Search Results (19,910)

Bird strike events on rotating jet engine fan blades pose significant risks to aviation safety, yet high-fidelity numerical simulations remain computationally expensive, limiting their use in parametric design studies. This study develops a physics-guided machine learning surrogate framework for predicting bird strike response on rotating Ti-6Al-4V fan blades, systematically comparing Lagrangian (gelatin-based, Mooney–Rivlin) and Smoothed Particle Hydrodynamics (SPH, water-like) formulations. A total of 100 explicit dynamic simulations were conducted in ANSYS LS-DYNA (R2) (50 per formulation), varying bird impact velocity and blade angular speed. Random Forest, Support Vector Regression, Polynomial Regression, and XGBoost regression models were trained and evaluated using five-fold cross-validation. Results demonstrate that SPH-based surrogates achieve superior predictive accuracy, with Random Forest yielding R² = 0.9938 for maximum deformation and R² = 0.9962 for total energy dissipation. In contrast, Lagrangian-based stress surrogates exhibited severe performance degradation (R² = 0.24) due to mesh-dependent numerical noise. The trained surrogates achieved computational speed-up factors of 10⁴–10⁵ relative to direct simulation. These findings establish that surrogate model reliability is fundamentally governed by the numerical quality of the training data, providing guidance for integrating machine learning with impact simulation workflows in aero-engine blade design. Full article

Reconciling environmental conservation with socioeconomic development remains a fundamental challenge for tropical forest governance. Thirty-five years ago, the Brazilian Amazon pioneered Extractive Reserves (RESEXs) as a radical model for socio-environmental synergy; however, their long-term efficacy faces increasing contestation. Through a systematic review of three and a half decades of research, we analyze the RESEX model’s performance in balancing forest integrity with the livelihoods of traditional communities. Our synthesis reveals a persistent conservation–development misalignment, where the prioritization of ecological preservation is coupled with chronic underinvestment in socioeconomic infrastructure. We demonstrate that this imbalance has yielded a “vulnerability trap,” where stagnant agro-extractive initiatives and insufficient technological integration inadvertently push local populations toward predatory land-use alternatives. We argue that prioritizing forest cover metrics while neglecting the economic agency of traditional populations constitutes an inadequate strategy for the Amazon’s future. Following the outcomes of the 30th Conference of the Parties (COP30) in Belém, we propose a paradigm shift toward a technologically enabled bioeconomy—a move essential for the pragmatic success of global climate goals and the protection of the Amazonian climate anchor. Full article

Background: South African university students face escalating levels of psychological distress driven by academic overload, financial precarity, and social challenges. Health professions students are particularly vulnerable due to the demanding nature of clinical training and repeated exposure to human suffering. Aim: This study aims to propose an Integrated Student Well-being and Resilience Model tailored to the South African health professions education context. Methods: This conceptual paper draws on empirical evidence from South African studies on student mental health, global campus well-being frameworks, and socio-ecological theory. Bronfenbrenner’s Socio-Ecological Systems Theory and a tiered public health approach were synthesized to develop a multi-level model aimed at addressing the academic, financial, and social determinants of student mental health. Conceptual synthesis: The study unequivocally identified a syndemic of interconnected factors predisposing students to depression, which included the interplay of academic rigour and cognitive burnout, financial vulnerability as a determinant of mental health, the crisis of social connection and psychological safety, and institutional failure and the resilience fallacy. Conclusions: The Integrated Student Well-being and Resilience (ISWR) Model is a systemic architecture designed to coordinate institutional governance with the complex psychosocial needs of health professions students. The model provides a holistic, scalable framework for strengthening student well-being within health professions education. By shifting from reactive counselling to proactive, system-level interventions, the model offers a strategic blueprint for creating resilient, supportive learning environments capable of improving student mental health and fostering a healthier future healthcare workforce. Full article

In the context of China’s rapid urbanization and rural land marketization reforms, the entry of rural collectively owned commercial construction land into the market (ERCCCLM) coexists with the traditional government-led land expropriation, forming a dual land supply system. China’s dual-structure land ownership system—where urban land belongs to the state and rural land to rural collectives—aims to balance land market allocation efficiency with government regulation for public interests. However, significant differences exist between the two patterns in terms of revenue distribution, risk-bearing, and institutional constraints. Consequently, stakeholders including rural collective economic organizations, farmers, local governments, and development companies face dilemmas in selecting land supply patterns, thereby limiting land resource allocation efficiency. The research employs multidimensional economic analysis to systematically compare the ERCCCLM and land expropriation patterns, establishing a land supply pattern selection mechanism with land market price and compensation for expropriation as key variables. First, the expenditure and revenue of stakeholders in both patterns were clarified based on relevant documents, and investment revenue models were constructed. Second, through comparative analysis of revenue formation mechanisms across land supply patterns and sensitivity analysis of multi-scenario calculations, the land market price and compensation for expropriation are identified as key variables determining economic revenue. The findings indicate that when the land market price exceeds compensation for expropriation, ERCCCLM generates higher economic revenue for the rural collective economic organization and farmer. Conversely, when the land market price is equal to or lower than the compensation for expropriation, land expropriation provides more stable revenue. The land expropriation and ERCCCLM examined in this research represent a unique land expropriation and utilization system exclusive to China. The proposed selection mechanism improves land market distribution efficiency and informs policy discussions on optimizing land supply patterns, ensuring a balance between market efficiency and stakeholder equity. Full article

Digital Twin (DT) implementations in electrical and industrial systems are governed by fragmented ISO/IEC and IEC standards spanning terminology, architecture, interoperability, lifecycle management, and cybersecurity. This paper proposes a mathematical framework that integrates these standards into a unified compliance model. A layered DT architecture is defined as a finite set of functional abstractions, and standards are linked to layers through a multivalued mapping and an incidence matrix. Traceability, interoperability, fidelity, and security/governance indicators are normalized and aggregated through a bounded weighted functional to obtain a deterministic compliance score. The model is then extended by treating selected indicators as random variables, which enables probabilistic maturity classification and Monte Carlo-based robustness analysis. The resulting functional is bounded, monotone, and stable under bounded perturbations. Numerical experiments on a synthetic portfolio illustrate deterministic scoring and uncertainty effects. The framework provides a proof-of-concept basis for structured DT compliance assessment across heterogeneous electrical systems; however, broader empirical validation is still required before operational deployment. Full article

The widely applied empirical Darcy’s law in geotechnical engineering faces significant challenges in describing low-velocity flow processes in low-permeability porous media such as tight sandstones containing irreducible water. A deep understanding of low-velocity non-Darcy two-phase flow behavior in low-permeability porous media is essential for evaluating the development of ultra-low-permeability reservoirs. In this study, seven low-permeability three-dimensional digital cores with distinct pore structures were constructed based on realistic ultra-low-permeability sandstones. Using the lattice Boltzmann method, pore-scale investigations of water displacing oil were conducted. Low-velocity two-phase flow behavior under varying wettability conditions, pore structures, and fluid viscosities was simulated. The underlying mechanisms of low-velocity non-Darcy flow in ultra-low-permeability sandstones were examined, leading to a modified low-velocity non-Darcy flow equation. This improved model was subsequently applied to numerical simulations of ultra-low-permeability reservoirs. The results demonstrate that non-Darcy effects manifest primarily as nonlinearities in seepage curves, representing a marked departure from conventional Darcy’s law. Low-velocity non-Darcy (LVND) flow is predominantly constrained by the influence of complex pore-throat structures and capillary forces on fluid distribution. The dynamic equilibrium among capillary forces arising from residual water saturation, viscous forces, and pressure gradients constitutes the fundamental mechanism governing the onset of LVND flow. Enhanced nonlinear behavior is observed with increasing viscosity of the invading phase and elevated capillary forces. Substantial discrepancies in reservoir production dynamics are identified between LVND and classical Darcian regimes. Through pore-scale numerical simulations, this study systematically elucidates LVND behavior during bi-phasic flow in low-permeability porous media, while identifying critical controlling factors. These findings provide scientific rationale and technical support for addressing geological engineering challenges in tight sandstone formations. Full article

Building Information Modeling (BIM) has fundamentally changed the way interdisciplinary coordination works in construction projects; however, the theoretical mechanisms underlying open collaboration standards in this field remain insufficiently explored. This article fills this gap by presenting a systematic analysis of the BIM Collaboration Format (BCF) through the lens of reification and serialization, two fundamental concepts in information systems theory. Although the BCF format is widely used in the industry and implemented in major BIM tools for clash detection and issue tracking, the existing literature treats it primarily as an operational tool, overlooking the deeper information systems principles that govern its architecture. The analysis demonstrates that BCF achieves reification by transforming informal coordination knowledge—such as verbally communicated clashes, scattered email threads, and undocumented design decisions—into first-class objects (Topic, Comment, Viewpoint) equipped with unique identifiers, typed attributes, ownership, temporal metadata, and formalized inter-object relationships. Further analysis was conducted on BCF’s serialization mechanisms, including XML encoding for file exchange, JSON for RESTful API communication, and ZIP archiving as a distribution container, each of which was selected to balance human readability, schema validation, compression, and cross-platform portability. The complementarity of these two mechanisms was examined: reification determines what to preserve and in what structure, while serialization determines how to encode and in what format, which together enable interoperable, auditable, and automatable coordination workflows in heterogeneous software environments. The analysis was illustrated with a real-world BCF example from a major infrastructure project in Poland, demonstrating practical alignment between theoretical constructs and their implementation. The research results provide both a conceptual foundation for researchers working on openBIM standards and practical guidance for practitioners seeking to optimize issue management, the implementation of a Common Data Environment (CDE), and the specification of Exchange Information Requirements (EIR). The study contributes new knowledge in three areas: (1) To the best of the authors’ knowledge, it provides the first systematic theoretical analysis of BCF through the lens of reification and serialization, filling a gap between the format’s widespread practical use and its limited theoretical understanding. (2) It demonstrates how the formal criteria of reification (unique identity, typed attributes, ownership, temporal metadata, and inter-object relationships) map onto specific BCF entities, offering a transferable analytical framework for evaluating other openBIM standards. (3) It identifies the complementarity of reification and serialization as a design principle that can guide the development of future standards for digital twins and IoT-based facility management. Full article

Traffic accident detection is a critical component of intelligent transportation systems (ITS), enabling timely incident response and traffic management. While most existing approaches rely on temporal information from video sequences, such methods are not always applicable in resource-constrained surveillance environments. This study investigates the feasibility of detecting traffic accidents from single static images by formulating the task as a binary classification problem. Representative architectures, including Vision Transformer (ViT), Swin Transformer, and ResNet-50, are systematically evaluated on the Car Crash Dataset (CCD) under multiple training configurations. To assess generalization capability, cross-domain evaluation is conducted using an external crash video dataset (ECVD) constructed to approximate real-world deployment conditions. Experimental results show that all models achieve strong performance under in-domain evaluation. However, cross-domain testing reveals substantial performance degradation, particularly in recall, indicating limited generalization capability under domain shift. Qualitative analysis further shows that missed detections are associated with weak visual cues, occlusion, and complex traffic environments, while false positives are caused by visually ambiguous patterns resembling accident scenarios. Unlike prior studies that primarily report performance improvements, this work provides empirical evidence that model behavior in static-image-based accident detection is governed by dataset composition rather than architectural design. Therefore, static-image-based accident detection should be interpreted as a coarse-level screening tool rather than a fully reliable decision-making system. This study highlights the importance of data-centric design and cross-domain evaluation for improving real-world applicability. Full article

Massive-scale, ultra-high-resolution numerical simulations for climate change prediction provide data of exceptional accuracy and reliability. However, this comes at the cost of enormous computational resources, and the underlying processes often remain a “black box”. In contrast to these sophisticated methods, we theoretically analyzed the water vapor feedback effect using a highly simplified model that focuses exclusively on the most critical physical factors governing climate change. Specifically, we formulated a two-layer box model by dividing the entire atmosphere into layers of equal optical thickness. Using this model, we quantitatively verified the extent to which the water vapor feedback effect—a key driver of global warming—can be theoretically reproduced. Full article

This work investigates the δ→σ phase transformation in 24Cr-14Ni stainless steel, specifically focusing on how heat treatment temperature, time, and nitrogen atmospheric ratios (NARs) dictate microstructural stability. Understanding the formation mechanism of the σ phase is critical for alloy design, as it remains the most detrimental intermetallic phase in austenitic steels. The results show that δ-ferrite decomposes into σ and secondary γ₂ phases through a cellular eutectoid reaction driven by elemental diffusion. Higher Cr and Si levels stabilize δ-ferrite and promote σ phase precipitation, accelerating the δ→σ transformation. Furthermore, the σ phase exhibits the highest Cr_eq/Ni_eq ratio among all constituent phases. The σ phase fraction is highest with 0 vol.% NAR during 1–8 h of aging and decreases progressively with increasing NARs (20–40 vol.%), reaching a minimum at 80 vol.% under all conditions. JMAK model analysis (n ≈ 0.531, k ≈ 0.905) indicates that σ phase precipitation at 800 °C with 40 vol.% NAR is governed by diffusion-controlled growth with early nucleation site saturation in δ-ferrite. Consequently, rapid σ phase formation occurs, reaching ~21.3% within 1 h. This behavior is attributed to the instability of δ-ferrite and the faster diffusion of Cr and Si compared to austenite. Full article

We propose a dimensionally consistent fractional spatio-temporal PDE framework for modelling immune-mediated demyelination in multiple sclerosis (MS). The system couples effector and regulatory T cells, M1/M2 macrophage polarisation, pro- and anti-inflammatory cytokines, oligodendrocyte dynamics, and time-dependent therapeutic controls within a unified distributed-parameter structure. In contrast to ad hoc replacements of integerorder derivatives by Caputo fractional derivatives, the fractional extension proposed here is derived from an underlying continuous-time random walk (CTRW) process with Mittag–Leffler-distributed residence times. This stochastic derivation yields a governing system in which a single commensurate fractional order α ∈ (0, 1], together with a characteristic memory timescale τ0, ensures dimensional consistency and mass balance across all coupled components. The model is formulated as a system of nonlinear reaction–diffusion equations with cross-regulatory and multiplicative interaction terms governing immune amplification, cytokine feedback, and the demyelination–remyelination balance. Analytical interpretation shows how non-Markovian residence times induce Mittag–Leffler-type relaxation and thereby modify effective growth, decay, and stability properties. Numerical simulations compare classical and fractional dynamics, revealing that memory-driven kinetics prolong effector T-cell and M1-macrophage activity, attenuate reparative M2 and oligodendrocyte responses, and extend the effective action of bang–bang therapy inputs representing IFN-β and glatiramer acetate beyond their dosing windows. The results indicate that integer-order models may underestimate chronic inflammatory persistence and demyelination severity, while providing a mathematically and physically well-posed platform for memory-aware immune modelling and therapy evaluation in MS. Full article

Digital social platforms have transformed political discourse into complex socio-technical environments characterized by rapid narrative diffusion, emotional amplification, and large-scale audience interaction. Understanding how sentiment and semantic alignment interact within such environments is important for analyzing polarization and patterns of audience response. This study examines narrative–audience interaction in Telegram political ecosystems using a combination of sentiment analysis, semantic similarity measures, and engagement metrics. Transformer-based language models are applied to quantify relationships between source posts and user-generated comments, enabling joint analysis of affective tone and topical alignment. The results reveal a consistent affective–semantic asymmetry: user responses tend to remain semantically aligned with source narratives while shifting toward more negative sentiment. This pattern indicates that disagreement is predominantly expressed through affective reframing rather than through divergence from the original topic. Further analysis shows systematic differences across ideological groups. Pro-government channels exhibit higher reach and more stable discourse alignment, while pro-opposition channels generate stronger engagement and more pronounced negative sentiment shifts. Neutral channels display intermediate characteristics. These findings demonstrate that online political discourse in Telegram is characterized by stable topical anchoring combined with systematic variation in emotional response. Full article

Corporate tax avoidance has become a major governance and fiscal sustainability concern, particularly in developing economies where corporate tax revenues constitute a critical source of public financing. While prior research suggests that board gender diversity (BGD) enhances ethical oversight and monitoring, its effectiveness in constraining aggressive tax planning may depend on firms’ informational and technological environments. This study examines whether artificial intelligence (AI) capability strengthens the governance role of BGD in reducing corporate tax avoidance. Using a balanced panel of 1586 non-financial firms from developing economies over the period 2009–2023, the analysis employs firm FE models and dynamic two-step System GMM estimations to address unobserved heterogeneity, endogeneity, and the persistence of corporate tax behavior. The results indicate that BGD is positively associated with effective tax rates, implying lower levels of corporate tax avoidance. Furthermore, AI capability—measured using a lagged specification—significantly strengthens this relationship, suggesting that firms with higher AI adoption exhibit a stronger governance effect of gender-diverse boards on tax compliance. Additional robustness tests—including alternative tax avoidance measures, alternative BGD specifications, heterogeneity analysis, and selection-bias corrections using Heckman, propensity score matching (PSM), and instrumental variable (2SLS) approaches—confirm the stability of the findings. Overall, the results highlight the complementary role of technological capability and board diversity in strengthening corporate governance (CG) and fiscal discipline in developing economies. Full article

Rock–fill composite slopes formed during the transition from underground to open-pit mining in metal mines are highly susceptible to interface hydraulic weakening and sudden sliding under intense rainfall, mainly due to the permeability contrast between the two media. Taking the Shizhuyuan Mine as a case study, a coupled hydro-mechanical numerical model was developed in ABAQUS 2025 to investigate slope stability under different rainfall patterns and interface strength degradation scenarios. The spatiotemporal evolution of seepage and deformation fields was examined in detail, with particular attention given to the variation of the safety factor, the distribution of pore water pressure along the interface, and the characteristics of interface slip. The results show that: (1) the deterioration of the hydraulic condition within the slope is governed by the water-blocking effect of the interface and the infiltration threshold of the surface layer. Under the same total rainfall, prolonged low-intensity rainfall is more likely than short-duration intense rainfall to produce sustained deep infiltration, and the factor of safety decreases from the initial 1.369 to 1.173 (0.005 m/h, 288 h) and 1.255 (0.02 m/h, 72 h), respectively, indicating that the former exerts a more pronounced weakening effect on slope stability. (2) Slope instability exhibits a clear interface-controlled pattern. Regardless of the degree of parameter degradation, the base of the plastic zone consistently develops along the rock–fill interface, accompanied by extensive plastic deformation within the overlying fill material. (3) Failure initiates at the slope toe where the mechanical equilibrium along the rock–fill interface is first disturbed. Under the combined influence of topographic conditions and the water-blocking effect of the interface, rainfall infiltration tends to converge toward the slope toe and form a local high-pore-pressure zone, resulting in a marked reduction in the effective normal stress at the interface. Once the local shear stress exceeds the shear strength, yielding is triggered first at the slope–toe interface, which then induces plastic deformation in the overlying fill material and ultimately leads to overall slope instability. Full article

The CO₂ storage–regeneration (CO₂-SR) process represents a promising strategy for integrating CO₂ capture and catalytic conversion within a single cyclic operation using multifunctional catalysts. In this concept, CO₂ is first stored on basic sites and subsequently converted through methane activation, enabling the coupling of CO₂ capture and reforming reactions in a single reactor. In this work, a series of unsupported Ni–Ba catalysts were investigated as model multifunctional materials for the CO₂-SR process. Catalysts with different Ni/Ba ratios were prepared to analyze how the distribution of storage and catalytic sites influences the cyclic CO₂ capture–conversion behavior. In addition, Rh was introduced as a promoter either during synthesis by co-precipitation or ex situ by impregnation, allowing to evaluate the influence of Rh location and surface enrichment on the catalytic properties. Rh incorporation in the NiBa catalyst (Ni/Ba = 10/1 and Ni/Rh = 100/1) increased the specific surface area (BET area 64 m²·g⁻¹ vs. 55 m²·g⁻¹ for NiBa) and reduced the NiO crystallite size from 250.4 Å to 231.5 Å, indicating improved dispersion of the metallic phase. XPS analysis revealed the coexistence of Rh⁰ and Rh³⁺ species, suggesting that Rh acts as a redox mediator that facilitates hydrogen activation and promotes hydrogen spillover to neighboring Ni sites. Raman and CO₂-TPD results show that Ba-derived domains stabilize carbonate species responsible for CO₂ storage, while Rh enhances catalyst reducibility and modifies the kinetics of carbonate decomposition during the regeneration stage. Transient CO₂–CH₄ pulse experiments demonstrate that the CO₂-SR process proceeds through a dynamic surface cycle involving reversible carbonate formation on Ba-derived basic sites coupled with methane activation on Ni-containing interfacial sites. The results indicate that catalyst performance is governed by a hierarchical surface architecture composed of Ni–O–Ba interfacial domains, reversible Ba–O–Ba carbonate storage sites, and more stable Ba-rich domains. The distribution of these domains, controlled by the Ni/Ba ratio and the dispersion of the metallic phase, determines the reversibility of carbonate formation and the efficiency of the cyclic CO₂ storage–regeneration process. Full article