Search Results (57)

With the rapid deployment of high-speed maglev transportation systems worldwide, the operational velocity, electromagnetic complexity, and channel dynamics have far exceeded those of conventional rail systems, imposing more stringent requirements on real-time capability, reliability, and interference robustness in wireless communication. In maglev environments exceeding 600 km/h, the channel becomes predominantly line-of-sight with sparse scatterers, exhibiting strong Doppler shifts, rapidly varying spatial characteristics, and severe interference, all of which significantly degrade the stability and convergence performance of traditional beamforming algorithms. Adaptive smart antenna technology has therefore become essential in high-mobility communication and sensing systems, as it enables real-time spatial filtering, interference suppression, and beam tracking through continuous weight updates. To address the challenges of slow convergence and high steady-state error in rapidly varying maglev channels, this work proposes a new Fractional Proportionate Normalized Least Mean Square (FPNLMS) adaptive beamforming algorithm. The contributions of this study are twofold. (1) A novel FPNLMS algorithm is developed by embedding a fractional-order gradient correction into the power-normalized and proportionate gain framework of PNLMS, forming a unified LMS-type update mechanism that enhances error tracking flexibility while maintaining $\mathcal{O}\left(L\right)$ computational complexity. This integrated design enables the proposed method to achieve faster convergence, improved robustness, and reduced steady-state error in highly dynamic channel conditions. (2) A unified convergence analysis framework is established for the proposed algorithm. Mean convergence conditions and practical step-size bounds are derived, explicitly incorporating the fractional-order term and generalizing classical LMS/PNLMS convergence theory, thereby providing theoretical guarantees for stable deployment in high-speed maglev beamforming. Simulation results verify that the proposed FPNLMS algorithm achieves significantly faster convergence, lower mean square error, and superior interference suppression compared with LMS, NLMS, FLMS, and PNLMS, demonstrating its strong applicability to beamforming in highly dynamic next-generation maglev communication systems. Full article

Notch and size effects significantly influence the fatigue performance of engineering components, which is crucial for ensuring structural integrity. A novel probabilistic fatigue life prediction Kt-V-L model considering both the size and the notch effect, based on the theory of critical distance L (TCD) and the improved highly stressed volume V (HSV) method, is proposed in this study. The new definition more accurately characterizes fatigue damage and accumulation, overcoming the underestimation issues of traditional HSV methods under high-stress or low cycle fatigue (LCF) conditions. Specifically, the Weibull distribution is also proposed to characterize the material fatigue failure probability. The experimental data of 26Cr2Ni4MoV, En3B, and TC4 materials with varying notched sizes are utilized for the model validation and comparison. In addition, the predictive ability of the point method (Kt-V-L-PM) and line method (Kt-V-L-LM) under the novel proposed model was explored and evaluated. The predicted lives of 26Cr2Ni4MoV specimens fall within the ±2 scatter band of the Kt-V-L-LM, while the Kt-V-L-PM shows increasing deviation with larger notches due to its limited ability to capture stress gradients. For En3B and TC4, the predicted lives are within ± 2 life factors, verifying the model’s reliability and accuracy. Furthermore, fracture morphology analysis reveals the influence of notches on fatigue performance and elucidates the fracture failure mechanisms. Full article

The umbilical cable is an important component of the deep-sea mining system, serving as the sole connection between the surface support vessel and the seabed mining system. The harsh marine environment poses significant challenges to umbilical cable safety. Methods based on traditional time-domain simulation are time-consuming and it is hard for them to meet the needs of real-time prediction. In this paper, a novel forecasting method is proposed, PFLM-PSML, which integrates the theory of potential flow (PF), the lumped mass method (LM), and a parameterised supervised machine learning method (PSML) to forecast the safety of umbilical cables. Six environmental and system parameters—wave height, wave direction, current velocity, current direction, cable length, and the relative position between vehicle and vessel—are used as model inputs, while outputs include cable top tension, curvature, and mining vehicle overturning moments. The model employs Latin hypercube sampling and an active learning approach with hybrid kernel functions to efficiently map input–output relationships. Validation through numerical simulations and a 6000 m deep-sea trial confirms that the proposed method achieves high accuracy and a computational speed thousands of times faster than traditional approaches, enabling real-time mechanical state prediction. Parametric analyses reveal that increases in wave height, current velocity, and water depth lead to higher cable tension and vehicle overturning moments. The PFLM-PSML framework demonstrates strong potential for real-time safety assessment and control of deep-sea mining systems under complex ocean conditions. Full article

As a favorable hydrophilic additive for antifouling modification of polyvinylidene fluoride (PVDF) membrane, titanium dioxide (TiO₂) nanoparticles have been applied for years. Sodium dodecyl sulfonate (SDS), a representative anionic surfactant, has been proven to benefit the dispersion of nano-TiO₂ via an electro-spatial stabilizing mechanism. In this study, various proportionally SDS-functionalized TiO₂ nanoparticles were adopted to modify PVDF membrane. Dispersion and stability of SDS-functionalized TiO₂ nanoparticles in casting solutions were evaluated by multiple light scattering technology. The properties and antifouling performance of PVDF/SDS-TiO₂ composite membranes were assessed. The uniformity of surface pores as well as structures on cross-section morphologies was modified. The finger-like structure of PVDF/SDS-TiO₂ composite membrane was adequately developed at the SDS/TiO₂ mass ratio of 1:1. The improved antifouling performance was corroborated by the increasing free energy of cohesion and adhesion as well as the interaction energy barrier between membrane surfaces and approaching foulants assessed by classic extended Derjaguin–Landau–Verwey–Overbeek (XDLVO) theory, the low flux decline during bovine serum albumin (BSA) solution filtration process, and the high critical flux (38 L/(m²·h·kPa)) in membrane bioreactor. This study exploits a promising way to modify PVDF membrane applicable to the wastewater treatment field. Full article

Conventional academic warning systems in higher education often rely on end-of-semester grades, which severely limits opportunities for timely intervention. To address this, our interdisciplinary study developed and validated a comprehensive socio-technical framework that integrates social-cognitive theory with learning analytics. The framework combines educational data mining with culturally responsive, personalized interventions tailored to a non-Western context. A two-phase mixed-methods design was employed: first, predictive models were built using Learning Management System (LMS) data from 2,856 students across 64 courses. Second, a quasi-experimental trial (n = 48) was conducted to evaluate intervention efficacy. Historical academic performance, attendance, and assignment submission patterns were the strongest predictors, achieving a Balanced Area Under the Curve (AUC) of 0.85. The intervention, specifically adapted to Confucian educational values, yielded remarkable results: 73% of at-risk students achieved passing grades, with a large effect size for academic improvement (Cohen’s d = 0.91). These findings empirically validate a complete prediction–intervention–evaluation cycle, demonstrating how algorithmic predictions can be effectively integrated with culturally informed human support networks. This study advances socio-technical systems theory in education by bridging computer science, psychology, and educational research. It offers an actionable model for designing ethical and effective early warning systems that balance technological innovation with human-centered pedagogical values. Full article

This article shows that there is a methodological problem in the traditional IS-LM model. If production cannot be sufficiently adjusted downwards, there is no uniform interest rate that simultaneously clears the money and goods markets. An extension of the credit market in the tradition of the loanable funds theory resolves this contradiction and yields a coherent mechanism for crisis dynamics and policy transmission. In this expanded model, the interest rate is determined by the credit market whereby the total supply of credit results from household savings and the credit supply of banks and the demand for credit is due to investment demand and the demand for liquidity. This methodological approach facilitates the explanation of crisis dynamics, as involuntary inventory investment generates liquidity problems and disequilibria in the goods market lead to imbalances in the financial markets. Full article

As a core component of industrial robots, the transmission accuracy life (TAL) of rotary vector (RV) reducers constitutes a primary factor determining the high-precision operation of robotic systems. However, current life evaluation methods for RV reducers predominantly rely on conventional bearing strength life calculations, while neglecting its transmission accuracy degradation during operation. To address this limitation, a static analysis model of RV reducers is established, through which a calculation method for transmission accuracy and TAL is presented. Simultaneously, tooth surface and bearing wear models are developed based on Archard’s wear theory. Through coupled analysis of the aforementioned models, the transmission accuracy degradation law of RV reducers is revealed. The results show that during the operation of the RV reducer, the transmission error (TE) maintains relative stability over time, whereas the lost motion (LM) exhibits a continuous increase. Based on this observation, LM is defined as the evaluation metric for TAL, and a novel TAL estimation model is proposed. The feasibility of the developed TAL estimation model is ultimately validated through accelerated transmission accuracy degradation tests on RV reducers. The error between the predicted and experimental results is 11.06%. The proposed TAL estimation model refines the life evaluation methodology for RV reducers, establishing a solid foundation for real-time transmission accuracy compensation in reducer operation. Full article

Background: Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business and operational perspectives, this study focuses on the acceptance of AVs from the standpoint of e-consumers—individuals who make purchases via digital platforms—in an emerging market context. Methods: Grounded in an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is specifically suited to consumer-focused technology adoption research, this study incorporates five constructs tailored to AV adoption. Structural Equation Modeling (SEM) was applied to survey data collected from 304 e-consumers in Northeast Brazil. Results: The findings reveal that performance expectancy, hedonic motivation, and environmental awareness exert significant positive effects on acceptance and intention to use AVs for LM delivery. Social influence shows a weaker, yet still positive, impact. Importantly, price sensitivity exhibits a minimal effect, suggesting that while consumers are generally cost-conscious, perceived value may outweigh price concerns in early adoption stages. Conclusions: These results offer valuable insights for policymakers and logistics providers aiming to implement consumer-oriented, cost-effective AV solutions in LM delivery, particularly in emerging economies. The findings emphasize the need for strategies that highlight the practical, emotional, and environmental benefits of AVs to foster market acceptance. Full article

This study develops a dynamic IS-LM macroeconomic model that incorporates delayed taxation and a memory-dependent income effect, and calibrates it to quarterly data for Romania (2000–2023). Within this framework, fiscal policy lags are modelled using a “memory” income variable that weights past incomes, an approach grounded in distributed lag theory to capture how historical economic conditions influence current dynamics. The model is analysed both analytically and through numerical simulations. We derive stability conditions and employ bifurcation analysis to explore how the timing of taxation influences macroeconomic equilibrium. The findings reveal that an immediate taxation regime yields a stable adjustment toward a unique equilibrium, consistent with classical IS-LM expectations. In contrast, delayed taxation, where tax revenue depends on past income, can destabilise the system, giving rise to cycles and even chaotic fluctuations for parameter values that would be stable under immediate collection. In particular, delays act as a destabilising force, lowering the threshold of the output-adjustment speed at which oscillations emerge. These results highlight the critical importance of policy timing: prompt fiscal feedback tends to stabilise the economy, whereas lags in fiscal intervention can induce endogenous cycles. The analysis offers policy-relevant insights, suggesting that reducing fiscal response delays or counteracting them with other stabilisation tools is crucial for macroeconomic stability. Full article

The current study examines the determinants influencing preparatory year (PY) students’ intentions to adopt AI-powered natural language processing (NLP) models, such as Copilot, ChatGPT, and Gemini, and how these intentions shape their conceptions of digital sustainability. Additionally, the extended unified theory of acceptance and use of technology (UTAUT) was integrated with a diversity of educational constructs, including content availability (CA), learning engagement (LE), learning motivation (LM), learner involvement (LI), and AI satisfaction (AS). Furthermore, responses of 274 PY students from Saudi Universities were analysed using partial least squares structural equation modelling (PLS-SEM) to evaluate both the measurement and structural models. Likewise, the findings indicated CA (β = 0.25), LE (β = 0.22), LM (β = 0.20), and LI (β = 0.18) significantly predicted user intention (UI), explaining 52.2% of its variance (R² = 0.522). In turn, UI significantly predicted students’ digital sustainability conceptions (DSC) (β = 0.35, R² = 0.451). However, AI satisfaction (AS) did not exhibit a moderating effect, suggesting uniformly high satisfaction levels among students. Hence, the study concluded that AI-powered NLP models are being adopted as learning assistant technologies and are also essential catalysts in promoting sustainable digital conceptions. Similarly, this study contributes both theoretically and practically by conceptualising digital sustainability as a learner-driven construct and linking educational technology adoption to its advancement. This aligns with global frameworks such as Sustainable Development Goals (SDGs) 4 and 9. The study highlights AI’s transformative potential in higher education by examining how user intention (UI) influences digital sustainability conceptions (DSC) among preparatory year students in Saudi Arabia. Given the demographic focus of the study, further research is recommended, particularly longitudinal studies, to track changes over time across diverse genders, academic specialisations, and cultural contexts. Full article

The time-dependent deformation control of weakly cemented soft rock in deep underground engineering is a critical scientific issue that directly affects the long-term stability of roadways. Traditional Nishihsara models encounter limitations in accurately capturing the weakening effects of material parameters during rock creep failure and in describing the accelerated creep stage, making them insufficient for analyzing the creep failure mechanisms of weakly cemented surrounding rock. To address these limitations, this study integrates SEM and X-ray scanning results to reveal the microscopic degradation process during creep: under external forces, clay minerals, primarily bonded face-to-face or through cementation, gradually fracture, leading to continuous microcrack propagation and progressive parameter degradation. Based on damage theory, an enhanced Nishihara creep model is proposed, incorporating a time-dependent damage factor to characterize the attenuation of the elastic modulus and a nonlinear winding element connected in series to represent the accelerated creep stage. The corresponding three-dimensional constitutive equations are derived. Using the Levenberg–Marquardt (L-M) algorithm for parameter inversion, the model achieves over 98% fitting accuracy across the full creep stages of weakly cemented soft rock, validating its applicability to other rock types such as salt rock and anthracite. The damage creep model is numerically implemented through secondary development in FLAC3D 6.0, with simulation results showing less than 5% deviation from experimental data and the failure mode is similar. These findings provide a solid theoretical foundation for further understanding the creep behavior of weakly cemented soft rocks. Full article

Surface fluxes are vital to understanding land–atmosphere interactions, with similarity theory forming the basis for their parameterization. However, this theory has limitations, particularly due to large eddy effects, which have not been widely considered in Earth system models. A novel scheme was proposed to address this, considering large eddy effects under unstable atmospheric conditions. This study systematically evaluates the proposed scheme using the CoLM2014 model, FLUXNET2015 data, and ERA5 data. Based on the analysis of flux parameterization mechanisms, it proposes specific improvements aimed at enhancing the scheme’s performance. Our findings indicate that the proposed and classical schemes yield similar results, partly because they employ the same dimensionless wind speed gradient under near-neutral conditions. Furthermore, the results revealed that friction velocity responded more strongly to large eddies than did heat flux, as friction velocity influenced atmospheric stability and thereby mitigates the large eddy effects on heat flux. Additionally, our analysis reveals that bare soil exhibits the most pronounced changes in surface fluxes and energy partitioning, while grassland-type and forest-type sites display more complex responses. These findings indicate that different land cover types respond distinctly to the influence of large eddies. Overall, this research deepens our understanding of large eddy impacts and improves Earth system modeling by enhancing land–atmosphere interaction parameterization. Full article

Urbanization has led to a reduction in green space, weakening the region’s carbon sink capacity and stability and bringing a series of ecological problems, making the restoration and improvement of the ecological environment crucial. This study used Nanjing, China, as a case to construct an ecological network by applying Morphological Spatial Pattern Analysis (MSPA) and the Linkage Mapper (LM) tool based on circuit theory. The connectivity of ecological patches was evaluated by calculating the delta potential connectivity index (dPC). The CASA model (Carnegie–Ames–Stanford approach) was applied to quantify carbon sequestration in Nanjing. We propose an innovative carbon sink index (CSI) that integrates three indicators: capacity, efficiency, and variability. This index assesses the carbon sink function of ecological patches from both static and dynamic perspectives. Using the Future Land Use Simulation (FLUS) model, we simulated carbon sequestration changes in 2035, providing insights for risk assessment and future optimization strategies. The results reveal a significant positive correlation between node connectivity and both carbon sink capacity and efficiency, indicating that enhancing connectivity at key nodes can effectively improve its carbon sequestration. On this basis, by coupling dPC and CSI indices to classify ecological network nodes, we proposed four strategies for optimization: ecological conservation, structural connectivity, carbon sink improvement, and synergistic enhancement. Finally, by adding 26 ecological stepping stones, 32 ecological corridors, and optimizing landscape components, we achieved dual improvements in both the structural and functional aspects of the ecological network. After optimization, the network connectivity increased by 1.6% and the carbon sink increased by 3.82%, demonstrating a significant improvement. This study emphasizes that by protecting, enhancing, and restoring ecological spaces, the carbon sequestration function and stability of urban ecological networks can be effectively improved. These findings provide valuable insights for the scientific management of ecological spaces in urbanized areas. Full article

Lifestyle medicine (LM) should be incorporated as part of routine clinical work and medical education programs. Objective: To develop and test the validity and reliability of a questionnaire that measures the level of knowledge, attitude, and practice (KAP) of LM domains among medical trainees through practicing physicians. Methods: The KAP questionnaire sections covered the nine domains of LM. The validation process included face and content validity. A total of 151 individuals from the medical field residing in Saudi Arabia were recruited through a convenient sampling technique to participate in the study. Item response theory (IRT) was applied to validate the knowledge domain, while exploratory factor analysis (EFA) was used to assess attitude and practice. Cronbach’s alpha was performed to test the reliability of the three sections. Results: The questionnaire contained 37 items of knowledge, 45 attitudes, and 28 practice items. According to the IRT analysis, 27 items of knowledge were within the acceptable range of difficulty and discrimination. The EFA analysis resulted in 6 factors, including all the items in the attitude domain, and 4 factors, for a total of 27 items in the practice domain, with satisfactory factor loading (>0.4). The Cronbach’s alpha for the three domains was very high (≥0.88). Conclusions: The KAP questionnaire for LM is valid and reliable across a spectrum, from medical trainees to practicing physicians. This tool could serve as an instrument to evaluate and develop adequate educational programs for medical doctors. Full article

Evaluating irreducible water saturation is crucial for estimating reservoir capacity and developing effective extraction strategies. Traditional methods for predicting irreducible water saturation are limited by their reliance on specific logging data, which affects accuracy and applicability. This study introduces a predictive method based on fractal theory and deep learning for assessing irreducible water saturation in complex carbonate reservoirs. Utilizing the Mishrif Formation of the Halfaya oilfield as a case study, a new evaluation model was developed using the nuclear magnetic resonance (NMR) fractal permeability model and validated with surface NMR and mercury injection capillary pressure (MICP) data. The relationship between the logarithm mean of the transverse relaxation time (T_2lm) and physical properties was explored through fractal theory and the Thomeer Function. This relationship was integrated with conventional logging curves and an advanced deep learning algorithm to construct a T_2lm prediction model, offering a robust data foundation for irreducible water saturation evaluation. The results show that the new method is applicable to wells with and without specialized NMR logging data. For the Mishrif Formation, the predicted irreducible water saturation achieved a coefficient of determination of 0.943 compared to core results, with a mean absolute error of 2.37% and a mean relative error of 8.46%. Despite introducing additional errors with inverted T_2lm curves, it remains within acceptable limits. Compared to traditional methods, this approach provides enhanced predictive accuracy and broader applicability. Full article