Search Results (3,793)

3D electrodeposition printing is an emerging process for fabricating metallic parts with controllable geometry, yet the coupled influences of electrochemical kinetics, ion transport, and tool motion on layer height remain difficult to interpret. This work presents a physics-based process model that links key process inputs, current density, electrolyte concentration, the inter-electrode gap, and tool scanning speed, to the resulting layer height in 3D electrodeposition printing of nickel-based structures. The model combines species transport in the inter-electrode gap with Butler–Volmer kinetics, under carefully stated assumptions regarding current efficiency, overpotential, and lateral spreading. Model predictions are validated against experimentally reported layer heights over a range of process conditions, yielding average errors (9–15%) and root-mean-square errors (0.13–0.28 µm) that demonstrate good agreement and highlight the impact of simplifying assumptions. Systematic parametric studies reveal how each process input monotonically influences layer height in ways consistent with Faraday’s law and diffusion-controlled growth, while also quantifying the relative sensitivity to different parameters. Building on these results, we introduce a dimensionless 3D Electrodeposition Printing Index that consolidates the key process and material parameters into a single scalar describing the geometric growth regime. The index enables construction of process maps that capture how combinations of current density, scan speed, concentration, and gap affect achievable layer height within the validated operating window. The scope and limitations of the proposed modeling framework and the index, particularly regarding other materials, more complex geometries, and pulsed or strongly convective regimes, are explicitly discussed, providing a basis for future model extensions and experimental validation. Full article

Multi-modal sentiment analysis (MSA) aims to accurately identify users’ emotional states by integrating textual, acoustic, and visual modalities. However, existing methods often suffer from insufficient cross-modal interaction, rigid fusion strategies, and limited sensitivity to subtle sentiment-level differences, which severely restrict model generalization and robustness. To address these issues, this paper proposes CrossSent, a multi-modal sentiment analysis framework that combines cross-modal attention with pairwise ranking regularization. Specifically, a Gated Multi-modal Residual Adapter (GMRA) is introduced to dynamically integrate heterogeneous features through gated residual connections, effectively mitigating modality asynchrony and noise interference. Meanwhile, a Monotonic Pairwise Ranking (MPR) regularization enhances discrimination among fine-grained sentiment levels. Furthermore, an Error-Interval Ordinal Inconsistency (EIOI) loss is designed to tolerate small prediction deviations, improving both stability and robustness. Experimental results on CMU-MOSI, CMU-MOSEI, and CH-SIMS demonstrate that CrossSent consistently surpasses state-of-the-art baselines across key metrics. For instance, it achieves 89.78% binary accuracy and 52.1% seven-class accuracy on CMU-MOSI, 87.72% and 54.7% on CMU-MOSEI, and 80.41%, 62.36%, and 43.54% for three- and five-level CH-SIMS tasks, with reduced mean absolute errors of 0.563, 0.513, and 0.408, respectively. We further report ordinal-consistency measures (QWK and level-jump statistics) to complement conventional metrics and quantify level-wise agreement. These results validate the effectiveness and generalization capability of the proposed framework. Full article

Field-scale nitrogen migration mechanisms in small watersheds remain poorly quantified due to insufficient representation of microtopographic heterogeneity. This study investigates nitrogen transport dynamics in a 1.27 km² agricultural watershed in China’s Jianghuai region using unmanned aerial vehicle (UAV) -derived 0.1 m digital elevation models (DEMs) and coupled hydrological–erosion modeling. The Soil Conservation Service Curve Number (SCS-CN) and Modified Universal Soil Loss Equation (MUSLE) models quantified nitrogen output loads, while the multi-flow direction algorithm simulated migration trajectories for total nitrogen (TN), ammonium, and nitrate. Results revealed strong spatial heterogeneity in nitrogen exports (watershed mean: 29.66 kg TN/km²·a), with bare land and greenhouses exhibiting the highest outputs (448.54 and 363.41 kg/km²·a) and forested areas showing minimal export (<6.1 kg/km²·a). Nitrogen migration was predominantly controlled by topographic gradients, with microtopographic features—field ridges, ditches, and buildings—physically redirecting flows and creating critical export nodes at field boundaries. DEM resolution critically affected simulation accuracy: erosion intensity displayed a non-monotonic response with an inflection point near 1 m resolution, corresponding to the median elevation difference (1.2 m) of field ridges. Structural equation modeling confirmed that high-resolution DEMs (0.1–2 m) maintained topographic control over nitrogen migration (~80% contribution), whereas 30 m DEMs reduced this influence to 30%, inducing spurious meteorological dominance. This study demonstrates that decimeter-scale DEMs are essential for accurately capturing microtopographic regulation of nitrogen transport, providing a methodological basis for precision management of agricultural non-point source pollution. Full article

This study investigates how outlet pressure influences the fire suppression performance of a compressed air foam system (CAFS), with the aim of supporting system optimization and engineering applications. An experimental apparatus for foam performance testing is used to measure changes in foam flow rate, expansion, initial velocity, initial momentum, and drainage time at different outlet pressures. On the basis of relevant theoretical models, the factors causing discrepancies between model predictions and experimental results are examined, and the models are then refined. How the outlet pressure of CAFS affects foam performance is thereby clarified. The results show that foam flow rate increases as outlet pressure increases. At higher pressures, shear-thinning and intensified gas–liquid mixing affect the foam. As a result, the growth of flow rate in the range of 0.01–0.03 MPa is significantly higher than that in the range of 0.06–0.10 MPa. Both initial velocity and initial momentum increase significantly with increasing pressure, whereas the expansion decreases. Within the outlet pressure range of 0.01–0.10 MPa, the initial velocity increases from 1.23 m/s to 6.65 m/s, the initial momentum rises from 4.6 kg·m/s to 34.1 kg·m/s, and the expansion decreases from 9.2 to 5.4, indicating reduced foam stability. Drainage time and drained mass vary non-monotonically with outlet pressure. The longest drainage time and the smallest drained mass occur at 0.06 MPa. Fire suppression performance improves as outlet pressure increases. A higher outlet pressure enables the foam solution to penetrate the flame zone more effectively and to cover the surface of the burning material. In addition, changes in foam properties enhance the thermal insulation and smothering effects of the foam layer, as well as its heat absorption and cooling capacity. These effects together improve the efficiency of fire source cooling. Full article

Sliding contact wear at the pantograph–catenary interface directly impacts the current collection performance and power supply reliability of electrified railways. Addressing the challenges in multi-environmental wear studies—namely, fragmented modeling chains, inconsistent parameter calibrations, and prohibitive computational costs that hinder horizontal comparisons—this study develops an equivalent parameterized modeling framework tailored for engineering assessment. The framework encapsulates environmental effects as equivalent load increments and interface coefficient corrections, facilitating efficient multi-scenario parameter scanning within a 3D contact model. Findings reveal that environmental factors drive wear through a distinct “pressure-wear” nonlinear decoupling mechanism. In sandy environments, abrasive-mediated micro-cutting dominates, leading to a monotonic surge in wear depth as sand concentration increases, despite a buffered contact pressure response. In icing conditions, the synergy of low-temperature brittleness and geometric impact renders hotspot wear highly sensitive to temperature fluctuations. For salt spray conditions, the environmental impact is represented via equivalent corrections to the interfacial parameters; within this equivalent framework, the results suggest that salt spray intensity has a more pronounced effect on wear accumulation than humidity alone. This work reveals the divergence of dominant damage pathways across environments, offering a quantitative basis for the differentiated maintenance and remaining life estimation of pantograph–catenary systems in extreme climates. Full article

The corrosion behavior of pipeline steels in marine environments is strongly affected by hydrodynamic conditions and microbial activity, yet their coupled influence remains insufficiently understood. In this study, the corrosion behavior of X70 pipeline steel was systematically investigated in flowing artificial seawater over a velocity range of 0–1.5 m/s, under both sterile conditions and in the presence of Pseudomonas aeruginosa. Corrosion weight loss measurements, electrochemical techniques, and surface characterization were employed to evaluate flow-dependent corrosion evolution. The results show that flow velocity plays a dominant role in regulating corrosion behavior. Under sterile conditions, increasing flow velocity enhances mass transfer and surface renewal, leading to progressively increased corrosion severity. In the presence of P. aeruginosa, corrosion behavior exhibits a non-monotonic dependence on flow velocity. Lower flow velocities are associated with reduced corrosion rates and relatively uniform surface degradation, whereas moderate flow velocities promote localized corrosion and increased pitting severity. At higher flow velocities, strong hydrodynamic effects suppress the retention of corrosion products and microbe-associated surface layers, resulting in corrosion behavior primarily controlled by fluid flow. Overall, the results indicate that microbial presence modifies the flow–corrosion relationship of X70 steel by altering interfacial conditions under low-to-moderate flow regimes. Full article

Global, industrialization-driven environmental bottlenecks push manufacturing enterprises toward green transitions; yet, the information asymmetry between central and local governments, and between enterprises and banks, hinders this process. Adopting a systemic–synergistic perspective integrating decentralized governance and green credit, in this study, we investigate the green transition decisions of manufacturing enterprises. We construct a quadrilateral evolutionary game model involving the central government, local governments, enterprises, and banks, employing MATLAB R2022b to simulate the effects of the key parameters. Subject to the model’s structural assumptions and parameter boundaries, three core findings emerge: first, we find that punitive environmental policies outperform incentive-based instruments in driving enterprise emission reduction; second, we find that the adaptive adjustments made by decentralized governance can effectively facilitate green practices among enterprises; third, within this framework, we find that green credit exerts a non-monotonic impact on enterprises’ green transition behaviors; meanwhile banks’ assessments of enterprises’ environmental risks can indirectly promote enterprise abatement by motivating local governments through signal transmission. This study underscores the systemic synergy of decentralized governance and green credit, offering insights for multistakeholder coordination and policy optimization to advance organizational sustainability transitions for the green economy. Full article

Underwater wireless power transfer (UWPT) operates under special conditions, where the conductivity of seawater introduces eddy current losses, thereby reducing system efficiency. Meanwhile, the design parameters of magnetic couplers significantly influence their transmission characteristics. This paper proposes a fast and accurate neural network prediction model for mutual inductance and losses of magnetic couplers based on mirror-method prior knowledge within a prior knowledge input (PKI) framework. The proposed model integrates a low-fidelity analytical model with data-driven learning to achieve high prediction accuracy while maintaining computational efficiency. Based on the developed model, the transmission characteristics of unipolar rectangular and bipolar DD magnetic couplers are systematically investigated. The results indicate that the rectangular couplers exhibit higher overall efficiency than the DD couplers, with a more monotonic variation in efficiency under design constraints. Owing to its structural characteristics, the DD couplers present an optimal current-carrying area ratio, which is approximately 0.85 within the parameter range. Experimental validation is conducted at a 1 kW power with outer dimensions of 200 mm × 250 mm. The optimal transfer efficiencies of the rectangular and DD couplers reach 97.33% and 96.19%, respectively. The experimental results show good agreement with both simulations and model predictions, demonstrating the reliability of the proposed method for UWPT magnetic coupler analysis. Full article

This paper develops a pessimistic two-stage network data envelopment analysis (DEA) model that integrates interval-valued data and endogenous weight restrictions within a unified linear programming framework. The proposed approach explicitly captures internal network structures while addressing bounded data uncertainty through an interval-to-deterministic transformation that preserves linearity and avoids probabilistic assumptions. Robustness is interpreted in the pessimistic interval DEA sense, where efficiency is evaluated under worst-case realizations of observed bounds rather than through explicit uncertainty-set optimization. To mitigate weight degeneracy and enhance discrimination power, data-driven proportional weight restrictions are introduced; these endogenous bounds are constructed solely from observed data and regularize the multiplier space without relying on subjective preferences or tuning parameters, while maintaining scale invariance and the nonparametric nature of DEA. The model admits equivalent multiplier and envelopment formulations and enables meaningful decomposition of overall efficiency into stage-specific components. Fundamental theoretical properties—including feasibility, boundedness, monotonicity, efficiency decomposition, and special case consistency—are rigorously established. An empirical application to OECD macroeconomic data, accompanied by sensitivity evaluation, demonstrates the stability and discriminatory capability of the proposed framework under bounded variability. Computational analysis confirms that the model retains linear programming structure and exhibits linear growth in problem size with respect to the number of decision-making units, thereby preserving the scalability characteristics of classical two-stage network DEA formulations. The proposed framework provides a theoretically grounded and computationally tractable approach for network efficiency analysis under bounded interval uncertainty. Full article

Goat (Capra hircus) welfare is an important issue in any farming system. The aim of the study was a comprehensive analysis of the impact of environmental factors and farming practices on the welfare of goats, with particular attention to physical, behavioural, and emotional aspects. It includes a review of the up-to-date literature on the effects of environmental conditions including air temperature, air humidity, space, feeding systems, social relationships (mother–offspring, human–animal, animal–animal), zootechnical procedures (dehorning, castration, hoof trimming) and welfare assessment methods. It compares the AWIN, Anzuino, Muri and Leite protocols for assessing goat welfare and their application in the Five Domain Model. Goat welfare is strongly influenced by their environment, nutrition and socialisation: heat stress and confined space cause physiological disorders, decreased immunity and increased aggressive behaviour and a monotonous diet leads to frustration and reduced cognitive activity, whereas positive early contact with humans reduces anxiety and maintaining the mother–kid bond supports the social development of young goats. Furthermore, significant improvements in welfare and stress reduction can be achieved by providing anaesthesia and painkillers where necessary to minimise pain and enriching the environment with items that support natural behaviour, such as platforms, brushes and items for cognitive tasks. In general, the keeper should take a holistic approach, combining environmental optimisation, humane husbandry practices and regular monitoring using validated assessment protocols to improve welfare. These measures are both an ethical obligation and a prerequisite for animal health and production efficiency. Nevertheless, there is a need for further research focussing on the development of non-invasive assessment methods and innovative forms of environmental enrichment. Full article

We show exactly when the topology of convergence in measure in Banach ideal spaces is linear (equivalently, coarser than the norm topology). Next, we present the relationship between the Kadets–Klee and suitable monotonicity properties with respect to global convergence in measure. Applying these results, we characterize the Kadets–Klee property with respect to the global convergence in measure in infinite direct sums. We also prove the criteria of some related monotonicity properties in infinite direct sums. Furthermore, we solve the fundamental lifting (inheritance) problem completely for all these properties. We finish the paper with concrete examples showing how our general results can be applied. Full article

In this study, first-principles calculations were employed to systematically investigate the adsorption of Cl⁻ on Al₂Cu(110) surfaces, clean Al(111)/Al₂Cu(110) interfaces, and Fe/Si-doped interfaces, as well as the influence of strain on interfacial electronic structure and corrosion activity. When Cl⁻ is adsorbed on Al sites, the bonding between Cl and Al exhibits strong ionic characteristics with localized charge transfer, while adsorption on Cu sites is characterized by more delocalized, covalent interactions. This competition dictates the site-dependent stability of adsorption. Through geometric–electronic synergy, the interface functions as both a “Cl⁻ enrichment zone” and an “activity source,” significantly favoring Cl⁻ adsorption at high-activity anodic sites such as Al-hole and Al-bridge. Conversely, Cu-top sites maintain a high work function and an inert cathodic nature, facilitating the formation of efficient micro-galvanic couples across the interface. Moreover, Fe/Si doping further modulates the interfacial electronic landscape: Si serves as an effective strengthening element due to its low substitution energy and high stability, while Fe primarily forms a solid solution on the Al side, potentially introducing galvanic corrosion risks. Stress analysis indicates that tensile strain systematically enhances surface activity by lowering the work function, while compressive strain non-monotonically influences corrosion through a three-stage mechanism involving the “densification–cracking–plastic relaxation” of the passive film. These findings elucidate the atomistic origins of corrosion initiation at Cu–Al composite interfaces and provide a theoretical foundation for enhancing corrosion resistance through alloy design and strain engineering. Full article

To address the challenge of identifying damage in the hangers and bridge deck systems of long-span suspension bridges, this paper proposes a non-contact monitoring method based on video image recognition. This method extracts structural vibration displacement responses through video acquisition and image analysis, and combined with the strain mode change rate index, it achieves damage localization, type identification, and severity assessment. The principle of extracting displacement time-history data from video images is first elaborated, and MATLAB-based computational code is developed, including pixel tracking and time-history curve generation methods. The eigensystem realization algorithm is used to identify displacement mode shapes, which are then converted into strain mode shapes via the central difference method. The strain mode change rate and its deviation rate are proposed as damage indicators: under undamaged conditions, the curve is smooth; at damage locations, peaks appear; the distribution range of peaks can distinguish between hanger damage and bridge deck cracks; the deviation rate quantifies damage severity. The feasibility of the method is validated through finite element simulations and physical model experiments. The results show that hanger damage causes broad peaks, while bridge deck cracks present narrow peaks; the deviation rate increases monotonically with damage severity. Applied to an in-service suspension bridge, the method successfully identified hanger bending and weld cracking, with assessment results consistent with on-site inspections. This study demonstrates that the strain mode change rate analysis based on video images enables damage identification without prior knowledge of the structural health state, relying solely on the damaged state response. Offering advantages such as non-contact measurement, full-field monitoring, and no need for sensor deployment, it provides a new technical approach for the long-term monitoring of suspension bridge hanger systems. Full article

We present a covariant geometric extension of General Relativity formulated within a controlled effective field theory framework. The gravitational action is supplemented by curvature-dependent operators parametrized by three coefficients $\alpha$, $\beta$, and $\gamma$, chosen such that the resulting field equations remain second order in time derivatives and free of Ostrogradsky instabilities. In a homogeneous and isotropic cosmological background, the modified dynamics generically replaces the classical Big Bang singularity with a smooth, nonsingular bounce driven by a repulsive curvature core proportional to $a^{-6}$. A distinctive feature of the framework is the presence of a geometric slip term proportional to $\dot{H}$, which encodes curvature-memory effects at the level of the background evolution without introducing additional propagating degrees of freedom. This term dynamically correlates the expansion rate with its temporal variation, leading to effective ultraviolet damping and enhanced dynamical stability across the high-curvature regime. As a consequence, the cosmological solutions admit the definition of an intrinsic relational time variable that is strictly monotonic throughout the evolution, including across the bounce. The emergent temporal ordering arises purely from geometric dynamics and does not rely on matter clocks, nonlocality, or fundamental violations of time-reversal or CPT symmetry. We discuss the consistency of the framework within its effective field theory domain of validity and comment on its implications for the conceptual problems of singularity resolution and the emergence of time in cosmology. Full article

This study investigates the axial–flexural behavior of steel fiber–reinforced concrete (SFRC) columns under combined constant axial load and monotonic lateral loading. Nine column specimens with different axial load ratios (0.0, 0.10, and 0.20) and steel fiber contents (0.0%, 0.5%, and 1.0%) were tested under monotonic loading to evaluate their failure modes, load–deflection behavior, ductility, and energy absorption capacity. In addition, a sectional P–M interaction analysis was performed to examine the influence of steel fiber inclusion on flexural strength under different axial compression levels. The interaction diagrams indicated that steel fibers expanded the flexural strength envelope, with a more pronounced enhancement in the low-axial-load region. The test results revealed that increasing the axial load ratio enhanced the specimens’ peak load capacity but reduced their ductility, leading to a brittle failure mode. Conversely, the incorporation of steel fiber improved the crack distribution, delayed crack propagation, and enhanced both ductility and energy absorption, particularly under moderate axial load conditions. The failure modes were characterized generally by flexural cracking and localized crushing in the compression zone, with the specimens that contained steel fiber exhibiting a more gradual post-peak load response than the specimens without steel fiber. The energy absorption capacity, quantified as the area under the load–deflection curve, was maximized when the axial load ratio of 0.10 was used in tandem with steel fiber reinforcement, indicating an optimal balance between strength and ductility. Overall, steel fiber inclusion improved deformation capacity and energy absorption under monotonic loading, particularly at low-to-moderate axial load ratios. Full article