Search Results (3,114)

This paper reports a study conducting an in-depth analysis of the impacts of ethical training on the adoption of AI tools among computer science students in higher vocational colleges. These students will serve as the pivotal human factor for advancing the field of AI. Aiming to explore practical models for integrating AI ethics into computer science education, the research seeks to promote more responsible and effective AI application and therefore become a positive influence in the field. Employing a mixed-methods approach, the study included 105 students aged 20–24 from a vocational college in Guangdong Province, a developed region in China. Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, a five-point Likert scale was used to evaluate the participants’ perceptions of AI tool usage based on ethical principles. The Structural Equation Modeling (SEM) results indicate that while participants are motivated to adopt AI technologies in certain aspects, performance expectancy negatively impacts their intention and actual usage. After systematically studying and understanding AI ethics, participants attribute a high proportion of responsibility (84.89%) to objective factors and prioritized safety (27.11%) among eight ethical principles. Statistical analysis shows that habit (β = 0.478, p < 0.001) and hedonic motivation (β = 0.239, p = 0.004) significantly influence behavioral intention. Additionally, social influence (β = 0.234, p = 0.008) affects use behavior. Findings regarding factors that influence AI usage can inform a strategic framework for the integration of ethical instruction in AI applications. These findings have significant implications for curriculum design, policy formulation, and the establishment of ethical guidelines for AI deployment in higher educational contexts. Full article

Hospitals are complex systems that function most effectively when operations are coordinated and supported by real-time information and feedback loops. Sustained growth, quality improvement, and financial viability increasingly rely on data-based management (DBM), yet adoption and use vary widely across healthcare institutions. This study examined the enabling and hindering factors influencing DBM, with the aim of generating insights to strengthen data use and improve management of eye hospitals. A qualitative multiple case study design was employed in six purposefully selected eye hospitals in India, varying in size and baseline capacity for DBM. At each site, five to six key personnel were interviewed. Data collection involved audio-recorded interviews, transcripts, and field notes, and analysis followed a grounded theory approach using open and axial coding to identify themes, relationships, and develop a conceptual framework. Findings reaffirmed the core enablers—leadership commitment, data availability, and technology adoption. Additional drivers included operational adaptability, regulatory demands, systematic improvement practices, daily reporting, information policies, and the use of communication platforms such as WhatsApp. Key barriers were incomplete data entry, software limitations, inadequate analytical reporting, and inconsistent adherence to processes. Overall, effective DBM requires both foundational enablers and contextual drivers, while addressing barriers to institutionalizing data use and improving outcomes. Full article

Finding high-performance and low-cost materials is essential for high-quality photocatalytic water purification to expand the spectral response and improve light utilization. In this paper, we used relatively inexpensive materials such as Ag and TiO₂. The influence of particle spacing, core radius, shell thickness, environmental refractive index, and incident light direction angle on the light absorption and scattering properties, local electric field enhancement, and photothermal effect of the Ag@TiO₂ core–shell nanosphere dimer is investigated by using the finite element method and the finite difference time domain. The formation mechanism of multipole resonance mode of the dimer is revealed by means of the multipole decomposition theory and the internal current distribution of the particles. The results show that light absorption and scattering of the dimer can be tuned within the visible light range by changing the particle spacing, core radius, and shell thickness. With the azimuth angle of incident light increases, the longitudinal local surface plasmon resonance (L-LSPR) mode will transform into the transverse local surface plasmon resonance (T-LSPR) mode, and the L-LSPR mode makes the dimer have better local electric field enhancement. Strong light absorption can easily cause a sharp increase in the temperature around the dimer, accelerating the rate of catalytic oxidation reactions and the elimination of bacteria and viruses in water. Strong light scattering causes a significant enhancement of the electric field between the particles, making the generation of hydroxyl and other active oxides more efficient and convenient. This work establishes a theoretical basis for designing efficient water purification photocatalysts. Full article

With the increasing global demand for energy, the development of unconventional resources has become a focal point of research. Among these, shale gas has drawn considerable attention due to its abundant reserves. However, its low permeability and complex fracture networks present substantial challenges. This study investigates the composite fracturing technology combining supercritical CO₂ and slickwater for shale gas extraction, elucidating the mechanisms by which it influences shale fracture roughness and conductivity through an integrated approach of theory, experiments, and numerical modeling. Experimental results demonstrate that the surface roughness of shale fractures increases markedly after supercritical CO₂–slickwater treatment. Moreover, the dynamic evolution of permeability and porosity is governed by roughness strain, adsorption expansion, and corrosion compression strain. Based on fluid–solid coupling theory, a mathematical model was developed and validated via numerical simulations. Sensitivity analysis reveals that fracture density and permeability have a pronounced impact on shale gas field productivity, whereas fracture dip angle exerts a comparatively minor effect. The findings provide a theoretical basis for optimizing composite fracturing technology, thereby enhancing shale gas extraction efficiency and promoting effective resource utilization. Full article

Frequent water and mud inrush accidents during karst tunnel construction severely impact tunnel construction safety, environmental sustainability, and the long-term use of infrastructure. Therefore, conducting practical risk assessment for karst tunnel water and mud inrush is crucial for promoting sustainable practices in tunnel engineering, as it can mitigate catastrophic events that lead to resource waste, ecological damage, and economic loss. This paper establishes an improved weighted cloud model for karst tunnel water and mud inrush risk to evaluate the associated risk factors. The calculation of subjective weight for risk metrics adopts the ordinal relationship method (G1 method), which is a subjective weighting method improved from the analytic hierarchy process. The calculation of objective weight employs the improved entropy weight method, which is superior to the traditional entropy weight method by effectively preventing calculation distortion. Game theory is applied to calculate the optimal weight combination coefficient for two computational methods, and cloud model theory is finally introduced to reduce the fuzziness of the membership interval during the assessment process. This study applied the established risk assessment model to five sections of the Furong Tunnel and Cushishan Tunnel in Southwest China. The final risk ratings for these sections were determined as “High Risk,” “High Risk,” “Medium Risk,” “High Risk,” and “Moderate Risk”, respectively. These results align with the findings from field investigations, validating the effectiveness and reliability of the cloud model-based mud and water outburst risk assessment using combined weighting. Compared to traditional methods such as fuzzy comprehensive evaluation and entropy weighting, the evaluation results from this study’s model demonstrate higher similarity and reliability. This provides a foundation for assessing mud and water outburst hazards and other tunnel disasters. Full article

The effect of third-order shear deformation theory (TSDT) on thick functionally graded material (FGM) spherical shells under sinusoidal thermal vibration is investigated by using the generalized differential quadrature (GDQ) numerical method. The TSDT displacement field and an advanced nonlinear shear correction coefficient are used to derive the equations of motion for FGM spherical shells. The simple stiffness of FGM spherical shells under a temperature difference along the linear vs. z-axis direction is considered in the heat conduction equation. The dynamic GDQ discrete equations of motion subjected to thermal load and inertia terms can be expressed in matrix form. A parametric study of environmental temperature, FGM power-law index, and advanced nonlinear shear correction on thermal stress and displacement is conducted under the vibration frequency of a simply homogeneous equation and applied heat flux frequency. This is a novel method for obtaining the numerical GDQ results, comparing cases with linear and advanced nonlinear shear correction. The novelty of the present work is that an advanced varied-value type of shear correction coefficient can be successfully used in the thick-walled structure of FGM spherical shells subject to thermal vibration while considering the nonlinear term of TSDT displacements. The purpose of the present work is to investigate the numerical thermal vibration data for a two-material thick FGM spherical shell. Full article

Using platform self-operation, customer reviews, and compensation commitments as traditional benchmarks, this study foregrounds blockchain traceability as a technology-enabled authenticity signal in cross-border cosmetic e-commerce (CBEC). Using an 8-scenario orthogonal experiment, we test a model in which perceived risk mediates the effects of authenticity signals on purchase intention. We probe blockchain boundary conditions by examining their interactions with traditional signals. Our results show that blockchain is the only signal with a significant direct effect on purchase intention and that it also exerts an indirect effect by reducing perceived risk. While customer reviews show no consistent effect, self-operation and compensation influence purchase intention indirectly via risk reduction. Moderation tests indicate that blockchain is most effective in low-trust settings—i.e., when self-operation, reviews, or compensation safeguards are absent or weak—while this marginal impact declines when such safeguards are strong. These findings refine signaling theory by distinguishing a technology-backed signal from institutional and social signals and by positioning perceived risk as the central mechanism in CBEC cosmetics. Managerially speaking, blockchain should serve as the anchor signal in high-risk contexts and as a reinforcing signal where traditional assurances already exist. Future work should extend to field/transactional data and additional signals (e.g., brand reputation, third-party certifications). Full article

Fresnel wave surfaces, or isofrequency light shells, provide a powerful framework for describing electromagnetic wave propagation in anisotropic media, yet their applicability is restricted to reciprocal, lossless materials and far-field radiation. This paper extends the concept by incorporating near-field effects and non-Hermitian responses arising in media with loss, gain, or non-reciprocity. Using the Om-potential approach to macroscopic electromagnetism, we reinterpret near fields as off-shell electromagnetic modes, in analogy with off-shell states in quantum field theory. Formally, both QFT off-shell states and electromagnetic near-field modes lie away from the dispersion shell; physically, however, wavefunctions of fundamental particles admit no external sources (virtual contributions live only inside propagators), whereas macroscopic electromagnetic near-fields are intrinsically source-generated by charges, currents, and boundaries and are therefore directly measurable—for example via near-field probes and momentum-resolved imaging—making “off-shell” language more natural and operational in our setting. We show that photonic density of states (PDOS) distributions near Fresnel surfaces acquire Lorentzian broadening in non-reciprocal media, directly linking this effect to the Beer–Bouguer–Lambert law of exponential attenuation or amplification. Furthermore, we demonstrate how Abraham and Minkowski momenta, locked to light shells in the far field, naturally shift to characterize source structures in the near-field regime. This unified treatment bridges the gap between sources and radiation, on-shell and off-shell modes, and reciprocal and non-reciprocal responses. The framework provides both fundamental insight into structured light and practical tools for the design of emitters and metamaterial platforms relevant to emerging technologies such as 6G communications, photonic density-of-states engineering, and non-Hermitian photonics. Full article

The freezing method is widely employed in the construction of a vertical shaft in soft soil and water-rich strata. As the construction depth increases, investigating the water–heat–force coupling effects induced by the hydration heat (internal heat source) of concrete is crucial for the safety of the lining structure and its resistance to cracking and seepage. A three-dimensional coupled thermal–hydraulic–mechanical analysis model was developed, incorporating temperature and soil relative saturation as unknown variables based on heat transfer in porous media, unsaturated soil seepage, and frost heave theory. The coefficient type PDE module in COMSOL was used for secondary development to solve the coupling equation, and the on-site temperature and pressure monitoring data of the frozen construction process were compared. This study obtained the model-related parameters and elucidated the evolution mechanism of freeze–thaw and freeze–swelling pressures of a frozen wall under the influence of hydration heat. The resulting model shows that the maximum thaw depth of the frozen wall reaches 0.3576 m after 160 h of pouring, with an error rate of 4.64% compared to actual measurements. The peak temperature of the shaft wall is 73.62 °C, with an error rate of 3.76%. The maximum influence range of hydration heat on the frozen temperature field is 1.763 m. The peak freezing pressure is 4.72 MPa, which exhibits a 5.03% deviation from the actual measurements, thereby confirming the reliability of the resulting model. According to the strength growth pattern of concrete and the freezing pressure bearing requirements, it can provide a theoretical basis for quality control of the lining structure and a safety assessment of the freezing wall. Full article

The primary objective of this study is to provide a more precise and beneficial mathematical model for assessing corruption dynamics by utilizing non-local derivatives. This research aims to provide solutions that accurately capture the complexities and practical behaviors of corruption. To illustrate how corruption levels within a community change over time, a non-linear deterministic mathematical model has been developed. The authors present a non-integer order model that divides the population into five subgroups: susceptible, exposed, corrupted, recovered, and honest individuals. To study these corruption dynamics, we employ a new method for solving a time-fractional corruption model, which we term the q-homotopy analysis transform approach. This approach produces an effective approximation solution for the investigated equations, and data is shown as 3D plots and graphs, which give a clear physical representation. The stability and existence of the equilibrium points in the considered model are mathematically proven, and we examine the stability of the model and the equilibrium points, clarifying the conditions required for a stable solution. The resulting solutions, given in series form, show rapid convergence and accurately describe the model’s behaviour with minimal error. Furthermore, the solution’s uniqueness and convergence have been demonstrated using fixed-point theory. The proposed technique is better than a numerical approach, as it does not require much computational work, with minimal time consumed, and it removes the requirement for linearization, perturbations, and discretization. In comparison to previous approaches, the proposed technique is a competent tool for examining an analytical outcomes from the projected model, and the methodology used herein for the considered model is proved to be both efficient and reliable, indicating substantial progress in the field. Full article

Under extreme operating conditions, the internal lubricating flow field of high-speed gear transmission systems exhibits a transient oil–gas multiphase flow, predominantly governed by cavitation-induced phase transitions and turbulent shear. This phenomenon involves complex mechanisms of nonlinear multi-physical coupling and energy dissipation. Traditional lubrication theories and single-phase flow simplified models show significant limitations in capturing microsecond-scale flow features, dynamic interface evolution, and turbulence modulation mechanisms. To address these challenges, this study developed a cross-scale coupled numerical framework based on the Lattice Boltzmann method and large eddy simulation (LBM-LES). By incorporating an adaptive time relaxation algorithm, the framework effectively enhances the computational accuracy and stability for high-speed rotational flow fields, enabling the precise characterization of lubricant splashing, distribution, and its interaction with air. The research systematically reveals the spatiotemporal evolution characteristics of the internal flow field within the gearbox and focuses on analyzing the nonlinear regulatory effect of baffle geometric parameters on the system’s energy transport and dissipation characteristics. Numerical results indicate that the baffle structure significantly influences the spatial distribution of the vorticity field and turbulence intensity by reconstructing the shear layer topology. Low-profile baffles optimize the energy transfer pathway, effectively reducing the flow enthalpy, whereas excessively tall baffles induce strong secondary recirculation flows, exacerbating vortex-induced energy losses. Simultaneously, appropriately increasing the spacing between double baffles helps enhance global lubricant transport efficiency and suppresses unsteady dissipation caused by localized momentum accumulation. Furthermore, the geometrically optimized double-baffle configuration can achieve synergistic improvements in lubrication performance, oil film stability, and system energy efficiency by guiding the main shear flow and mitigating localized high-momentum impacts. This study provides crucial theoretical foundations and design guidelines for developing the next generation of theory-driven, energy-efficient lubrication design strategies for gear transmissions. Full article

Disaster headlines often underscore devastation and loss while overlooking success stories where proactive disaster risk reduction (DRRM) measures have averted catastrophe, saved lives, and reduced economic damage. This study addresses the gap in documentation and analysis of DRRM success stories in Africa, particularly within the Southern African Development Community (SADC), arguing that the absence of such narratives hampers a shift from reactive to proactive disaster risk governance. The research aims to extract critical lessons from success stories for enhancing future preparedness and response frameworks. A qualitative research design was employed, integrating document analysis, expert interviews, field observations, and practitioner workshops. Data was triangulated from diverse sources, including national disaster management agency reports (e.g., South Africa’s NDMC, Botswana’s NDMO, Mozambique’s INGC), peer-reviewed literature, UNDRR reports, SADC policy documents, and first-hand experiences from the authors’ consultancy work in the African Union’s biennial DRRM reporting processes. Case studies examined include Mozambique’s response to Cyclone Idai in 2019, South Africa’s drought and flood risk governance (e.g., the 2023 floods in Eastern and Western Cape), and Malawi’s flood resilience programs. Findings reveal that successful DRRM outcomes are driven by a combination of anticipatory governance, community-based preparedness, integration of Indigenous Knowledge Systems (IKSs), and investment in infrastructure and ecosystem-based adaptation. These cases demonstrate that locally embedded, yet scientifically informed, interventions enhance resilience and reduce disaster impacts. The study underscores the relevance of theoretical frameworks such as resilience theory, narrative theory, and social learning in interpreting how success stories contribute to institutional memory and adaptive capacity. Policy recommendations emphasize the need for institutionalizing success-story documentation in national DRRM frameworks, scaling up community engagement in risk governance, and fostering regional knowledge-sharing platforms within the SADC. Furthermore, the paper advocates for making DRRM success stories more visible and actionable to transition toward more anticipatory, inclusive, and effective disaster risk management systems. Full article

The development of generative AI has disrupted various fields, and the field of online healthcare is no exception. However, there is a lack of research on patients’ intention to adopt generative AI on online healthcare platforms. Therefore, the aim of this study is to investigate the factors influencing patients’ intention to adopt generative AI. Employing a questionnaire-based survey, we explore the factors influencing patients’ intention to adopt generative AI through the UTAUT2 model, considering the moderating effects of construal level, health literacy, and AI literacy. We find that performance expectancy, social influence, and facilitating conditions are positively associated with patients’ intention. Surprisingly, effort expectancy and hedonic motivation do not have a significant impact on patients’ intention. Construal level positively moderates the relationship between performance expectancy and patients’ intention; health literacy negatively moderates the relationship between social influence and patients’ intention. AI literacy positively moderates the relationship between effort expectancy and patients’ intention but negatively moderates the relationship between social influence and patients’ intention. This study enriches UTAUT2 theory and provides practical insights for the development and promotion of generative AI on online healthcare platforms. Full article

The cooperative flight of manned and unmanned aerial vehicles (MAV/UAVs) has recently become a focus in the research of civilian and humanitarian fields, in which formation control is crucial. This paper takes the improvement of convergence performance and resource conservation as the entry point to study control problems of cooperative formation configuration of MAV/UAVs. Following the backstepping recursive design procedures, an event-triggered fixed-time formation control strategy for MAV/UAVs operating under modeling uncertainties and external disturbances is presented. Moreover, a novel switching threshold event-triggered mechanism is introduced, which dynamically adjusts control signal updates based on system states. Compared with periodic sampling control (Controller 1), fixed threshold strategies (Controller 2) and relative threshold strategies (Controller 3), this mechanism enhances resource efficiency and prevents Zeno behavior. On the basis of Lyapunov stability theory, the closed-loop system is shown to be stable in the sense of the fixed-time concept. Numerical simulations are carried out in Simulink to validate the effectiveness of the theoretical findings. The results show that compared with the three comparison methods, the proposed control method saves 86%, 34%, and 43% of control transmission burden respectively, which significantly reduces the number of triggered events. Full article

While methods like cyclic triaxial testing and p-y model updating theory exist in geotechnical and offshore wind engineering, they have not been systematically applied to solve the specific deformation problems of offshore PV piles. This study investigates a specific offshore photovoltaic (PV) project in Qinhuangdao City, Hebei Province. Initially, field tests of horizontal static load on steel pipe pile foundations were conducted. A finite element model (FEM) of single piles was subsequently developed and validated. Further analysis examined the failure modes, initial stiffness, and ultimate resistance of offshore PV single piles in sandy soil foundations under varying pile diameters and embedment depths. The hyperbolic p-y curve model was modified by incorporating pile diameter size effects and embedment depth considerations. Key findings reveal the following: (1) The predominant failure mechanism of fixed offshore PV monopiles manifests as wedge-shaped failure in shallow soil layers. (2) Conventional API specifications and standard hyperbolic models demonstrate significant deviations in predicting p-y (horizontal soil resistance-pile displacement) curves, whereas the modified hyperbolic model shows good agreement with field measurements and numerical simulations. This research provides critical data support and methodological references for calculating the horizontal bearing capacity of offshore PV steel pipe pile foundations. Full article