Search Results (605)

We formulate a covariant mechanical equilibrium condition for the quark–hadron mixed phase boundary in the presence of a magnetic-field-induced pressure anisotropy. Using the relativistic thin-shell formalism to describe the quark–hadron boundary, we interpret conservation of stress-energy across the interface as a set of generalized Young–Laplace conditions which characterize the geometry of the interface. In a comoving stationary frame, this provides a covariant description of mechanical equilibrium at the interface, which serves as a replacement for the scalar pressure-balance condition used in the isotropic Gibbs construction. Full article

Prescribed fires can reduce hazardous fuel loads, but planning remains challenging in landscapes with complex terrain, mixed vegetation, and nearby infrastructure. Predicting and controlling where a prescribed fire may breach its containment lines can be carried out by integrating fire-behavior simulations with practical treatment planning. This paper presents a two-stage framework using QUIC-Fire to identify high-risk escape zones and allocate containment treatments under cost and resource constraints. Stage 1 identifies high-risk boundary segments and assigns adjacent zones to fuel removal or moisture treatment to limit simulated fire spread beyond the control line. Stage 2 refines these assignments by incorporating treatment costs and penalty values near infrastructure to evaluate resource-constrained alternatives. Applied to a 14.2-ha prescribed-fire unit in Mount Laguna, California, the optimized Stage 2 configuration maintained containment under the simulated conditions while reducing total implementation cost from USD 97,319 to USD 95,266 (approximately 2.1%) and reducing fire-engine demand from six to three. These results illustrate how cost-aware treatment reallocation can improve resource efficiency for prescribed-fire performance. Full article

Purpose: Saudi Arabia’s Vision 2030 designates tourism as a non-oil diversification engine. This study tests Tourism-Led Growth Hypothesis (TLGH) predictions among tourism professionals across five regions of the Kingdom of Saudi Arabia (KSA), proposing the TLGH-GCC (Gulf Cooperation Council) Framework. Design/Methodology/Approach: Sequential explanatory mixed-methods design: Structural Equation Modelling (SEM; N = 612; five regions) as primary evidence, executive interviews (n = 24) explaining mechanisms, and exploratory ARDL (T = 9; non-inferential). Findings: Perceptual support was found for all four hypothesised structural pathways (all p < 0.001), with megaproject investment exhibiting the strongest association with employment generation (β = 0.63) and sustainability governance challenges inversely associated with diversification efficiency. All associations are directionally consistent with TLGH predictions but do not establish causation. Qualitative findings further identified Saudisation alignment and workforce competency development as critical boundary conditions for translating tourism employment growth into sustained economic diversification. Theoretical Contribution: The TLGH-GCC Framework extends TLGH with institutional acceleration, Dutch Disease boundary conditions, and sustainability governance as a diversification determinant. The SGS-6 scale is validated for GCC megaproject contexts. Practical Implications: Regional decentralisation of gigaproject investment, occupational upgrading, and proactive sustainability governance are the highest-leverage Vision 2030 policy interventions. The findings further inform human capital development priorities under Vision 2030, including sector-specific tourism competency frameworks and Saudisation alignment in megaproject workforce planning. Originality/Value: The study addresses a methodological gap in the TLGH literature by combining five-region stratified SEM, executive interviews, and the validated SGS-6 sustainability governance scale within a single GCC-contextualised framework. Full article

Light Detection and Ranging (LiDAR) three-dimensional (3D) object detection degrades under point sparsity, outliers, coordinate noise, and calibration drift, yet detector evaluation remains largely limited to clean benchmarks. This study focuses on sensing robustness rather than detector redesign. We introduce Bounded Graph Conditioning (BGC)—a deterministic pre-voxelization front-end that applies k-nearest-neighbor (kNN) neighborhood averaging with bounded residual correction upstream of an unchanged detector backbone. BGC is evaluated together with a reproducible sensor-degradation stress protocol and a risk-constrained operating-boundary analysis. Experiments on KITTI with PointPillars, SECOND, and Voxel R-CNN show that BGC most clearly improves retained detection quality and feasible operating coverage under strong noise and strong outlier stress; gains under other degradation types are smaller and backbone-dependent. In the primary score-level box-disjoint calibration/test evaluation on SECOND, maximum feasible coverage at a target risk bound of $0.2$ improves from $0.0754$ to $0.1374$ under strong noise ($\sigma =0.10$ m) and from $0.1323$ to $0.1591$ under strong outliers ($p=0.10$); a cross-backbone check on Voxel R-CNN confirms the same direction ($0.1860\to 0.2864$). Comparison with traditional filtering (SOR and ROR) reveals complementary strengths across fault types. A range-adaptive BGC variant that adjusts parameters per distance bin further improves performance under mixed unknown faults, spherical-coordinate noise, and on a dataset-matched nuScenes validation (adaptive BGC mAP/NDS: $0.2687/0.4493$ vs. baseline $0.2471/0.3846$ under strong noise). Severe translation drift collapses all configurations to full rejection, exposing an explicit sensing boundary beyond the reach of local conditioning. These results support BGC as a practical sensor-side robustness enhancement under the studied degradation protocol, with conditional rather than universal applicability across backbones and fault types. Full article

Thermal monocular depth estimation can provide more robust depth predictions than RGB-based methods under nighttime and adverse weather conditions. However, when trained with projected LiDAR supervision, depth models often retain structural errors in sky regions, long-range areas, and object boundaries because LiDAR measurements are sparse or missing in such regions. To address this limitation, we propose a thermal monocular depth estimation framework that incorporates propagation-based refinement. To make this refinement applicable across different base models, we further design a multi-scale feature adapter that converts heterogeneous multi-scale features with different spatial resolutions and channel dimensions into a unified representation. As a result, the same refinement architecture can be used across different base models without model-specific refiner redesign. On the multispectral stereo (MS2) dataset, the proposed method improves both BTS (big-to-small) and NeWCRFs (neural window fully connected CRFs), reducing the meter-based error metrics SqRel from 0.380 to 0.369 and RMSE from 3.163 to 3.126 for BTS, and reducing SqRel from 0.331 to 0.328 and RMSE from 2.937 to 2.924 for NeWCRFs. Qualitative results further show that the proposed method alleviates mixed-depth artifacts and abnormal depth patterns in regions lacking reliable depth supervision. Full article

Driven by renewable energy, the operating temperatures of alkaline water electrolyzers (AWEs) exhibit significant dynamic variations. Conventional control strategies rely on fixed startup parameters, causing dispatch plans to deviate from actual physical states, which leads to transient over-temperature or startup failures. To address this issue, this paper proposes a dual-layer optimization strategy for multi-electrolyzer systems based on temperature–power adaptation. First, a thermo-electro-hydrogen coupling model is established to quantitatively reveal the dynamic relationship among the initial temperature, startup power, and transition time. This relationship is utilized to construct a dynamic startup boundary, overcoming the limitations of traditional static constraints. Within the proposed framework, the upper layer utilizes a Mixed-Integer Linear Programming (MILP) model to formulate state-switching and baseline power allocation plans derived from short-term forecasts. Concurrently, the lower layer employs the Mongoose Optimization Algorithm (MOA) for real-time rolling optimization, enabling the system to actively perceive temperature variations and adaptively schedule power allocation. Simulations across typical seasonal scenarios validate the strategy’s superiority. In a typical spring scenario, compared to the traditional Daisy Chain and Rotation Control strategies, as well as the Equal Allocation strategy, the proposed approach reduces total startup time and energy consumption by 59.2% and 54.6%, respectively. Furthermore, it increases wind power accommodation rates by 17.7% and 14.2%, and total hydrogen production by 20.0% and 14.9%, respectively. These superior renewable energy utilization and production efficiencies are robustly maintained across typical seasonal scenarios. By actively perceiving actual temperatures for adaptive scheduling, the proposed strategy ultimately ensures synergy and reliability between the control strategy and actual operational constraints under fluctuating conditions. Full article

Fishways play a critical role in restoring river connectivity and conserving fishery resources, yet their efficiency is often limited by mismatches between hydraulic conditions and species-specific behavioral traits. To quantify the upstream migration behavior of fish under the combined influence of flow heterogeneity and schooling effects, this study examined the endangered species L. elongata in the Yangtze River Basin. Volitional swimming behavior was tested in an open-channel flume under three spatially heterogeneous flow regimes (I: Low–Moderate–High; II: High–Moderate–Low; III: Moderate–High–Low). A video monitoring system recorded the upstream movement of solitary fish and three-individual schools. Swimming trajectories, upstream migration time, preferred flow velocities, and schooling metrics—including nearest neighbor distance (NND) and mean pairwise distance (MPD)—were analyzed. Linear mixed-effects models were employed to account for repeated measures and individual variability. Results showed that schooling behavior significantly enhanced upstream migration efficiency: schooling fish arrived at the target area on average 8.93 s earlier than solitary individuals (p < 0.01), while flow condition alone had no detectable effect on arrival time. L. elongata consistently preferred low-velocity zones (0.20–0.50 m/s) and avoided high-velocity regions (0.75–1.25 m/s), with meandering upstream trajectories predominating. NND showed no significant differences across flow conditions (p > 0.05), indicating stable schooling cohesion. However, MPD increased significantly under Flow III compared to Flows I and II (p < 0.01), suggesting that higher flow heterogeneity leads to more dispersed group spacing while overall cohesion is maintained. Distinct movement strategies were observed: solitary fish predominantly utilized boundary regions as hydraulic refuges (wall-following: 63.8–80.5%), whereas schools exhibited greater spatial exploration and reduced wall-following. These findings demonstrate that schooling enhances migration efficiency while preserving a cohesive group structure and that flow heterogeneity influences within-group spatial organization. To optimize fishway performance for L. elongata, we recommend maintaining flow velocities within 0.20–0.50 m/s. This study provides scientific guidance for hydraulic regulation in fishway design and habitat restoration, emphasizing the combined effects of flow heterogeneity and schooling behavior on migration performance. Full article

Despite the growing adoption of Ti6Al4V-ELI made by Laser Powder Bed Fusion (LPBF) in tribologically demanding applications, the influence of hybrid nanoparticle additives on its lubrication behavior under starved contact conditions remains insufficiently explored. The tribological performance of Ti6Al4V was investigated under starved boundary-to-mixed lubrication conditions using engine oil modified with Ag-doped TiO₂ nanoparticles. Double-scan LPBF-fabricated discs were tested in a ball-on-disc configuration against AISI 52100 bearing steel using a TRB³ tribometer. Nanolubricants were prepared by dispersing TiO₂ and Ag–TiO₂ nanopowders with different Ag⁺/Ti⁴⁺ ratios (0.5%, 1.5%, and 2.5%) in SAE 10W-40 engine oil at a constant nanoparticle concentration of 0.05 wt%. Comprehensive physicochemical characterization of the nanopowders and nanolubricants was performed through structural, chemical, optical, morphological, rheological, and stability analyses. Tribological experiments were conducted following a full-factorial design combining three normal loads (5–15 N), three sliding speeds (0.10–0.20 m·s⁻¹), and four lubricant formulations. The steady-state coefficient of friction ranged between 0.281 and 0.359, while the specific wear rate varied from 2.81 × 10⁻⁴ to 4.83 × 10⁻⁴ mm³·N⁻¹·m⁻¹. The contact temperature rise remained relatively moderate, within the interval of 1.9–9.4 °C. Among the investigated formulations, the lubricant containing 1.5% Ag–TiO₂ exhibited the lowest friction coefficient, whereas the formulation with the highest Ag content showed improved stability of tribological performance across the investigated operating domain. These results indicate that Ag-modified TiO₂ nanoparticles are consistent with the formation of protective tribofilms and contribute to the stabilization of friction, wear, and thermal behavior under starved lubrication conditions. ANOVA confirmed that sliding speed and the load–lubricant interaction are the dominant factors governing friction and wear, while normal load controls the thermal response. These findings support the use of Ag–TiO₂ nanolubricants as a viable strategy for stabilizing interfacial behavior in LPBF-fabricated titanium components operating under starved lubrication conditions. Full article

Classical eddy viscosity models add a viscosity term with a turbulent viscosity coefficient developed beginning with the Kolmogorov–Prandtl parameterization. Approximations of unknown accuracy of the unknown mixing lengths and turbulent kinetic energy are typically constructed by solving associated systems of nonlinear convection–diffusion-reaction equations with nonlinear boundary conditions. These often over-diffuse, so additional fixes are added such as wall laws, or different approximations are used in different regions (which must also be specified). Alternately, one can solve an ensemble of NSEs with perturbed data, compute the ensemble mean and fluctuation, and simply directly compute the turbulent viscosity parameterization. This idea is recent. From previous work it seems to be of a lower complexity and greater accuracy. It also produces parameterizations with the correct near-wall asymptotic behavior. The question then arises: Does this ensemble eddy viscosity approach over-diffuse solutions? This question is addressed herein. Full article

Freeze–thaw damage significantly reduces the performance and durability of airport pavements in cold regions. Traditional assessment methods, such as the F300 freeze–thaw test, are time-consuming and hinder rapid optimisation of mix design. In addition, previous studies have mostly relied on long-term laboratory testing and have evaluated phase-change concrete (PCC) independently, without considering synergistic effects. These approaches lack fast, synergy-aware predictive capability and interpretable tools for mix proportion design, resulting in a gap between laboratory research and practical engineering applications. To address this issue, this study proposes an intelligent and explainable framework for predicting freeze–thaw damage and guiding mix design of steel fibre-reinforced phase-change concrete (SF–PCC). A boundary-controlled experimental programme was first conducted, varying steel fibre (SF) content from 0 to 1.2% and phase-change material (PCM) content from 0 to 12% under fixed mixture conditions. The freeze–thaw test results were recorded sequentially and used to construct a supervised learning dataset. Then, an XGBoost model was developed to predict two key durability indicators: relative dynamic modulus of elasticity (RDEM) and mass loss. SHAP (SHapley Additive exPlanations) analysis was further applied to quantify feature importance and interaction effects. The model achieved high predictive accuracy (R² = 0.9938 for mass loss and R² = 0.9935 for RDEM) under controlled experimental conditions. After 300 freeze–thaw cycles, the reference mix exhibited an RDEM of 61.2%, while optimised configurations showed improved performance. The economical design (9% PCM + 0.9% SF) achieved an RDEM of 66.8%, and the high-performance design (12% PCM + 1.2% SF) reached 72.6%. These results demonstrate that the proposed framework can effectively enhance durability and support rapid preliminary decision-making. The framework significantly accelerates freeze–thaw performance evaluation by enabling near-instant prediction and serves as an efficient supplementary tool for mix design optimisation alongside conventional laboratory testing. It also provides interpretable, data-driven insights for the design of freeze–thaw-resistant airport pavement concrete in cold regions. Full article

This paper presents two complementary classes of analytical benchmark problems for the one-dimensional wave equation governing longitudinal vibration of a prismatic rod with mixed (clamped–free) boundary conditions. The first benchmark class consists of classical initial-value problems and includes both compatible and incompatible initial data at the space–time corners, highlighting their influence on convergence, regularity, and termwise differentiation of displacement, velocity, and axial force series representations. The second benchmark class prescribes the displacement at two time instants (initial and final time), leading to a fundamentally different modal structure and revealing spectral conditioning effects governed by the ratio $L/\left(c{t}_e\right)$. The derived closed-form solutions provide reference configurations for verification of transient numerical solvers, particularly in scenarios where classical smooth compatibility assumptions are not satisfied. Full article

Anaerobic digesters operating under neotropical conditions face significant technological constraints. High humidity, intense solar radiation, and pronounced diurnal temperature variations increase conductive, convective, and radiative heat losses. These factors reduce internal thermal stability and directly affect methane production rates and overall energy efficiency. As a result, thermal instability becomes a recurrent operational bottleneck in biogas plants without active temperature control. This review examines the thermal and dynamic behavior of anaerobic reactors from a process-engineering perspective. It integrates energy balances, heat-transfer mechanisms, and computational fluid dynamics (CFD) modeling. The combined effects of temperature gradients, hydrodynamic mixing patterns, and structural material properties are analyzed to determine their influence on thermal homogeneity, microbial stability, and methane yield consistency under mesophilic conditions. Technological strategies to mitigate thermal losses are evaluated. These include passive insulation using low-conductivity materials, geometry optimization supported by numerical modeling, and thermal recirculation schemes, as these factors govern temperature distribution and process resilience. Current limitations are also discussed, particularly the frequent decoupling between ADM1-based kinetic models and transient heat-transfer analysis. This separation restricts predictive capability under real-scale diurnal temperature oscillations. The development and validation of coupled hydrodynamic–thermal–biokinetic models under fluctuating neotropical boundary conditions are proposed as critical steps. Such integrated approaches can enhance operational stability, ensure consistent methane production, and improve energy self-sufficiency in organic waste valorization systems. Full article

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior under representative user-load conditions within controlled ambient thermal boundaries. The model combines system-level power profiling, thermal evolution, voltage dynamics, and aging-related capacity correction within a unified framework. To support model development and validation, a dual-source dataset is established using laboratory battery characterization data and real-world smartphone behavioral data, from which users are classified into light, heavy, and mixed usage patterns. Comparative results against four benchmark models (M1–M4) show that MTEC-SOC achieves the highest overall accuracy, with average MAE, RMSE, and TTE error values of 0.0091, 0.0118, and 0.08 h, respectively. The results suggest distinct degradation tendencies across user types: calendar aging dominates under prolonged high-voltage dwell in light-use scenarios, whereas, within the tested thermal range, heavy-use scenarios exhibit stronger voltage sag, relative temperature rise, and polarization-related stress; mixed-use scenarios are characterized by transient responses induced by abrupt load switching. Sensitivity analysis further indicates that the predictive behavior of the model is strongly scenario-dependent, with higher-load operation within the calibrated range amplifying parameter perturbations. Overall, the proposed MTEC-SOC framework provides accurate SOC estimation and physically interpretable insight within the evaluated dataset and operating conditions, offering potential guidance for battery management and energy optimization in intelligent mobile terminals. Full article

Transonic compressors encounter significant challenges from shock formations due to high-speed supersonic blade tips, particularly at high altitudes where lower Reynolds numbers result in laminar boundary layer separation and increased mixing losses. Understanding shock wave–boundary layer interaction (SBLI) is essential for improving compressor performance. This study examines SBLI under varying Reynolds numbers, simulating higher altitude conditions in a transonic blow-down wind tunnel. Using an inlet valve setup to control inflow total pressure and Reynolds numbers, this study also reveals an increase in turbulence. The findings indicate that laminar-to-turbulent transition occurs upstream of the shock wave, resulting in interaction with a turbulent boundary layer, even at lower Reynolds numbers. Full article

In this work, we study a one-dimensional coupled hyperbolic–parabolic system modeling the dynamics of swelling soils under thermodiffusion effects. The model describes the interaction between the deformation of the solid skeleton, the pore fluid motion, the temperature variation, and a diffusive process formulated through chemical potential. Under mixed boundary conditions and without introducing additional mechanical damping or imposing restrictive relations among the physical parameters, we prove exponential stability of the system. Our analysis is based on the energy method. In contrast to the standard energy functional commonly used in related thermodiffusion models, we introduce a modified positive energy functional better adapted to the coupled structure of the system. By combining this energy with suitable auxiliary functionals, we construct an appropriate Lyapunov functional and derive an exponential stability estimate. Our result shows that thermodiffusion alone yields sufficient dissipation for exponential stabilization, complementing earlier works where exponential stability requires extra damping mechanisms or equal wave-speed assumptions. Full article