Search Results (4,705)

This study addresses a key limitation in meso-scale discrete element modeling (DEM) of ultra-high performance concrete (UHPC). Most existing DEM frameworks rely on extensive macroscopic calibration and do not provide a clear, transferable pathway to derive contact law parameters from measurable micro-scale properties, limiting reproducibility and physical interpretability. To bridge this gap, we develop and validate a micro-indentation-informed, poromechanics-consistent calibration framework that links UHPC phase-level micromechanical measurements to a flat-joint DEM contact model for predicting uniaxial compression, direct tension, and flexural response. Elastic moduli and Poisson’s ratios of the constituent phases are obtained from micro-indentation and homogenization relations, while cohesion (c) and friction angle (α) are inferred through a statistical treatment of the indentation modulus and hardness distributions. The tensile strength limit (σₜ) is identified by matching the simulated flexural stress–strain peak and post-peak trends using a parametric set of (c, α, σₜ) combinations. The resulting DEM model reproduces the measured UHPC responses with strong agreement, capturing (i) compressive stress–strain response, (ii) flexural stress–strain response, and (iii) tensile stress–strain response, while also recovering the experimentally observed failure modes and damage localization patterns. These results demonstrate that physically grounded micro-scale measurements can be systematically upscaled to meso-scale DEM parameters, providing a more efficient and interpretable route for simulating UHPC and other porous cementitious composites from indentation-based inputs. Full article

Solar-aided power generation offers a pathway to reduce the carbon dioxide emissions from existing coal-fired plants. This study addresses the gap in comparing different solar integration modes by conducting a thermo-economic analysis of a 600 MW coal-fired system retrofitted with a solar tower. Four integration modes were designed and rigorously compared, encompassing series and parallel configurations at either the high-exergy reheater or the lower-exergy economizer. A detailed thermodynamic model was developed to simulate its off-design and annual performance. The results showed that integration at the primary reheater outperformed the economizer integration. Specifically, the parallel configuration at the primary reheater (Mode II) achieved the highest annual solar-to-electricity efficiency of 18.43% at a thermodynamically optimal heliostat field area of 125,025.6 m². Economic analysis revealed a trade-off, with the minimum levelized cost of energy (LCOE) of −0.00929 USD/kWh for Mode II occurring at the economically optimal area of 321,494 m² due to greater coal and emission savings. Sensitivity analysis across two other locations confirmed that the annual solar-to-electricity efficiency and LCOE are directly influenced by solar resource quality, but the thermodynamically optimal and economically optimal heliostat field area remain consistent. This work demonstrates that parallel integration with the primary reheater presents a favorable and practical configuration, balancing high solar-to-electricity conversion efficiency with favorable economics for hybrid solar–coal power plants. Full article

In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with low power consumption. The present work demonstrates the process of design, implementation and experimental evaluation of a single-cell lithium-ion battery monitoring prototype, intended for standalone operation or integration into other systems. The architecture is compact and energy efficient, with a reduction in complexity and memory usage: modular architecture with clearly distinguished responsibilities, avoidance of unnecessary dynamic memory allocations, centralized error handling, and a low-power policy through the usage of deep sleep mode. The data is stored in a cloud platform, while minimal storage is used locally. The developed system combines the functional requirements for an embedded external battery monitoring system: local voltage and current measurement, approximate estimation of the State of Charge (SoC) using a look-up table (LUT) based on the discharge characteristic, and visualization on a monochrome OLED display. The conducted experiments demonstrate the typical U(t) curve and the triggering of the indicator at low charge levels (LOW − SoC ≤ 20% and CRITICAL − SoC ≤ 5%) in real-world conditions and the absence of unwanted switching of the state near the voltage thresholds. Full article

To investigate the complex interaction in multi-structure systems, this study establishes a refined 3D numerical model based on a transportation hub project in Tianjin to analyze the asymmetric coupling deformation mechanism of a deep excavation adjacent to a shared-wall metro station and elevated bridge piles. This study highlights the transition from soil-mediated interaction mechanisms to those dominated by structures under shared-wall constraints. Results show that the existing station acts as a high-stiffness boundary, effectively suppressing lateral-wall deflection and basal heave on the proximal side. A critical finding is the reversal of the station’s deformation mode: while stations with a soil buffer typically tilt toward the excavation, the shared-wall station exhibits a clockwise rotation away from the excavation; this phenomenon is driven by excavation-induced basal rebound directly transferred through the common diaphragm wall. Furthermore, the station exerts a significant “shielding effect” on adjacent bridge piles, shifting their maximum lateral displacement from the pile head to the toe and reducing overall deformation. Parametric analyses reveal that optimizing shared-wall thickness is more effective for controlling lateral deformation, whereas increasing wall depth primarily mediates vertical heave. This study concludes that, for shared-wall systems, design priorities must shift from settlement control to anti-heave measures, and pile monitoring should extend to the deeper critical zones identified in this study. Full article

The paper presents the development and preliminary evaluation of a two-dimensional (2-D) network of magnetometers for magnetic anomaly detection. The configuration significantly improves over the existing one-dimensional (1-D) architecture, as it enhances the spatial characterization of magnetic anomalies through the simultaneous acquisition of data over an extended area. This leads to a reliable estimation of the target motion parameters. Each sensor node in the network includes a custom-designed electronic system, integrating a biaxial fluxgate magnetometer that operates in null mode. Deep learning models process the raw measurements collected by the magnetometers and extract structured information that enables both automated detection and preliminary target tracking. In the experimental evaluation, a $5\times 5$ array of nodes was deployed over a $12\times 12$ m² area for terrestrial tests, using moving ferromagnetic cylinders as targets. The results confirmed the feasibility of the 2-D configuration and supported its integration into intelligent, real-time surveillance systems for security and underwater monitoring applications. Full article

Multi-branch deep neural networks like AASIST3 achieve state-of-the-art comparable performance in audio anti-spoofing, yet their internal decision dynamics remain opaque compared to traditional input-level saliency methods. While existing interpretability efforts largely focus on visualizing input artifacts, the way individual architectural branches cooperate or compete under different spoofing attacks is not well characterized. This paper develops a framework for interpreting AASIST3 at the component level. Intermediate activations from fourteen branches and global attention modules are modeled with covariance operators whose leading eigenvalues form low-dimensional spectral signatures. These signatures train a CatBoost meta-classifier to generate TreeSHAP-based branch attributions, which we convert into normalized contribution shares and confidence scores ($C_b$) to quantify the model’s operational strategy. By analyzing 13 spoofing attacks from the ASVspoof 2019 benchmark, we identify four operational archetypes—ranging from “Effective Specialization” (e.g., A09, Equal Error Rate (EER) 0.04%, $C=1.56$) to “Ineffective Consensus” (e.g., A08, EER 3.14%, $C=0.33$). Crucially, our analysis exposes a “Flawed Specialization” mode where the model places high confidence in an incorrect branch, leading to severe performance degradation for attacks A17 and A18 (EER 14.26% and 28.63%, respectively). These quantitative findings link internal architectural strategy directly to empirical reliability, highlighting specific structural dependencies that standard performance metrics overlook. Full article

Timber has gained popularity in the construction industry in recent years due to its low carbon footprint, favorable seismic performance, and esthetic appeal. However, due to the size limit and inevitable natural defects such as knots in the lumber, the axial capacity of timber columns might be insufficient. Therefore, wrapping the timber column with basalt fiber-reinforced polymers (BFRPs), which is an environmentally sustainable material, to improve the load-carrying capacity has been a promising technology. While existing research mostly focuses on defect-free specimens, this study investigates the effects of knots on the structural performance of timber columns wrapped by BFRP. Axial compressive tests were carried out on timber columns, i.e., Douglas fir (knot-free) and camphor pine (with knots), wrapped by BFRP. The results showed that the load-carrying capacity, stiffness, and ductility can be significantly enhanced by the BFRP wrapping. The failure mode of the Douglas fir specimens transitioned from timber crushing failure to shear failure, while the camphor pine specimens failed around the knot area, and the failure mode changed from overall bending to BFRP rupture when the three layers of BFRP were employed. Furthermore, compared to knot-free columns, those specimens containing knots exhibited greater variability in load capacity and recorded a higher percentage increase in strength after reinforcement by BFRP. Based on the test results, three prediction models of the compressive strength of the BFRP-wrapped Douglas fir and camphor pine columns are presented. Full article

The whole-angle hemispherical resonator gyroscope (WA-HRG) is critical to high-precision attitude control and navigational positioning, boasting significant deployment potential in both highly dynamic inertial navigation systems and industrial instrumentation. This paper presents a mechanistic analysis of quantization error inherent to the HRG’s hardware detection and driving circuits, focusing specifically on its impact on parameter calculation and driving control in whole-angle mode. Furthermore, a simulation platform was constructed to verify and elucidate the correlations between the effects of quantization error and key resonator parameters, such as the major axis amplitude and the standing wave azimuth. Compared to existing HRG error studies which frame quantization error as isolated circuit noise, this work uniquely uncovers the azimuth-modulated periodic behavior of quantization error within the WA-HRG. It also formalizes a quantitative relationship between quantization error and the resonator’s key parameters, laying a critical theoretical foundation for suppressing quantization error and enhancing accuracy in high-performance WA-HRGs. Full article

The highly squinted mode, as an operational configuration of synthetic aperture radar (SAR), fulfills specific remote sensing demands. Under equivalent conditions, it necessitates a higher pulse repetition frequency (PRF) than the side-looking mode but produces inferior imaging quality, thereby constraining its widespread application. By applying the sparse SAR imaging method to highly squinted SAR systems, imaging quality can be enhanced while simultaneously reducing PRF requirements and expanding swath. Hyperparameters in sparse SAR imaging critically influence reconstruction quality and computational efficiency, making hyperparameter optimization (HPO) a persistent research focus. Inspired by HPO techniques in the deep unfolding network (DUN), we modified the iterative soft-thresholding algorithm (ISTA) employed in fast sparse SAR reconstruction based on approximate observation operators. Our adaptation enables adaptive regularization parameter tuning during iterations while accelerating convergence. To improve the robustness of this enhanced algorithm under realistic SAR echoes with noise, we integrated hypergradient descent (HD) to automatically adjust the ISTA step size after regularization parameter convergence, thereby mitigating overfitting. The proposed method, named Hyper-ISTA-GHD, adaptively selects regularization parameters and step sizes. It achieves high-precision, rapid imaging for highly squinted SAR. Owing to its training-free iterative minimization framework, this approach exhibits superior generalization capabilities compared to existing DUN methods and demonstrates broad applicability across diverse SAR imaging modes and scene characteristics. Simulations show that the hyperparameter selection and reconstruction results of the proposed method are almost consistent with the optimal values of traditional methods under different signal-to-noise ratios and sampling rates, but the time consumption is only one-tenth of that of traditional methods. Comparative experiments on the generalization performance with DUN show that the generalization performance of the proposed method is significantly better than DUN in extremely sparse scenarios. Full article

Defect modes in phononic crystals (PnCs) provide strongly localized resonances that are essential for frequency-dependent wave filtering and highly sensitive sensing. Their functionality increases greatly when their spectral characteristics can be externally tuned without altering the structural configuration. However, existing feedback control strategies rely on laminated piezoelectric defects, which have uniform electromechanical loading that causes voltage cancellation for even-symmetric defect modes. Consequently, only odd-symmetric defect bands can be manipulated effectively, which limits multi-band tunability. To overcome this constraint, we propose a segmented piezoelectric sensor–actuator design that enables symmetry-dependent feedback at the defect site. We develop a transfer-matrix analytical framework to incorporate complex-valued feedback gains directly into dispersion and transmission calculations. Analytical predictions demonstrate that real-valued feedback yields opposite stiffness modifications for odd- and even-symmetric modes. This enables the simultaneous tuning of both defect bands and induces an exceptional-point-like coalescence. In contrast, imaginary feedback preserves stiffness but modulates effective damping, generating a parity-dependent amplification-suppression response. The analytical results closely match those of fully coupled finite-element simulations, reducing computation time by more than two orders of magnitude. These findings demonstrate that segmentation-enabled feedback provides an efficient and scalable approach to tunable, multi-band, non-Hermitian wave control in piezoelectric PnCs. Full article

Dense and complex underground structures impose stringent requirements on shield tunneling. In the close-proximity construction of super-large-diameter shield tunnels, challenges may arise, including adverse impacts on the normal operation of existing structures, as well as difficulties in ensuring the bearing capacity and deformation control of these structures during excavation. This study, based on the stratigraphic conditions of the Chengdu area, employs FLAC3D 7.0 version software to simulate the section where the Shuanghua Road Tunnel underpasses both Metro Line 10 and the Chengdu-Guiyang High-Speed Railway. The main conclusions are as follows: (1) Tunnel underpassing induces uneven settlement in the metro tunnel, with a maximum settlement reaching 47.7 mm. The settlement trough exhibits a twin-peak morphology during dual-line construction. When a single super-large-diameter tunnel line crosses the existing structural cluster, the maximum settlement is located directly above the crossing point. During dual-line crossing, the maximum settlement shifts towards the midpoint between the two new tunnel lines. (2) As the left line of the new tunnel approaches the existing structure, the cross-sectional deformation of the existing structure is “pulled” towards the direction of the excavated new tunnel. After the new left line moves away, the cross-sectional deformation gradually recovers to a bilaterally symmetrical state. (3) The tunnel cross-section undergoes dynamic “compression-tension” convergence changes during the construction process, with a maximum longitudinal tensile convergence of −1.28 mm. (4) During the underpassing of the existing structural cluster by the super-large-diameter tunnel, the maximum torsion angle is approximately −0.016°, occurring at the moment when the shield machine head first passes directly beneath, located directly above the new tunnel. The torsion angle of the existing structure is greatest during the first underpassing event, and the maximum torsion angle during the second underpassing is lower than that during the first. This study reveals the composite deformation mode of “settlement-convergence-torsion” during the underpassing of existing structural clusters by super-large-diameter shield tunnels, providing a theoretical basis for risk control in similar adjacent engineering projects. Full article

To study the crack resistance of UHPC precast composite slabs, this paper conducts flexural performance tests on one UHPC monolithic slab and four UHPC precast composite slabs, investigating the influence of structural form, loading method, and shear reinforcement on the failure mode and crack resistance of UHPC precast composite slabs. The test results showed that UHPC precast composite slabs do not experience shear failure along the composite interface. They exhibit extensive microcracks and do not fail due to the immediate appearance of a single wide crack, demonstrating good plasticity and toughness. The cracking load of the monolithic slab is 6.6% to 12.5% higher than that of the composite slabs. However, the yield load and ultimate load of composite slabs equipped with shear reinforcement are 19.5% to 26.5% and 24.5% to 29.5% higher than those of the monolithic slab, respectively. These composite slabs are also characterized by extensive, dense microcracks with high quantity, small width, small spacing, short length, and dense distribution. Shear reinforcement can effectively improve the bearing capacity and crack resistance of UHPC precast composite slabs, with truss reinforcement showing a better effect in enhancing bearing capacity and inhibiting cracks. The comparison between positive and reverse loading methods better explains the “strain lag” of concrete and “stress advance” of reinforcement in composite slabs. Based on the section internal force equilibrium and the bond stress transfer principle between reinforcement and concrete, considering the enhancement effect of UHPC on bond stress, the calculation formulas for average crack spacing and maximum crack width in existing codes are modified. The calculated values are in good agreement with the test results. Full article

Existing high-voltage alternating current (AC) transmission line fault ranging methods have several drawbacks, including weak transition resistance, a complicated feature extraction process, and difficult calibration of the travelling wave head. To address these issues, a single-end fault ranging method for high-voltage AC transmission lines based on Attention-GRU and modulus amplitude ratio is proposed. Firstly, based on the travelling wave dispersion characteristics, an approximate formula is derived between the fault distance of the high-voltage AC transmission line and the amplitude ratio of the sum of the initial transient voltage travelling wave modes 1 and 2 and the mode 0 components at the ranging location. This shows that a definite nonlinear mapping relationship exists between the two. Secondly, the Attention-GRU is constructed using the multiscale wavelet modal maxima ratio between the sum of the initial transient voltage travelling wave mode 1 and 2 components and the mode 0 component as the input eigenquantities and the fault distance as the output quantity. The fault distance is then calculated using the Attention-GRU and the modal amplitude ratio. The Attention-GRU neural network fault ranging model is then constructed using the distance as the output quantity. After training is completed, the fault feature quantities obtained from the measurement points are inputted into the Attention-GRU model to achieve the purpose of fault ranging. The ranging ability of this model is then compared with that of other neural network models. A large number of simulations verify that the proposed method has high ranging accuracy and that the ranging capability is not affected by the fault type, transition resistance or the initial phase angle of the fault. Full article

Background: The World Health Organization and the Australian physical activity guidelines, in line with contemporary research, recommend regular muscle-strengthening activities for optimal muscular fitness in children and adolescents. However, the extent to which muscle-strengthening or muscular fitness receives curricular emphasis is unknown in Australia. Objectives: To examine to what extent the Australian Health and Physical Education Curriculum, Foundation to Year 10 (AHPEC; F–10) addresses and/or promotes muscular fitness. Methods: This study involved a mixed-methods content analysis of the AHPEC F–10 using: (i) conceptual analysis to identify muscular fitness-related terms; and (ii) relational analysis to examine alignment between muscular fitness content and curriculum rationale/aims. A search of national and international physical activity guidelines and school-based muscular fitness intervention literature generated a keyword set to guide abstraction from the AHPEC. Curriculum aim, rationale, level descriptions, achievement standards and content were coded to determine the extent to which muscular fitness was embedded. Intercoder reliability was established via consensus meetings. Muscular fitness content coverage was quantified as the proportion of directly aligned muscular fitness relevant content points per stage and aggregated primary (F–6), secondary (7–10), and F–10 scores. Results: A review of 32 national and one international physical activity guidelines identified 88 muscular fitness activities in total, with some activities appearing in multiple guidelines; 53.1% of national guidelines did not provide explicit muscular fitness examples, and where examples existed, they emphasised accessible modes (e.g., climbing, bodyweight tasks, jumping, and lifting). Additionally, analysis of school-based muscular fitness intervention literature identified 22 distinct muscular fitness activities to guide abstraction. Muscular fitness was absent in the AHPEC rationale and aims, was largely inferred in primary years level description and achievement standards and became more explicit in secondary achievement standards. Direct alignment of content with muscular fitness was non-existent or low across stages of learning (Foundation = 0%, Stage 1 = 0%, Stage 2 = 6.1%, Stage 3 = 9.1%, Stage 4 = 8.6%, Stage 5 = 8.8%). Overall, muscular fitness content coverage averaged 3.8% in primary, 8.7% in secondary, and 5.4% across F–10. Conclusions: The AHPEC treats muscular fitness as a low priority in primary schooling and a minor content area in secondary, yielding developmental messaging that is less aligned with contemporary evidence and physical activity guidelines. Full article

The widespread adoption of Electric Vehicles (EVs) is critically dependent on the deployment of efficient charging infrastructure. However, existing facility location models typically treat charging duration as an exogenous parameter, thereby neglecting the traveler’s autonomy to make trade-offs between service time and energy needs based on their Value of Time (VoT). This study addresses this theoretical gap by developing a heterogeneous network design model that endogenizes both charging mode selection and continuous charging duration decisions. A bi-objective optimization framework is formulated to minimize the weighted sum of infrastructure capital expenditure and users’ generalized travel costs. To ensure computational tractability for large-scale networks, an exact linearization technique is applied to reformulate the resulting Mixed-Integer Non-Linear Program (MINLP) into a Mixed-Integer Linear Program (MILP). Application of the model to the Hubei Province highway network reveals a convex Pareto frontier between investment and service quality, providing quantifiable guidance for budget allocation. Empirical results demonstrate that the marginal return on infrastructure investment diminishes rapidly. Specifically, a marginal budget increase from the minimum baseline yields disproportionately large reductions in system-wide dwell time, whereas capital allocation beyond a saturation point yields diminishing returns, offering negligible service gains. Furthermore, sensitivity analysis indicates an asymmetry in technological impact: while extended EV battery ranges significantly reduce user dwell times, they do not proportionally lower the capital required for the foundational infrastructure backbone. These findings suggest that robust infrastructure planning must be decoupled from anticipations of future battery breakthroughs and instead focus on optimizing facility heterogeneity to match evolving traffic flow densities. Full article