Search Results (4,843)

The anchorage system at column bases plays a critical role in transferring forces between the superstructure and foundation in steel structure-reinforced concrete systems, thereby governing overall seismic performance. This study investigates the seismic behavior of reinforced concrete foundation columns using two anchorage systems: traditional foundation bolts (TFB) and friction-welded rebar anchor bolts (FRAB). A total of six full-scale specimens were tested under quasi-static cyclic loading to evaluate strength, deformation capacity, and failure mechanisms. The FRAB system integrates reinforcing bars with threaded rods through friction welding, aiming to enhance bond performance compared to conventional smooth anchor bolts. Test results indicate that specimens with FRAB exhibit improved seismic capacity and more stable hysteretic behavior than those with TFB. The enhanced performance is attributed to the superior bond characteristics of the welded reinforcing bars, which provide more effective force transfer between steel columns and concrete foundations. Full article

The growing need to mitigate climate change has accelerated the development of Carbon Capture, Utilization, and Storage (CCUS) technologies, where the safe transport of supercritical CO${}_2$ (sCO${}_2$) through pipelines is a key challenge. The flow behavior in such systems is strongly influenced by phase-change processes under transient conditions such as decompression and heat transfer and is further complicated by the presence of impurities (e.g., N${}_2$, CH${}_4$, and Ar). These impurities modify thermodynamic properties and phase boundaries, thereby affecting the overall flow dynamics. In this study, the influence of impurities on leakage, mass flow rate, and decompression wave propagation in sCO${}_2$ pipelines is investigated using computational fluid dynamics (CFD) simulations. A real-fluid model (RFM) implemented in the CONVERGE CFD solver is employed, with a tabulation-based approach to accurately capture thermodynamic and transport properties across multiphase regimes. The simulations were validated against available experimental data and performed for varying impurity concentrations to assess their impact on key flow variables, including pressure, temperature, and wave speed. Although simplifying assumptions were used, the results are in fairly good agreement with experimental observations and provide a better understanding of the phase behavior induced by impurities during transient decompression. Additionally, the effects of outlet geometry, pipeline configuration, and the choice of equation of state are examined, highlighting their influence on the predicted flow response. The validity of the RFM modeling framework is further demonstrated by simulations of a large-scale pipeline configuration representative of industrial conditions, which will serve as a benchmark for future improvements. Full article

Gypsum-based fire protection relies on thermally activated dehydration, where chemically bound water is released and evaporated, thereby providing an endothermic heat sink that delays heat penetration through assemblies. In parallel, inorganic hydrated salts are increasingly used as flame-retardant additives in gypsum-based systems to enhance heat absorption over specific temperature ranges. Fire simulation tools and performance-based fire engineering approaches require reliable kinetic data and reaction enthalpies that can be implemented as coupled thermal–chemical source terms. However, additive-specific kinetic datasets remain limited, particularly under restricted vapor exchange conditions representative of porous construction materials. This work investigates the thermal decomposition behavior and dehydration kinetics of Aluminum Trihydrate (Al(OH)₃, ATH), Magnesium Hydroxide (Mg(OH)₂, MDH), Calcium Aluminate Sulfate (3CaO·Al₂O₃·3CaSO₄·32H₂O, CAS), and Magnesium Sulfate Heptahydrate (MgSO₄·7H₂O, ESM) with emphasis on vapor-restricted conditions representative of confined porous systems. Differential scanning calorimetry (DSC) experiments were conducted at three heating rates (2, 10, and 20 K/min for MDH, CAS and ESM and 20, 40 and 60 K/min for GB-ATH) up to 600 °C using pinhole crucibles to simulate autogenous vapor pressure. The thermal analysis indicates that ATH and MDH exhibit predominantly single-step dehydration behavior, while ESM shows a complex multi-step mechanism. Although CAS presents a single dominant thermal peak in the DSC signal, the isoconversional analysis reveals a multi-stage reaction behavior, demonstrating that peak-based interpretation alone may be insufficient for such systems. Kinetic parameters were determined using both model-free (Starink) and model-fitting approaches in accordance with the recommendations of the Kinetics Committee of the International Confederation for Thermal Analysis and Calorimetry (ICTAC). All reactions were consistently described using the Avrami–Erofeev model as an effective phenomenological representation of the conversion behavior. The extracted kinetic triplets were validated through numerical simulations, showing good agreement with experimental conversion and reaction rate data. The resulting kinetic parameters and dehydration enthalpies provide a physically consistent dataset for the description of dehydration processes under restricted vapor exchange. These results support the development of thermochemical models for gypsum-based systems; however, their transferability to full-scale assemblies remains subject to validation under coupled heat- and mass-transfer conditions. Full article

Against the background of highly interconnected global capital markets and rapidly propagating cross-lingual information streams, traditional anomaly detection paradigms based solely on single-modality numerical time-series sensors are insufficient for forward-looking risk sensing. From the perspective of artificial intelligence-driven sensing, this study proposes a multilingual semantic–numerical collaborative Transformer framework to construct a unified multimodal financial sensing architecture for intelligent anomaly sensing and risk perception. Within the proposed sensing paradigm, multilingual texts are conceptualized as semantic sensors that continuously emit event-driven sensing signals, while market prices, trading volumes, and order book dynamics are modeled as heterogeneous numerical sensor streams reflecting behavioral market sensing responses. These heterogeneous sensors are jointly integrated through a cross-modal sensor fusion architecture. A cross-modal temporal alignment attention mechanism is designed to explicitly model dynamic lag structures between semantic sensing signals and numerical sensor responses, enabling temporally adaptive sensor-level alignment and fusion. To enhance sensing robustness, a multilingual semantic noise-robust encoding module is introduced to suppress unreliable textual sensor noise and stabilize cross-lingual semantic sensing representations. Furthermore, a semantic–numerical collaborative risk fusion module is constructed within a shared latent sensing space to achieve adaptive sensor contribution weighting and cross-sensor feature coupling, thereby improving anomaly sensing accuracy and robustness under complex multimodal sensing environments. Extensive experiments conducted on real-world multi-market financial sensing datasets demonstrate that the proposed artificial intelligence-driven sensing framework significantly outperforms representative statistical and deep learning baselines. The framework achieves a Precision of $0.852$, Recall of $0.781$, F1-score of $0.815$, and an AUC of $0.892$, while substantially improving early warning time in practical risk sensing scenarios. In cross-market transfer settings, the proposed sensing architecture maintains stable anomaly sensing performance under bidirectional domain shifts, with AUC consistently exceeding $0.86$, indicating strong structural generalization across heterogeneous sensing environments. Ablation analysis further verifies that temporal sensor alignment, semantic sensor denoising, and collaborative cross-sensor risk coupling contribute independently and synergistically to the overall sensing performance. Overall, this study establishes a scalable multimodal intelligent sensing framework for dynamic financial anomaly sensing, providing an effective artificial intelligence-driven sensing solution for cross-market risk surveillance and adaptive financial signal sensing. Full article

In multi-turn conversational AI, individually innocuous personally identifiable information (PII) fragments disclosed across successive turns can accumulate into a re-identification risk that no single utterance reveals on its own. Existing PII detectors operate on isolated utterances and therefore cannot track this cross-turn evidence build-up. We propose a stateful middleware guardrail whose core design principle is speaker-attributed entity isolation: every extracted PII fragment is attributed to its originating conversational participant, and evidence is accumulated in entity-isolated subgraphs that prevent cross-entity contamination. The system signals re-identification onset $t_{\mathrm{pred}}$ at the earliest turn where combination-based rules grounded in the uniqueness literature are satisfied. On a 184-record template-synthetic evaluation corpus, the gated NER configuration leads on primary timeliness (OW@5 $= 73.4\%$, MAE$= 1.357$ turns); the full system achieves OW@5 $= 70.7\%$ with MAE $= 2.442$ turns as an alternative operating mode for ambiguity-sensitive disclosure patterns. We further evaluate behavior on a 300-record mutation stress set, test RULE_B on the ABCD external corpus, and supplement RULE_A evaluation with both a proxy-labeled transfer analysis on PersonaChat and a manual annotation study on 151 Switchboard dialogues. The reported results should be interpreted as an initial empirical reference point rather than a sufficient endpoint for autonomous runtime enforcement. Full article

Generative Artificial Intelligence (GenAI) is reshaping how students engage with learning both within and beyond traditional classroom settings. In a time when the development of transferable skills is essential for enabling students to thrive in varied and rapidly evolving environments, the potential of GenAI to enhance learning engagement remains insufficiently understood. Despite rising interest in interactive, personalised learning companions that enable deep engagement and ongoing skills development, scholarly research remains limited. This gap constrains effective institutional use of GenAI, reinforces black-box thinking, and restricts understanding of meaningful student engagement and skills acquisition. This paper investigates how a GenAI-enabled AI teacher supports student learning engagement, focusing on behavioral engagement as evidenced by learner interaction and participation patterns across diverse international higher education institutions. Using a combination of quantitative engagement metrics and qualitative learner reflections, the study examines how GenAI supports personalised learning, sustained interaction, autonomy, and cognitive engagement among students with varying educational backgrounds. The findings demonstrate that GenAI-based teaching systems can promote meaningful learning engagement, enhance motivation, and strengthen the development of transferable and employability skills. The study contributes empirical evidence to current debates on GenAI integration, teacher practices, and student engagement, offering implications for curriculum design and institutional adoption of GenAI-enabled learning tools. Full article

Resilient wheels are widely employed in metro vehicles to mitigate vibration and noise, in which rubber damping components play a critical role in load transmission and fatigue resistance. However, stress concentration and cyclic loading can significantly compromise their durability and service life. In this study, the structural optimization and fatigue life of rubber damping components in resilient wheels are systematically investigated based on finite element analysis and in-service metro operational data. A three-dimensional finite element model incorporating hyperelastic material behavior is developed to evaluate stress distributions under three representative conditions: press-fit assembly, straight-line operation, and curved-track operation. Based on the resulting stress fields, critical high-stress regions within the rubber component are identified and selected as targets for structural optimization. The Design of Experiments (DOE) methodology, integrated with the Isight 2022 optimization platform, is employed to determine the optimal geometric parameters that minimize the von Mises equivalent stress. Furthermore, a fatigue life prediction framework is established using actual metro service mileage data. Fatigue performance is assessed using Fe-safe 2022 software in conjunction with rubber fatigue crack propagation theory, and the results before and after optimization are systematically compared. This study demonstrates that stress concentrations in resilient wheel rubber damping components predominantly occur at fillet transition regions, governed by load transfer characteristics under press-fitting and service conditions. Through DOE-based structural optimization, the critical geometric parameters are effectively refined, leading to a significant reduction in stress levels in key regions. As a result, the proposed approach markedly improves fatigue performance, extending the minimum fatigue life from 1300 days to 24,322 days, thereby substantially enhancing the durability and reliability of the resilient wheel system. Full article

Background: Accurate digital impressions are crucial for the long-term success of implant-supported prostheses, with scan bodies playing a pivotal role in transferring the implant position into the virtual model. Recent work has focused on PEEK (polyether-etherketone) scan bodies because their optical behavior may facilitate intraoral scanning; however, the breadth and quality of supporting evidence remain unclear. Methods: This scoping review followed PRISMA-ScR reporting guidelines and was registered in the Open Science Framework (OSF; Registration DOI 10.17605/OSF.IO/CU3V8). Pub-Med/MEDLINE, Embase, and Scopus were searched through September 2025. Eligible designs included in vitro studies, randomized trials, observational studies, and technical reports evaluating PEEK scan bodies in implant dentistry. Screening and data extraction were performed in duplicate, and findings were synthesized descriptively. Results: The search identified 227 records, and after screening, 31 studies met the inclusion criteria. Most studies were in vitro, with limited clinical evidence, and only one prospective clinical study was identified. Outcomes commonly addressed trueness, precision, scan time, and handling. Comparators varied (e.g., titanium, resin; splinted vs. unsplinted), and the results on accuracy were heterogeneous, with deviations typically within clinically acceptable limits (<100 µm). Conclusions: PEEK scan bodies are applicable for digital implant impressions. Clinical data are sparse, though, and methods vary. Controlled clinical studies are necessary to confirm the accuracy, reliability, and indications of this approach compared to titanium ISBs. Full article

Steel–UHPC composite beams are widely used in bridge engineering due to their high strength, durability, and suitability for prefabricated construction. However, the mechanical performance of shear connectors in UHPC differs significantly, and the uniform use of a single connector type along the beam span may result in a mismatch between connector mechanical characteristics and regional force demands, leading to suboptimal force transfer and inefficient utilization of connector capacity along the beam span. While previous studies have mainly focused on the local behavior of individual connectors, a system-level design strategy considering regional force demands is still limited. This study proposes a system-level design method for combined shear connectors in steel–UHPC composite beams, in which headed stud connectors and trapezoidal composite dowel connectors are arranged according to bending moment distribution and interface shear demand, thereby integrating connector mechanical characteristics with the spatial variation in internal forces along the beam span. The design procedure includes shear span division, longitudinal interface shear calculation, and resistance verification of different connector types. The method is applied to a practical steel–UHPC composite beam in a long-span approach bridge. Results show that headed studs provide reliable uplift resistance and ductile behavior in negative bending regions, whereas composite dowel connectors are shown to be more suitable for shear-dominated positive bending regions due to their higher shear capacity and stiffness. The combined system ensures effective composite action under different stress states and reduces total connector steel consumption compared with a stud-only layout. The proposed approach advances connector design toward performance-oriented and system-level structural optimization, providing a practical framework for connector arrangement in steel–UHPC composite beams. 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

Accurate inverse kinematics for rehabilitation robotic arms remains challenging because of strong nonlinearity, multiple feasible joint configurations, and strict joint-limit constraints. Inspired by the cooperative construction, adaptive exploration, and collective information-sharing behaviors of beavers, this study develops an improved biomimetic beaver behavior optimizer (IBBO) for optimization-based inverse kinematics solving. In the proposed framework, biologically inspired cooperative search is translated into an engineering-oriented numerical strategy through four complementary mechanisms: a strict elitist replacement with rollback to preserve population fitness consistency, a momentum-inspired information transfer scheme to accumulate effective search directions, a lightweight memetic coordinate-wise local search to strengthen late-stage exploitation, and an adaptive builder–disturbance schedule to progressively shift the search from exploration to refinement. The optimization capability of IBBO is first evaluated on the CEC2017 benchmark suite, where it demonstrates competitive accuracy and robustness. It is then applied to inverse kinematics solving for representative rehabilitation robotic arms by minimizing pose errors under joint constraints. The experimental results show that IBBO can consistently generate feasible joint solutions with improved terminal pose accuracy and stable convergence compared with baseline metaheuristics. Beyond numerical improvement, this study provides a biomimetic optimization framework that transfers beaver-inspired cooperative behaviors into rehabilitation robotics, offering an effective computational approach for constrained inverse kinematics problems. Full article

Driven by the large-scale deployment of renewable electricity, water electrolysis has emerged as a leading pathway for high-efficiency hydrogen production. Under practical operating conditions, gas evolution and gas–liquid two-phase flow inside electrolyzers substantially reshape electrode interfacial states and the in-cell mass transfer environment and have been reported to cause performance losses on the order of 10–30% under unfavorable conditions. This review summarizes the evolution of electrode-generated bubbles during nucleation, growth, detachment, and coalescence, and consolidates the fundamental features of two-phase hydrodynamics and phase-distribution patterns in electrolyzer channels. Progress and limitations of major two-phase modeling approaches are then assessed with respect to their capability to resolve the relevant interfacial and transport processes. The impacts of gas evolution and two-phase flow on electrochemical performance, stability, and durability are subsequently discussed. Finally, recent advances in two-phase-flow management—through flow-field organization and structural design, as well as the introduction of external physical fields—are reviewed, together with experimental and diagnostic methods used to quantify bubble behavior and phase distributions. This review aims to provide a coherent understanding of the governing behaviors, research tools, and performance implications of gas evolution and two-phase flow in water electrolysis, and to inform electrode/transport-layer design, flow-field management, and the development of predictive numerical models. Full article

Digital urban–rural sharing of social public resources (SPRs) is important for improving resource allocation efficiency and narrowing urban–rural disparities. This study applies a tripartite evolutionary game framework to analyze the strategic interactions among the government sector, the sharing supply side, and the sharing demand side in the digital urban–rural SPR sharing process. A system dynamics (SD) model is further constructed to simulate the dynamic evolution of the system under different initial conditions and parameter settings. The results show that the system generally evolves along a path of government initiation, demand-side response, and supply-side follow-up. Higher collaborative benefits, lower resource transfer costs, stronger government credibility, and appropriately designed subsidies promote active sharing and accelerate convergence toward a high-sharing stable outcome. In contrast, high transfer costs, weak collaborative incentives, and insufficient regulatory credibility inhibit sharing behavior or delay convergence. In addition, different initial cooperation levels mainly affect the convergence speed and fluctuation pattern of the evolutionary process. This study extends the application of the tripartite evolutionary game framework to the digital urban–rural SPR sharing context and combines it with SD simulation to reveal the system’s dynamic evolution mechanism. The findings provide practical implications for promoting digital urban–rural SPR sharing through moderate subsidies, reduced transfer costs, enhanced regulatory credibility, and strengthened collaborative mechanisms. Full article

Aluminum–graphene nanoplatelet (Al/GNP) composites have attracted significant attention as lightweight structural materials, yet their mechanical performance is strongly influenced by interfacial reactions and the formation of carbides. In this study, Al/GNP composites containing 0.1–1.1 wt.% graphene were produced via powder metallurgy and hot extrusion at 400 °C and 500 °C. Hot extrusion at the higher temperature enables the controlled in situ formation of aluminum carbide (Al₄C₃). A comprehensive microstructural characterization using SEM and HRTEM was combined with tensile testing to elucidate the influence of carbide size on mechanical behavior. Hot extrusion at 500 °C promotes the formation of uniformly distributed, nanoscale Al₄C₃ carbides whose size, morphology, and aspect ratio depend on graphene content. Composites containing nano-sized carbides exhibit a markedly improved strength–ductility balance compared to carbide-free counterparts, with optimal performance achieved at 0.3 and 0.7 wt.% GNPs. The enhancement is attributed to synergistic strengthening mechanisms involving improved interfacial bonding, efficient load transfer, nanoscale dispersion strengthening, and carbide–dislocation interactions. The results indicate that the controlled formation of nanoscale Al₄C₃ is not detrimental; rather, it contributes to the optimization of the mechanical properties of Al/GNP composites. Unlike most previous studies that treat carbide formation as a detrimental effect, this work demonstrates that its controlled nanoscale evolution can be used as a deliberate strengthening strategy through its influence on microstructural mechanisms. Full article

This study investigates the impact response and energy absorption performance of additively manufactured PLA–geopolymer hybrid composites for potential application in road safety barriers. Hybrid Charpy specimens were fabricated with three different infill densities (20%, 60%, and 100%), combining a 3D-printed PLA outer shell with a geopolymer core. Charpy impact tests were conducted in accordance with ISO 179-1 and ASTM D6110, and the absorbed energy, specific energy absorption, and mass efficiency were determined experimentally. A phase-based analytical model was also used to estimate elastic energy contributions, while fracture surfaces were examined to identify infill-dependent damage mechanisms. To extend the material-level findings to an engineering-scale application, the observed trends were transferred to a New Jersey-type road safety barrier model and evaluated using ANSYS Explicit Dynamics. The results showed that infill density strongly affects fracture behavior and energy dissipation performance, with 60% infill providing the most balanced response in terms of energy absorption and mass/material efficiency. The originality of the present study lies in going beyond a material-scale investigation of the impact behavior of additively manufactured PLA–geopolymer hybrid structures by integrally correlating the experimental Charpy results with a theoretical energy-based framework, fracture-surface observations, and explicit dynamic finite element analysis of a New Jersey-type road safety barrier model. Full article