an overall increasing trend, with regional levels ranked as follows: eastern region, northeastern region, western region, and central region. It exhibits significant positive spatial correlation and convergence characteristics. Second, financial virtual agglomeration also continues to strengthen, with regional levels ranked as eastern, central, western, and northeastern regions. Its convergence patterns display regional heterogeneity, and no significant spatial correlation is observed. Third, the coupling coordination degree between the two agglomerations has steadily improved nationwide and across all four major regions with convergent trends. By 2023, the eastern region has entered a stage of primary coordination, while the central, western, and northeastern regions remain in a near-dysfunctional state. In terms of driving patterns, most provinces are primarily driven by geographic agglomeration. Hunan, Hainan, and Guizhou are driven by virtual agglomeration, whereas Beijing, Anhui, Shandong, Guangdong, and Yunnan demonstrate a synchronized pattern driven by both agglomeration types. Overall, our findings highlight the systemic nature of financial agglomeration in the digital economy and enrich the theoretical understanding of financial dual-agglomeration synergy. They provide an analytical framework and empirical evidence for designing differentiated regional financial development policies. Full article

Pan-Homophonic events denote fluctuations in stock prices that are triggered by phonetic similarities between event keywords and stock tickers. As a relatively novel and under-researched phenomenon, they mirror a subtle yet influential behavioral deviation within financial markets. Centering on the case of Chuandazhisheng, this study delves into how such events produce dynamic and time-varying impacts on stock prices. A linguistic amplitude segmentation method is devised to discriminate between high- and low-intensity events based on information entropy. To separate pan-homophonic-driven price movements from broader market trends, the Relational Stock Ranking (RSR) model is integrated with a Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) framework to establish an adjusted price benchmark. The empirical analysis reveals a sequential price response: initial moderate fluctuations in the low-amplitude phase often yield to more prominent volatility in the high-amplitude phase. While price surges typically occur within one or two days of the event, they generally revert within approximately three weeks. Moreover, repeated exposures to homo- phonic stimuli seem to attenuate the response, indicating a decaying spillover pattern. These findings contribute to a more profound understanding of the intersection between linguistic cues and market behavior and provide practical insights for investor education, information filtering, and regulatory supervision. Full article

This study investigated twin-propeller-induced scour on sandy seabeds with varying grain sizes (d₅₀ = 0.11, 0.5, and 0.95 mm) through a series of laboratory experiments. The effects of propeller rotation speed (rpm), offset height (y₀), propeller diameter (D_p), and sediment grain size (d₅₀) on scour development were examined. Results indicated that sediment grain size significantly influences scour patterns. A key objective was to develop predictive expressions for primary scour characteristics at equilibrium: maximum scour depth (S_max), scour hole length (L_max), and maximum scour width (B_max). Using a nonlinear regression approach, the proposed expressions demonstrated strong predictive performance. Findings show that equilibrium scour depth increases with higher Froude numbers (F₀) but decreases with larger sediment size (d₅₀) and higher propeller offset (y₀). Additionally, empirical equations were formulated to predict the temporal evolution of scour depth, achieving high correlations with experimental data (R² > 0.97). These results enhance understanding of scour induced by unconfined twin-propeller jets in harbors or navigation channels and provide valuable data for the design and protection of harbor basins. Full article

Prostate cancer (PCa) is a common malignancy among men worldwide. After radical prostatectomy (RP) and radical radiotherapy (RT), patients may experience biochemical recurrence (BCR) of prostate cancer, indicating disease progression. Therefore, it is meaningful to predict and accurately assess the risk of BCR, and a machine-learning-based-model for BCR prediction in PCa based on fatty-acid metabolism and cancer-cell stemness was developed. A stemness prediction model and ssGSEA (single-sample gene set enrichment analysis) empirical cumulative distribution function algorithm were used to score the stemness scoring (mRNAsi) and fatty-acid metabolism of prostate-cancer samples, respectively, and further analysis showed that the two scores of the samples were positively correlated. Based on WGCNA (weighted correlation network analysis), we discovered modules significantly associated with both stemness and fatty-acid metabolism and obtained the genes within them. Then, based on this gene set, 101 algorithm combinations of 10 machine-learning methods were used for training and prediction BCR of PCa, and the model with the best prediction effect was named fat_stemness_BCR. Compared with 23 published PCa BCR models, the fat_stemness_BCR model performs better in TCGA and CPGEA data. To facilitate the use of the model, the trained model was encapsulated into an R package and an online service tool (PCaMLmodel, Version 1.0) was built. The newly developed fat_stemness_SCR model enriches the prognostic research of biochemical recurrence in PCa and provides a new reference for the study of other diseases. Full article