Search Results (774)

This study evaluated how the ice dosage and water-to-binder (W/B) ratio affect the uniaxial response of roller-compacted concrete (RCC). Three levels of W/B, 0.40, 0.45, and 0.50, were examined. For every W/B level, five proportions of ice substitution, namely 0%, 25%, 50%, 75%, and 100%, were adopted in the mixing process, producing 15 distinct mixture categories. In each group, three prismatic specimens and three cubic specimens were fabricated for compression testing. Uniaxial compression experiments were conducted to determine the failure characteristics and full stress–strain behavior, whereas scanning electron microscopy (SEM) was used to analyze the internal microstructure. Based on the test data, a constitutive expression describing RCC under uniaxial compression was established. The experimental results showed that, within the investigated range, compressive strength displayed a rise-then-fall trend as the W/B ratio increased from 0.40 to 0.50, and the best overall performance was obtained at W/B = 0.45. Microscopic examination further confirmed that this ratio produced a denser and more integrated internal structure. In addition, the role of ice dosage in governing the mechanical response of RCC varied significantly with the W/B level. The outcomes of this study provide useful support for RCC mix proportion optimization and practical engineering design. Full article

The dynamic modulus of asphalt mixtures is a key design parameter in pavement design, which significantly impacts the mechanical properties of asphalt pavements. This study simulated dynamic modulus tests of asphalt mixtures using the three-dimensional (3D) discrete element method (DEM) to investigate mechanical behaviors such as the loading-bearing ratio of individual aggregates. Fine-grained AC-13 and medium-grained AC-20 asphalt mixture models were randomly constructed in the DEM program using user-defined methods. The dynamic modulus and phase angle values of the asphalt mixtures were predicted. By comparing laboratory experiments with DEM simulation results, the model was validated, and the effects of temperature and loading frequency on the dynamic modulus were explored. Further exploration was conducted on the loading-bearing ratio and mechanical interactions among aggregates of different sizes within the mixtures. The results show that the 3D DEM model can accurately predict the dynamic modulus and phase angle of asphalt mixtures. Temperature and frequency have an impact on these parameters, and the increase in gradation has an impact on the loading-bearing ratio, due to the proportion of coarse aggregates. Full article

To investigate the deterioration law of the mechanical properties of PVA-fiber-reinforced rubber concrete under the combined action of high-temperature and salt erosion, physical index tests, dynamic mechanical property experiments, and microstructural morphology observations were carried out on specimens subjected to different temperatures (ambient temperature, 100 °C, 300 °C) and various solution attacks (water, 5% NaCl, 5% Na₂SO₄, and 5% NaCl + 5% Na₂SO₄ mixture). The results show that, after exposure to 300 °C, the PVA fibers melt and the rubber pyrolyzes, since this temperature exceeds their melting points. A residual pore network is formed inside the matrix, and the damage degree of ultrasonic pulse velocity is about 2.3 times that of the 100 °C group. Although salt solution and its crystallization products can physically fill the pores and cause a partial recovery of pulse velocity, this change is mainly due to the alteration of the pore medium and does not represent a substantial restoration of the microstructure. The effects of different salt solutions on dynamic mechanical properties vary significantly: Sulfate erosion improves the dynamic performance significantly at ambient temperature by forming gypsum and ettringite to fill pores, but this strengthening effect disappears after 300 °C. Sodium chloride attack generates Friedel’s salt and consumes C₃A, leading to general strength deterioration. In composite salt erosion, the competitive and synergistic effects of Cl⁻ and SO₄²⁻ destabilize erosion products and weaken interfacial bonding, resulting in consistent decreases in dynamic compressive strength and elastic modulus under all temperatures and impact pressures. The strength reduction reaches 66.2% after 300 °C. Microscopic analysis confirms that composite salt erosion leads to the dissolution of ettringite and loose structure, which verifies the synergistic deterioration law of macroscopic properties. This study systematically reveals the damage evolution mechanism of PVA-fiber-reinforced rubber concrete under the coupled action of high-temperature and salt erosion, and provides a theoretical basis for the dynamic bearing capacity evaluation and durability design of concrete structures in such coupled environments. Full article

Pesticides constitute a critical class of anthropogenic contaminants whose pervasive occurrence in surface waters, groundwater, and drinking water distribution systems poses substantial ecological and public health risks. Their pronounced structural heterogeneity, spanning highly polar herbicides to hydrophobic fungicides, together with their co-occurrence at trace levels, requires analytical methodologies capable of delivering rapid, robust, and high-resolution separations. In this study, a UHPLC-based analytical strategy is presented as a methodological framework for the development and optimization of UHPLC methods targeting multiresidue pesticide mixtures of environmental interest. The framework integrates a two-factor, three-level Design of Experiments, quadratic response surface modeling, and a multicriteria global desirability function to optimize the chromatographic resolution of 27 environmentally relevant pesticides. Statistical modeling revealed significant linear and quadratic effects of flow rate and gradient duration, highlighting the importance of multivariate optimization for complex multiresidue separations. The optimized UHPLC conditions improved simultaneous resolution, particularly for structurally similar analytes prone to coelution under conventional HPLC conditions. Overall, this work provides a statistically supported and transferable methodology for chemometric optimization of UHPLC separations and establishes a basis for extending desirability-driven optimization to additional classes of organic contaminants. Full article

Graph Neural Networks (GNNs) have emerged as the standard for learning representations from graph-structured data. While traditional architectures relying on message-passing mechanisms excel in homophilic settings, they essentially function as fixed low-pass filters. However, this smoothing operation limits their ability to generalize to heterophilic graphs, where connected nodes often exhibit dissimilar labels and high-frequency signals are crucial for discrimination. Furthermore, existing Mixture-of-Experts (MoE) methods for graphs often suffer from local-view routing, failing to capture global structural context during expert selection. To address these challenges, this paper proposes SS-AdaMoE, a novel Spatio-Spectral Adaptive Mixture of Experts framework designed for robust node classification across diverse graph patterns. Specifically, a Dual-Domain Expert System is constructed, integrating heterogeneous spatial aggregators with learnable spectral filters based on Bernstein polynomials. This allows the model to adaptively capture arbitrary frequency responses—including high-pass and band-pass signals—which are overlooked by standard GNNs. To resolve the locality bias, a Hierarchical Global-Prior Gating Network augmented by a Linear Graph Transformer is introduced, ensuring that expert selection is guided by both local node features and global topological awareness. Extensive experiments are conducted on five benchmark datasets spanning both homophilic and heterophilic networks. The results demonstrate that SS-AdaMoE consistently outperforms baselines, achieving accuracy improvements of up to 2.65% on Chameleon and 1.41% on Roman-empire over the strongest MoE baseline, while surpassing traditional GCN architectures by margins exceeding 28% on heterophilic datasets such as Texas. These findings validate that the synergy of learnable spectral priors and global gating effectively bridges the gap between spatial aggregation and spectral filtering. Full article

Bundle recommendation has gained significant attention, but it faces two key challenges: sparse interaction data and complex UB, UI, and BI relations. Recent work uses multi-view contrastive learning, yet current frameworks rely on fixed-weight fusion that ignores view-specific importance and suffers from gradient suppression on sparse data. We propose TriadCBR, a tri-view adaptive contrastive learning architecture for bundle recommendation. It uses a simplified GCN to learn view-specific representations and a Mixture-of-Experts (MoE) module to generate personalized fusion weights, addressing the limitations of fixed-weight fusion. TriadCBR further incorporates a fine-grained contrastive module integrating InfoNCE, DCL, and Barlow Twins. This combination effectively mitigates gradient vanishing from invalid negatives and minimizes cross-view feature redundancy. To handle data sparsity, we design a Difficulty-Aware BPR (DA-BPR) with curriculum augmentation to dynamically refine the ranking trajectory. Extensive experiments on Youshu, iFashion, and NetEase demonstrate that TriadCBR achieves statistically significant improvements, boosting Recall and NDCG by an average of 3.61%, with 9 of 12 metric–dataset combinations reaching statistical significance, over state-of-the-art baselines, validating the robustness of its dynamic fusion and adaptive optimization. Full article

Micropropagated plantlets, after removal from controlled laboratory conditions, require an acclimatization period. Adaptation to the new environment induces anatomical and physiological changes controlled by cellular processes. This study investigated the involvement of the primary proton transport systems of total membranes in pineapple root colonization by diazotrophic bacteria and in the development of plantlets treated with different nitrogen doses, allowing an understanding of nutrient absorption and accumulation dynamics. The experiment followed a randomized block design (RBD) in a factorial scheme (2 × 3 × 2), with two inocula (a mixture of diazotrophic bacteria containing Burkholderia sp. UENF 114111, Burkholderia silvatlantica UENF 117111, and Herbaspirillum seropedicae HRC 54, and another without bacteria), three urea doses (0, 5, and 10 g L⁻¹), and two evaluation (90 and 150 days) and bacterial counting times (30 and 150 days), with three blocks. Diazotrophic bacterial populations were lower in older plantlets. H⁺ transport mediated by P H⁺-ATPases changed with acclimatization time. Inoculation did not induce transport; however, the F_max of V H⁺-ATPase was lower without nitrogen fertilization. Nitrogen fertilization affected V H⁺-ATPase proton transport activity in root membranes. The presence of diazotrophic bacteria did not induce proton transport. On the other hand, nitrogen fertilization and acclimatization time affected the proton transport activity mediated by H⁺-ATPases isolated from roots of micropropagated pineapple. Full article

Petroleum hydrocarbons remain major environmental contaminants, and understanding the mechanisms governing their biodegradation is essential for designing effective remediation plans. The strategy in this article is slightly different from other cases in the literature. Such literature models require, for their elaboration, a significant number of experiments; the number of experimental determinations is at least proportional to the square of the number of constants introduced in the mathematical expressions. For this reason, the strategy followed in this article is different—starting from a set of experiments carried out and presented in a coherent and published manner, a simple methodology for building specific and minimal models, which will allow solving specific problems, was effectively developed. This study develops a nonlinear mathematical structure, expressed as a system of coupled differential equations, that simultaneously describes the degradation of petroleum hydrocarbons and the dynamics of hydrocarbon-degrading bacteria and fungi in soil–sludge mixtures. The model was calibrated using experimental data obtained from biopiles prepared with different volumetric ratios of contaminated soil and sewage sludge. Approximate analytical solutions were derived and the distributed constants were evaluated. For a consistent discussion, the analytical solutions were assessed against numerical desk simulations performed with a classical fourth-order Runge–Kutta method, which accurately reproduced the nonlinear behavior of the specific system. This numerical approach was chosen in order to overcome the proper difficulties encountered in this strategy implementation. The results show that the soil–sludge ratio strongly influences biodegradation efficiency, while kinetic parameters determine whether microbial communities evolve toward a stationary regime or accelerated contaminant removal. The combined analytical–numerical framework provides a robust predictive tool for optimizing mixture composition and improving the design of bioremediation treatments for petroleum-contaminated soils. Full article

The use of resource-saving technology in road construction material production is a current problem, the solution of which will allow us to increase the environmental and economic efficiency of the road construction industry. Nowadays, secondary raw materials are widely used in highway construction, obtained both from the waste of old road construction materials and collected from other industries. During asphalt production, up to 90% of raw materials can be replaced by reclaimed asphalt pavement (RAP). This technology requires residual binder modification to reduce the negative impact on the technological and operational asphalt concrete properties. On the other hand, the use of rubber crumbs or granules obtained from the disposal of old car tires in asphalt–concrete mixtures is widespread. However, some types of car tires cannot be used as raw materials to produce an effective modifier. Truck tires and tires from special vehicles are suitable for use as a modifier for asphalt–concrete mixtures. Tires designed for passenger cars do not contain enough polymer. As an experiment on asphalt–concrete mixture production using secondary resources only, a testing facility was developed. The testing facility uses hot gas obtained by burning automobile tires in a special oven as a heat source. Rubber residues from the recycling of automobile tires are used as fuel, which cannot be used to produce rubber powder or granules. RAP obtained by cold milling of the pavements of city and public roads was used as the object of the research. When studying the characteristics of the asphalt–concrete-mixture-based binder, it was found that the sulfur compounds present in the composition of hot gases change the properties of the binder, leading to a serious deterioration in the technological characteristics of asphalt–concrete mixtures. The asphalt–concrete mixture obtained during RAP processing is characterized by a narrow temperature range in which it can be laid and compacted to the required density values. After laying the pavement, quality control revealed a significant variation (the number of air voids ranged from 0.8 to 5.5%) in the average density of samples taken from the compacted layer. In addition, there were significant violations of the longitudinal evenness of the finished coating. Experiments were carried out to extract the binder from asphalt–concrete mixtures before and after regeneration. The physico-mechanical and rheological characteristics were studied and qualitative analysis of the binder was realized by IR spectroscopy. The data obtained allow us to establish the mechanism of how sulfur-containing gases influence the bitumen binder’s properties in asphalt mixtures. Additionally, the features of thermo-oxidative degradation occurring during the hot recycling of asphalt–concrete mixtures were established. A justification is also given for the need to use anti-aging modifiers to restore the properties of the residual binder. Full article

Rollpave technology offers an efficient and low-disruption solution for pavement rehabilitation but has not yet been widely implemented in practice. This review aims to provide a comprehensive overview of rollpave technology by examining performance evaluation methods, material design strategies, and construction workflows, and identifying its advantages and limitations to support practical application. Recent advances in rollpave pavement technology are reviewed, including flexural performance testing methods and evaluation criteria for rollable pavement materials, as well as the design of flexible asphalt mixtures and interlayer bonding materials. Construction techniques across different stages of rollpave implementation are summarized, and existing engineering case studies are reviewed. The advantages and limitations of rollpave technology are evaluated in comparison with other pavement construction and rehabilitation approaches, and current research focuses are discussed. The review indicates that pavement performance requirements can be achieved through the development of specialized modified asphalt binders and optimized mixture designs. On-site installation relies on coordinated operation of multiple devices to ensure adequate interfacial bonding between new and existing layers; however, current practices are largely experience-based and lack standardized guidelines. It is believed that rollpave technology demonstrates unique advantages for rapid pavement repair and emergency rehabilitation, but there are still challenges related to material and structural design, on-site installation, and cost-effectiveness that remain, limiting large-scale adoption. Future research could focus on establishing technical standards, developing specialized equipment, and enhancing multifunctional integration. Full article

Performance deterioration and unstable operation are common when multistage centrifugal pumps handle gas–liquid mixtures. Here, we investigate a two-stage centrifugal pump over a wide speed range and inlet gas volume fractions (IGVFs) using experiments and CFD. The two-phase flow is simulated with a Eulerian–Eulerian two-fluid approach (liquid as the continuous phase; gas as a dispersed bubbly phase with a representative bubble diameter of 0.3 mm). Turbulence is closed using the SST k–ω model for the liquid phase and the built-in dispersed-phase turbulence treatment in ANSYS CFX. Transient pressure signals are analyzed in the time and frequency domains (FFT) to assess how rotational speed affects void-fraction distribution, overall performance, and the dominant unsteady components within the adopted modeling framework. The results show that IGVF primarily controls gas accumulation in the impeller passages: as IGVF increases, the gas phase evolves from dispersed bubbles to a central core, whereas speed mainly alters the detailed morphology via centrifugal effects. Similarity-law scaling is strongly speed-dependent in this pump: agreement is better at higher speeds and deteriorates at lower speeds where viscous effects become more influential. The dominant unsteady content also changes with speed, shifting from low-speed broadband features associated with gas redistribution to high-speed periodic components linked to blade–vane rotor–stator interaction (RSI). In addition, the downstream stage exhibits more uniform void fraction and more regular periodic signatures, consistent with an inter-stage flow-rectification effect. These observations provide practical guidance for hydraulic design and variable-speed operation of multistage pumps under gas entrainment. Full article

The separation of xenon (Xe) and krypton (Kr) is very important for industrial applications and environmental protection. However, the lack of permanent dipoles, low polarizabilities arising from their spherical nature, and similar kinetic diameters make their efficient separation by porous adsorbents exceptionally challenging. This study explored the effects of pore geometry and surface polarity of a series of aluminum-based metal–organic frameworks (CAU-10-H, MIL-160, KMF-1, CAU-23) on Xe/Kr separation performance using a heteroatom engineering strategy. These MOFs are composed of AlO₆ clusters and bent dicarboxylic acid linkers, enabling us to systematically investigate the effects of pore size and heteroatom types on Xe/Kr separation performance. Among them, MIL-160 has a polar linker based on furan, showing the best balance performance. At 298 K and 1.0 bar, the uptake of Xe is 4.12 mmol g⁻¹ and the IAST selectivity is 7.63 for a Xe/Kr (20/80) mixture. The practical performance was verified by dynamic breakthrough experiments, which yielded a long Xe breakthrough time of 42.9 min g⁻¹. Grand Canonical Monte Carlo (GCMC) simulations and first-principles density functional theory (DFT) calculations revealed that the enhanced performance originates from cooperative confinement and polarization effects, with the furanyl oxygen atoms providing optimal Xe-binding sites. This work clarifies the structure–property relationships governing Xe/Kr separation in aluminum-based MOFs (Al-MOFs), highlighting the potential of heteroatom engineering for designing efficient noble gas adsorbents. Full article

To address the challenge that existing vehicle chassis coordinated control methods struggle to balance the nonlinear couplings and control conflicts among Four-Wheel Steering (4WS), Direct Yaw-moment Control (DYC), and Active Suspension Systems (ASS), this paper proposes a Cooperative Distributed Model Predictive Control (Co-DMPC) strategy. First, the 4WS, DYC, and ASS are modeled as three interacting agents that effectively mitigate inter-subsystem control conflicts through information exchange and coupling compensation. Second, a Gaussian Mixture Model (GMM) is utilized to extract features from vehicle state data to enable the real-time grading of instability risks, which dynamically adjusts the control weights of the 4WS, DYC, and ASS agents. Finally, a distributed iterative optimization algorithm is designed to ensure that all agents converge to a global Pareto-optimal solution through rapid negotiation, achieving a balance between control performance and computational burden. Simulation results demonstrate that compared with No-Control and CMPC, the proposed Co-DMPC strategy significantly enhances the comprehensive performance of the vehicle. In terms of path tracking accuracy, the maximum tracking errors under high- and low-adhesion road conditions are reduced by 32.73% and 17%, respectively. Regarding roll stability, the peak roll angles of the vehicle are 0.27 rad and 0.01 rad under the respective conditions. For lateral stability, the proposed method maintains a more compact sideslip angle-yaw rate phase plane envelope, effectively achieving the coordinated optimization of chassis subsystems. Hardware-in-the-Loop (HIL) experiments further validate the performance and effectiveness of the controller. Full article

As a multiple-energy carrier, hydrogen can facilitate the transition to a low-carbon future, and coupling renewable energy sources with hydrogen-power generation systems (e.g., gas turbines) can markedly enhance gas turbine combined cycles (GTCCs) power generation regarding cleanliness and flexibility. Conventional gas turbines fuel the natural gas–hydrogen mixture and encounter issues like unstable combustion and elevated nitrogen oxide (NO_x) emissions. Initially, the alterations in combustion characteristics resulting from the fuel transition are analyzed, and the principal technical challenges of hydrogen-mixed combustion are summarized. It is found that hydrogen exhibits a laminar flame speed approximately 7–10 times higher than that of methane, and a hydrogen blending ratio beyond 30% significantly increases the risk of flashback and thermoacoustic oscillations. The existing technical proficiencies of advanced hydrogen combustion strategies are delineated to offer decision-making assistance for the industry. For instance, micromix combustors can achieve NOx emissions below 20 ppm even with 100% hydrogen, while axial staging technology expands the stable operating range to 25–106% load. Additionally, current research on hydrogen-fueled gas turbines primarily focuses on enhancing traditional combustor designs. Conversely, the focus on the overall alteration of gas turbines has been relatively restricted. It further examines component failure issues arising from elevated temperatures and material hydrogen embrittlement, highlighting that X80 pipeline steel experiences a 17-fold increase in hydrogen embrittlement index when the hydrogen blending ratio rises from 1% to 20%, as well as safety concerns related to fuel transitions from conventional gas turbines to hydrogen gas turbines, offering technical references for the comprehensive optimization of hydrogen-fueled gas turbines. Full article

Soil–steel interface shear governs load transfer and long-term serviceability in piles, retaining systems, and buried infrastructure; yet the large-displacement interface mechanics of fibre-reinforced sands remain poorly resolved, limiting sustainable design. This study couples large-displacement ring-shear testing with physics-guided hybrid AI to quantify and predict the peak and residual resistance of sand–polypropylene fibre mixtures sliding on smooth and rough steel. Two quartz sands with contrasting particle morphology were tested under 25–200 kPa normal stress and 0–1.0% fibre content, producing a design-oriented database that captures post-peak evolution and residual states. The experiments reveal a strongly nonlinear reinforcement law: an optimum fibre range enhances dilation, stabilises the shear band, suppresses post-peak softening, and increases residual strength, whereas excessive fibres disrupt the granular skeleton and reduce mobilisation efficiency. Roughness and confinement act as amplifiers, intensifying fibre-driven dilation and asperity interlock. To translate mechanisms into prediction, three strategies were benchmarked: a deep neural network (DNN), the Physics-Guided Neural Additive Model (PG-NAM++), and the physics-anchored Residual-DNN that learns only the correction to a mechanical baseline. Residual-DNN achieved the tightest agreement and the highest physical consistency for both peak and residual strength, enabling robust parameter selection with reduced uncertainty and overdesign. The combined experimental–AI framework advances the United Nations Sustainable Development Goals (SDGs) by supporting SDG 9 through resilient, innovation-led infrastructure design and contributing to SDG 12 by enabling optimised (rather than maximal) use and reuse of reinforcement materials within circular ground-improvement practice. Full article