Search Results (4,321)

The King Abdulaziz Public Transport Project in Riyadh is one of the massive undertakings that could transform mobility and quality of life in the Saudi capital. However, a full grasp of its many-sided consequences is still hard to obtain. The project is expected to deliver several positive outcomes, including decreased traffic congestion and better air quality, as well as increased mobility; however, it remains vital that the impact of this development on different aspects of urban life is studied using modern spatial analysis methods. This research seeks to address this gap by delving into the project’s influence on land use patterns, transportation behaviors, economic development, urban growth, environmental conditions, population dynamics, and road network efficiency. Using these techniques, some of the achievements and struggles of the project are identified in terms of service coverage, travel times, and how well it fits within Riyadh’s sustainability objectives to reduce car dependency and increase ridesharing. In conclusion, the study aims to contribute knowledge that supports urban planning, policy formulation, and future infrastructure projects so that the King Abdulaziz Public Transport Project is better aligned with the needs of this growing city for a workable, sustainable Riyadh. The King Abdulaziz Public Transport Project is a significant step towards improving Riyadh’s transportation system and achieving environmental, economic, and social sustainability goals. It will contribute to alleviating traffic congestion, improving air quality, boosting economic growth, and enhancing the quality of life for residents. Sustainable landuse planning around the stations, with the allocation of green spaces and public facilities, is essential. Full article

Lunar regolith simulant (LRS) geopolymers are promising construction materials for lunar in situ resource utilization, but their brittle behavior and limited crack resistance restrict their structural applications. This study investigated the effect of basalt fiber length on the mechanical properties, failure modes, stress–strain behavior, constitutive relationship, and microstructure of CQU-1 LRS geopolymers. Basalt fiber-reinforced LRS geopolymers were prepared under weak alkali activation and high-temperature curing at 80 °C. The basalt fiber content was fixed at 0.1%, and six fiber lengths of 0, 6, 9, 12, 15, and 18 mm were considered. Compressive and flexural tests were conducted after curing for 1 d and 7 d, and the normalized stress–strain curves were fitted using the Saenz L.P., Carreira D.J., and Zhenhai Guo models. The results showed that basalt fiber length significantly affected the mechanical performance of LRS geopolymers. An appropriate fiber length improved strength, stiffness, ductility, and post-peak load-bearing capacity, whereas excessively short or long fibers weakened the reinforcing effect. The 15 mm fiber group exhibited the best overall performance. After curing for 1 d, its compressive strength reached 2.23 MPa, 49.7% higher than that of the control group, and its elastic modulus increased approximately 2.5-fold. After curing for 7 d, its compressive strength reached 13.44 MPa, 32.0% higher than that of the control group. The Zhenhai Guo model provided the best fit for the stress–strain curves. SEM-EDS analysis showed that basalt fibers improved interfacial bonding and promoted gel enrichment near the fiber–matrix interface. Overall, 15 mm was recommended as the optimal basalt fiber length for CQU-1 LRS geopolymers under the conditions used in this study. Full article

Spiro-dioxy-biphenyl cyclotriphosphazene derivatives, namely mono-spiro cyclotriphosphazene (SCP) and bis-spiro cyclotriphosphazene (Bis SCP), were incorporated into poly(vinyl alcohol) (PVA)–borax hydrogels to investigate the effect of phosphazene architecture on hydrogel properties and release behavior. Hydrogels containing 5 and 10 wt% phosphazene derivatives were prepared by borax crosslinking combined with freeze–thaw gelation and characterized by SEM, FTIR, thermogravimetric analysis, swelling measurements, rheological analysis, and release kinetics. SEM analysis revealed that phosphazene incorporation modified the hydrogel morphology and increased network heterogeneity. Swelling behavior strongly depended on phosphazene structure; 5 wt% SCP/PVA exhibited the highest equilibrium swelling ratio (~990%), whereas Bis SCP-containing hydrogels showed lower swelling capacities (~290–350%). Rheological measurements confirmed gel-like behavior (G′ > G″) for all formulations, and SCP-loaded hydrogels exhibited greater mechanical reinforcement than Bis SCP-loaded systems. Thermal analysis demonstrated improved thermal stability with increasing phosphazene content. Release studies performed in PBS/DMSO (1:1, pH = 7.4) revealed diffusion-controlled transport. The Higuchi (R² = 0.994–0.995) and Korsmeyer–Peppas (R² = 0.999) models provided the best fit, while diffusion exponent values (n = 0.365–0.396) indicated a mechanism of Fickian diffusion. These outcomes demonstrate that the degree of spiro substitution effectively governs the structure–property relationships of PVA–borax hydrogels for controlled-release applications. Full article

In this work, a type II Topp-Leone-G family of distributions is parametrically transformed to create a new flexible family of continuous probability distributions called exponentiated type II Topp-Leone-G distribution through exponentiation. A variety of density shapes and hazard rate behaviors, such as increasing, decreasing, bathtub, inverted bathtub-shaped, and unimodal forms, can be captured by the proposed family, which generalizes several current lifetime models. In addition to discussing significant special cases that correspond to well-known distributions, explicit expressions for the cumulative distribution function, probability density function, survival function, hazard rate function, quantile function, actuarial measures, and linear representation of the probability density function are derived. The maximum likelihood approach is used for parameter estimation, and the simulation study provides a brief discussion of the estimator’s asymptotic characteristics. Kolmogorov–Smirnov and Cramer–Von Mises goodness-of-fit metrics and their p-values, along with information criteria like Akaike Information Criterion, Bayesian Information Criterion, Consistent Akaike Information Criterion, and Hannan–Quinn Information Criterion, are used to evaluate the appropriateness of the model. Furthermore, to visually assess model performance, graphical diagnostic techniques, such as density and distribution function overlays, quantile–quantile plots, and probability–probability plots, are used. Real-life datasets are analyzed to show the applicability of the exponentiated type II Topp-Leone-G family using Weibull distribution as the baseline, and its performance is compared with some other competing distributions. The findings demonstrate the potential utility of the proposed model in the areas of insurance and related applied domains by showing that it fits better than the competing models considered. Full article

Entomopathogenic fungi (EPF) are well established as biological control agents, but their emerging role as endophytes reveals a broader and more powerful function in crop protection. By colonizing plant tissues, endophytic entomopathogenic fungi (EEPF) create a dynamic tripartite interaction between plants, fungi, and herbivores, enabling systemic, plant-mediated pest suppression. This review synthesizes current knowledge on the behavioral and ecological responses of herbivorous arthropods to EEPF-colonized plants, with an emphasis on the mechanisms and implications for integrated pest management (IPM). Growing evidence indicates that EEPF consistently modify herbivore behavior and performance across diverse crops and insect taxa. Colonization frequently alters feeding, host selection, and oviposition, often deterring pests, although mediated responses may vary among fungal species, host plants, insect taxa, and environmental conditions. These responses are driven by EEPF-induced changes in plant chemistry, including shifts in volatile organic compounds (VOCs) and defensive metabolites. In parallel, EEPF impair insect fitness by delaying development, reducing survival, and lowering fecundity, thereby suppressing pest populations. These plant-mediated and behavioral changes extend to multitrophic interactions, potentially affecting associations with natural enemies and the transmission efficiency of some insect vectors of plant viruses. Despite rapid progress, critical gaps remain in resolving the mechanistic basis of these interactions and their stability under field conditions. Advancing the application of EEPF will require integrated approaches combining microbial ecology, chemical ecology, and insect behavioral biology. Harnessing these interactions offers a compelling pathway to reduce reliance on synthetic pesticides while enhancing the resilience and sustainability of agricultural systems. Full article

In this work, gradient-sized Pr₆O₁₁ powders were fabricated via a wet ball-milling method with variable milling durations. The microstructural evolution and temperature-dependent magnetic properties of different Pr₆O₁₁ powders were systematically investigated. The results reveal that wet ball-milling effectively refines powder particle size and introduces controllable lattice defects without altering the intrinsic crystal structure. Magnetic measurements over a temperature range of 3–300 K demonstrate that the unmilled powder exhibits typical paramagnetic behavior. However, milling-induced particle refinement significantly enhances the low-temperature magnetic moments of Pr₆O₁₁, accompanied by characteristic superparamagnetic hysteresis at 3 K. Furthermore, the fitted paramagnetic Curie temperature θp and Curie constant C confirm that the magnetic regulation is milling-affected and dependent on milling time. Prolonged milling above 1 day cannot continuously increase low-temperature magnetic moments. The above temperature-dependent magnetic properties of milled Pr₆O₁₁ can possibly be attributed to milling-induced grain refinement and lattice distortion, as supported by the microstructure analysis. This work provides valuable physical insights into the low-temperature magnetic properties of Pr₆O₁₁ and offers guidance for its magnetic functional applications. Full article

This paper presents a hybrid approach for tool-holder bearing prognosis, combining a Sparse Autoencoder with an Exponential Degradation Model. The objective is to enable early detection of wear and degradation, allowing for timely maintenance interventions and minimizing unplanned downtime. The Sparse Autoencoder is employed as an unsupervised anomaly detection tool, trained solely on defect-free data to learn normal bearing behavior and identify deviations through reconstruction errors. These errors serve as a health indicator, capturing early signs of degradation. To estimate the Remaining Useful Life, the Exponential Degradation Model is applied to the health indicator, fitting an exponential curve to predict the degradation rate and forecast the time remaining until failure. The experimental study involved two tool-holders operating under identical conditions. Tool-holder 1 was used for SAE development and for the offline calibration of the health-indicator thresholds, whereas the complete fixed configuration was subsequently applied to tool-holder 2 without SAE retraining or threshold recalibration. The results obtained on tool-holder 2 provide a preliminary within-domain evaluation of the proposed framework on an unseen component. A comparative analysis with three baseline models—Principal Component Analysis (PCA), a Naive Constant Model (NCM), and a supervised Long Short-Term Memory (LSTM) network—further contextualized the results. The Sparse Autoencoder consistently outperformed PCA and NCM in terms of accuracy and robustness, while requiring only a short defect-free baseline for training. The LSTM achieved the lowest Root Mean Square Error values overall, but its dependence on full life-cycle data and higher computational demands limit its practicality in industrial scenarios. Overall, the main contribution of this study lies in demonstrating that a simple, interpretable, and data-efficient hybrid framework can achieve reliable Remaining Useful Life prediction without relying on large supervised datasets, thus bridging the gap between deep learning performance and industrial applicability. Full article

(1) Background: Motorcyclists remain disproportionately represented in road-traffic fatalities and serious injuries worldwide, yet the behavioral factors associated with their crash involvement are still incompletely understood. (2) Methods: This study integrates several established behavioral theories—Human Information Processing (HIP), Reason’s Generic Error-Modelling System (GEMS), the Theory of Planned Behavior (TPB), and Protection Motivation Theory (PMT)—into a single mixed-theory framework in order to examine simultaneously how risk behavior and protective behavior are associated with self-reported motorcycle crash involvement. A cross-sectional survey was administered to 2910 active motorcyclists using a Modified Motorcycle Rider Behavior Questionnaire (MRBQ) to capture four dimensions of risk behavior. (3) Results: A second-order confirmatory factor analysis (CFA) confirmed that the four risk dimensions load onto a single higher-order motorcyclist risk behavior construct, and the full measurement model demonstrated good reliability, convergent validity, and discriminant validity. Structural equation modeling (SEM) showed excellent fit. Motorcyclist risk behavior was positively and significantly associated with crash involvement, whereas protective behavior was negatively associated with it; because protective equipment mainly reduces injury severity rather than preventing crashes, this inverse relationship is interpreted as an indirect association rather than a direct reduction in crash occurrence, and both hypotheses were supported. (4) Conclusions: The findings support the value of integrating error-based and motivation-based theories when modeling motorcyclist safety and highlight the need for generationally tailored interventions that simultaneously reduce risky riding and promote consistent protective behavior. Full article

As generative artificial intelligence (GenAI) increasingly permeates educational contexts, understanding the factors driving learners’ Behavioral Intention (BI) toward GenAI-powered tools has become critical. This study integrates the Technology Acceptance Model (TAM), the Task-Technology Fit (TTF) framework, and privacy and ethical risk considerations to explore the determinants of Chinese higher education students’ Behavioral Intention to adopt these tools. Data were collected from 716 students via a structured self-reported questionnaire. A multi-stage analytical approach was employed by integrating structural equation modeling (SEM) with artificial neural networks (ANN) and support vector regression (SVR). SEM was first utilized to validate the theoretical hypotheses and the measurement model. Subsequently, ANN and SVR models were constructed to explore non-linear relationships and rank the importance of core predictors for Behavioral Intention, including Perceived Ease of Use (PEU), Privacy and Ethical Concerns (PEC), Perceived Technical Features (PTF), and TTF. The modeling performance of the two algorithms was then rigorously compared. The SEM results indicate that PTF exerts an indirect impact on Behavioral Intention via the sequential mediation of Task-Technology Fit and Perceived Usefulness (PU), while PEU positively influences both Perceived Usefulness and Behavioral Intention. Notably, PEC did not exhibit a significant negative effect on users’ Attitude (ATT) or Behavioral Intention. These findings were further elucidated by the machine learning analyses, where PTF and PEU emerged as the dominant predictors, whereas the non-linear contribution of PEC was marginal. Furthermore, SVR outperformed ANN in terms of predictive accuracy and model stability. This study demonstrates the efficacy of combining theoretical modeling with machine learning techniques to elucidate the adoption mechanisms of GenAI in higher education. In addition, preliminary teaching observations in undergraduate mathematics and logistics management courses link quantitative results with actual learning scenarios. We acknowledge that future research should validate these patterns using observed behavioral data. Full article

To support vehicle crash simulation in lightweight automotive design, this study develops high-precision constitutive and fracture models for a giga casting Mg-7Al-1.5La-1Zn magnesium alloy. Through mechanical testing and microstructural analysis, the plastic hardening, strain-rate strengthening, and ductile fracture behaviors are systematically investigated. A weighted mixed hardening model combining the saturation-type Hockett–Sherby and unsaturated Swift models is established. To overcome the limitations of the classical Johnson–Cook (J-C) model in capturing strain rate–plastic strain coupling, a modified dynamic increase factor incorporating equivalent plastic strain is proposed. Comparative fitting with the Cowper–Symonds model confirms that the modified J-C model better captures the twinning deformation mechanism under high strain rates and achieves higher accuracy. Among three fracture models Johnson–Cook (J-C), Modified Mohr-Coulomb(MMC), and Damage Initiation and Evolution Model (DIEM), the MMC model, which incorporates both stress triaxiality and Lode angle, shows the best predictive performance, with an average accuracy of 86.46% for fracture displacement across the full stress range. Microstructural characterization reveals grain refinement and dispersed Al₁₁La₃ precipitates that improve grain boundary properties. The established parameter set and calibration methods provide a reliable reference for material card calibration in crash simulation of giga casting magnesium alloy body structures. Full article

Mobile applications are increasingly promoted as instruments for improving agricultural information access, advisory delivery, market participation, and decision support, particularly for smallholder farmers in developing nations. Research, however, has been dominated by general technology-acceptance and diffusion constructs, while the design-sensitive and infrastructural mechanisms studied in human–computer interaction (HCI) have received comparatively little attention. In this paper we develop a parsimonious HCI model of mobile application use in agriculture. Drawing on the technology acceptance model, the unified theory of acceptance and use of technology, diffusion of innovations, socio-technical systems theory, and human–computer interaction for development (HCI4D), the model proposes that agricultural application use is driven by five antecedent domains: perceived agronomic value, inclusive usability and accessibility, contextual and cultural fit, trust and transparency, and social and institutional embeddedness. Each plays a distinct role across three use stages: adoption intention, sustained use, and decision impact. Contextual constraints (infrastructure and farmer characteristics) moderate these relationships. We develop six testable propositions from the model. The model is conceptual: it is offered as a framework for empirical testing rather than as a validated account of farmer behavior. The paper contributes an HCI-sensitive specification of mobile application use under agricultural field conditions: a “field-ready” conception of mobile HCI. Full article

Infertility is a complex health issue that extends beyond biological mechanisms, encompassing significant psychological, relational, and social aspects. Advances in assisted reproductive technologies have improved the chances of conception, yet many couples discontinue treatment due to stress, stigma, and reduced quality of life. This narrative review explores the role of rehabilitation as a complementary approach to conventional fertility care, highlighting psychological, physical, and integrated interventions. Psychological rehabilitation strategies, such as cognitive behavioral therapy, mindfulness-based interventions, and couple-focused counseling, have been shown to reduce anxiety and depression, improve coping, and, in some cases, support better adherence to fertility treatments. Physical rehabilitation approaches, including pelvic floor muscle training, structured exercise for women with polycystic ovary syndrome, lifestyle and fitness interventions for men, yoga, and sexual rehabilitation, contribute to reproductive health, reduce pain, and enhance intimacy. Integrated models that combine psychological and physical components show the greatest promise, as they address the interaction between stress, physical function, and reproductive outcomes. Overall, the evidence suggests that infertility care should not focus solely on achieving conception. Rehabilitation offers opportunities to strengthen emotional resilience, improve sexual health, and support relationship stability, ensuring that patients benefit not only medically but also in terms of long-term well-being and quality of life. Full article

Mathematical modeling of degradation data is essential for quantifying the lifetime, reliability, and long-term stability of advanced materials when a direct experimental assessment is costly or limited. This paper develops an applied statistical framework based on the proportional hazard Gumbel Type-II (PHGT-II) distribution for modeling positive degradation times under a generalized progressive hybrid censoring scheme. The proposed model extends the baseline Gumbel Type-II distribution through a proportional hazard structure, providing additional flexibility for representing non-monotone hazard behavior, heavy-tailed lifetime patterns, and heterogeneous degradation mechanisms. The probability density, survival, hazard, and mean time to failure functions were derived, and the likelihood function was formulated under generalized progressive hybrid censoring. Parameter estimation was performed using maximum likelihood estimation and Bayesian inference with independent Gamma priors. Bayesian estimates were obtained under squared error and general entropy loss functions using a Metropolis–Hastings algorithm. The model was applied to thermal degradation data of the hydroxylated fullerene nanocomposite Sc₃N@C₈₀(OH)₁₈, where the degradation time was defined through a 2% weight-loss threshold obtained from a thermogravimetric analysis. The PHGT-II model was compared with other distributions using several goodness-of-fit measures. The results show that the PHGT-II distribution provides the best fit to the observed degradation data and yields consistent reliability estimates across maximum likelihood and Bayesian approaches. The proposed framework offers a flexible and interpretable tool for modeling censored degradation data and can be extended to other reliability and lifetime applications in engineering and material science. Full article

Accurate modeling of post-peak rock behavior is fundamental to the safe design and stability assessment of underground excavations in high-stress environments. In this context, the dilation angle is a key constitutive parameter, typically expressed as a function of plastic shear strain and confinement. The model proposed by Zhao and Cai reproduces the complex evolution of the dilation angle with notable accuracy; however, its reliance on nine interdependent fitting coefficients hinders practical calibration and numerical implementation. This study introduces a simplified model, derived from the Zhao and Cai model and guided by the conceptual framework of Zhao and Li, that reduces the number of fitting parameters to four while preserving the nonlinear dependence of the dilation angle on plastic shear strain and confinement. When calibrated against triaxial compression data, the proposed expression attained a coefficient of determination of 41.14%, markedly exceeding the 26.73% obtained with the original formulation. Although numerical simulations in FLAC^2D v. 9.7 did not fully capture the experimental response, the proposed model outperformed the Zhao and Cai model, which systematically underestimated the evolution of the volumetric strain. The results demonstrate that the proposed model provides a more tractable description of dilatancy and an improved numerical approximation of the volumetric response. Full article

Accurate long-term prediction of GNSS satellite clock errors is essential for autonomous navigation, real-time precise point positioning (PPP), and continuous positioning, navigation, and timing (PNT) services when real-time clock products are unavailable or delayed. However, conventional methods, such as quadratic polynomial fitting and Kalman filtering, have limited capability in modeling nonlinear and non-stationary clock behaviors over long prediction intervals, especially under abnormal onboard atomic clock conditions. To address this issue, an inter-satellite comparison data (ISCD)-based radial basis function neural network (RBFNN) model is proposed for long-term satellite clock error prediction. Through correlation analysis, reference satellite clocks closely related to the target satellite are selected, and both ISCD and satellite-ground comparison data are integrated to establish a nonlinear prediction model. Experiments using GPS and Galileo satellite clock datasets demonstrate that the proposed method significantly improves long-term prediction accuracy. For 180-day prediction, the proposed model reduces the RMSE by more than 60% for GPS satellites and approximately 99% for Galileo satellites with abnormal clock behavior compared with conventional methods. Rolling prediction experiments further verify the robustness and stability of the proposed model. Full article