Search Results (4,659)

Shafting load directly reflects shafting alignment quality and is critical to ship safety and reliability, yet remains difficult to measure directly in engineering practice. To address this, we propose a load identification and prediction method based on a Digital–Physical hybrid model. This approach integrates shafting load inversion with the time-series dependency characteristics of LSTM networks to construct an interpretable framework comprising physical, data, and decision layers. Modal testing calibrates the finite element model, while Tikhonov regularization addresses the ill-posed nature of frequency response function inversion. Additionally, a weight allocation strategy is designed during preprocessing to enhance training data quality. Validation experiments for load identification and prediction are conducted using an optimized dataset fused from measured and simulation data. Results show that, compared with purely physical or purely simulation-based models, the proposed hybrid model reduces prediction errors (RMSE, MAE, MSE) by 32–48.4% and increases the goodness of fit of prediction curves by 4%. This demonstrates superior predictive capability and interpretability, providing a new avenue for the monitoring of shafting conditions and load prediction in complex mechanical structures. Full article

Power systems with high renewable penetration impose multi-dimensional demands on energy storage (ES) regulation. Short-duration ES is required for power balance and frequency support, while medium- and long-duration ES is essential for daily, weekly, and seasonal peak shaving and energy time-shifting. Aiming at the challenge of multi-timescale configuration of hybrid energy storage (HES) in the initial planning stage of carbon-neutral transition, this paper proposes an optimal configuration strategy combining STL-PDM-VMD. First, the seasonal and trend decomposition using Loess (STL) is used to extract quarterly trends of annual net power for seasonal ES configuration. Then, the Past Decomposable Mixing (PDM) module in the time-mixer model is applied to decouple and mix multi-scale features of the detrended power curve for monthly and weekly configurations. Finally, an improved Variational Mode Decomposition (VMD) is adopted to decompose daily net power fluctuations and optimize intra-day energy storage schemes. Based on actual data from a carbon-neutral transition region, simulations are carried out and compared with the VMD method with decomposition layers optimized by Gurobi. The results show that the proposed STL-PDM-VMD multi-timescale hybrid energy storage configuration strategy can effectively capture the multi-timescale fluctuation characteristics of net load, significantly improve the Renewable Energy (RE) penetration rate, and ensure the power and energy balance of the new power system at multiple timescales. penetration, and maintain power and energy balance in the new-type power system. Full article

Obesity is characterized by low-grade systemic inflammation and alterations in gut-related immune pathways that may contribute to metabolic dysfunction. Composite biomarker indices may better capture these complex processes than individual markers, although their performance may differ across biological domains. In this cross-sectional study, 88 adults without diabetes or infection were categorized as BMI < 25 kg/m² (n = 20), BMI 25–29.9 kg/m² (n = 34), or BMI ≥ 30 kg/m² (n = 34). Circulating biomarkers reflecting systemic inflammation (high-sensitivity C-reactive protein, ferritin, interleukin-6, presepsin) and intestinal barrier-related activity (β-defensin-2, regenerating islet-derived protein 3 alpha) were measured and subsequently combined into two composite indices: the Inflammatory Load Index, derived from inflammatory markers, and the Barrier Activation Index, derived from barrier-related markers. Group differences were assessed using analysis of variance with post hoc testing. Additional analyses included effect size estimation, receiver operating characteristic (ROC) analysis, and logistic regression. Individual biomarkers showed limited differences across BMI categories. The Inflammatory Load Index differed significantly across BMI categories (p = 0.040), with higher values observed in individuals with BMI ≥ 30 kg/m² compared with those with BMI 25–29.9 kg/m² (p = 0.032; Cohen’s d = 0.80), while the Barrier Activation Index did not differ (p = 0.257). In ROC analysis, the Inflammatory Load Index discriminated BMI ≥ 30 kg/m² with an area under the curve of 0.720 (95% confidence interval 0.576–0.851), yielding 77.8% sensitivity and 67.7% specificity. Each one standard deviation increase in the index was associated with higher odds of obesity (odds ratio 2.34, 95% confidence interval 1.22–4.49; p = 0.011). In conclusion, a composite inflammatory biomarker index, but not a barrier-related index, differentiates degrees of BMI in individuals without diabetes. These findings support integrated biomarker approaches for reflecting obesity-related biological burden beyond single markers. However, these observations are based on cross-sectional data and do not imply causality. Full article

This study investigates the development of sustainable composite materials using recycled low-density polyethylene (rLDPE) and high-density polyethylene (rHDPE) in an 80/20 mass ratio, incorporating kraft lignin as a bio-derived additive and polyethylene-graft-maleic anhydride (PE-g-MA) as a compatibilizer. Reactive melt mixing was employed to produce composites with varying lignin loadings (1, 3, 5, and 10 wt%). The structural, thermal, and mechanical properties and segmental dynamics of the materials were thoroughly examined using differential scanning calorimetry (DSC), infrared spectroscopy (IR), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), thermogravimetric analysis (TGA), pyrolysis–gas chromatography/mass spectrometry (Py–GC/MS), tensile testing, scanning electron microscopy (SEM), and dielectric relaxation spectroscopy (DRS). The incorporation of lignin exhibited minimal disruption to the polymeric thermal transitions, while it boosted thermal stability, as confirmed by the TGA curves. According to the segmental dynamics findings, the glass transition temperature of the polymeric blend (−35 °C) was increased systematically with the addition of lignin by ~1–20 K. Tensile tests showed that the 1 wt% additive ratio demonstrated the optimal balance of strength and ductility. Morphological observations supported these findings, revealing uniform dispersion at low additive ratio and increased agglomeration at higher ratios. Based on its superior performance, the composite containing 1 wt% lignin was successfully extruded into filament suitable for 3D-printing. This study highlights the synergy of bio-based additives and recycled polymers in engineering high-performance materials, promoting circular economy principles and reduced environmental footprint through upcycling post-consumer waste into functional, valuable products. Full article

Harmful algal blooms (HABs) caused by Karenia brevis (K. brevis) present a persistent ecological and public health challenge across coastal Florida. Reliable bloom forecasting is critical for protecting public health, supporting coastal economies, and enabling timely management responses. This study develops a regionally integrated machine learning framework to predict weekly K. brevis bloom occurrence using environmental data from both the Peace and Caloosahatchee Rivers, combined with coastal bloom records from Southwest Florida and Tampa Bay to enhance the spatial and temporal continuity of the response record. A Random Forest classifier was trained on a multi-decadal dataset incorporating river discharge, nutrient concentrations (total nitrogen and total phosphorus), wind forcing, sea surface temperature, salinity, and sea surface height anomalies as a proxy for Loop Current variability. The model achieved strong predictive performance on a chronologically withheld test set, with an overall accuracy of ~90%, balanced accuracy of 87.6%, and ROC–AUC of 0.972, indicating strong discrimination between bloom and non-bloom conditions with high precision and recall for bloom events. Bloom timing and persistence were captured with strong agreement during ongoing bloom periods, while non-bloom conditions were identified with low false-positive rates. Feature-response analyses indicated that bloom probability increased most sharply under moderate discharge and nutrient conditions, with diminished sensitivity at higher extremes. Learning curve analysis demonstrated robust training performance and stable generalization, with validation accuracy plateauing near 84%, suggesting a data-limited ceiling on forecast skill. By aggregating nutrient inputs across multiple watersheds and integrating spatially aligned bloom observations, this study demonstrates the utility of multi-source machine learning frameworks for regional-scale HAB prediction. The results support the development of early warning tools and provide a reproducible foundation for evaluating how combined watershed loading and physical forcing are associated with K. brevis bloom occurrence in complex estuary systems with watershed and coastal coupling. Full article

Under long-term marine environmental loading, deep-water risers are highly susceptible to fatigue damage, and the accumulation of local damage may lead to global structural failure. In this study, the fatigue damage mechanism and crack growth behavior of a girth-welded riser are systematically investigated. Full-scale radial fatigue test results of risers are referenced, and the experimental process is reproduced through numerical simulation. A finite element model of a girth-welded riser is established. The fatigue crack growth process is subsequently simulated, yielding the crack propagation path and crack growth rate curves. By comparison with experimental results, the characteristics of the crack growth process are analyzed, and the feasibility and accuracy of numerical simulations in predicting fatigue crack growth in riser girth welds are verified. A relatively accurate prediction model for fatigue crack growth in risers is proposed. To further improve the accuracy of crack growth prediction, a machine learning-based correction model is developed. On the basis of available in-service inspection data, a correction strategy is proposed in which the predicted crack growth process is dynamically updated with measured crack growth data. The proposed approach establishes a theoretical foundation for accurate and forward prediction of fatigue fracture damage in riser structures. Full article

Under long-term cyclic loading, the cumulative plastic deformation of unsaturated sandy subgrade is a key control factor for the pavement’s service performance. However, its evolution mechanism and quantitative characterization still lack a universal model. In this study, based on the GDS dynamic triaxial system, a series of cyclic tests were conducted under different conditions: matric suction from 0 to 90 kPa, net confining pressure from 30 to 120 kPa, dynamic stress amplitude from 60 to 240 kPa, and compaction degrees of 87–96%, reaching a total of 10,000 cycles. The results reveal that the permanent deformation of unsaturated sandy subgrade material evolves through three stages: fast, slow, and stable. The deformation is exponentially negatively correlated with matric suction, net confining pressure, and compaction degree, and exponentially positively correlated with dynamic stress amplitude. A coupling prediction model was developed by embedding matric suction and compaction degree factors into the Karg model. This model incorporates net confining pressure, dynamic stress amplitude, matric suction, and compaction degree. By using a normalized master curve method, the permanent deformation curves under different working conditions were compressed into a unique dimensionless function. The parameters have clear physical significance and allow for a unified description across stress, suction, state, and soil types. Experimental data, along with data from the literature, were used to validate the model, showing prediction errors of less than 10% and R² > 0.95. The model provides a simple, high-precision, and transferable theoretical tool for long-service-life subgrade deformation control. Full article

To further improve the mechanical properties of shotcrete in coal mine roadways, end-hooked steel fibers and chopped basalt fibers were selected. Based on the optimal mix ratios identified in existing research, steel–basalt hybrid fiber shotcrete (SBFC) specimens were prepared. Dynamic impact tests under different impact loads and various hybrid fiber contents were conducted using an SHPB. The microstructure was characterized using SEM and XRD. The results show that the dynamic compressive stress–strain curve of steel–basalt hybrid fiber shotcrete can be classified as elastic deformation stage, plastic yield stage, and post-peak failure stage. The incorporation of hybrid fibers reduces the elastic deformation and plastic yield stage, and the post-peak failure stage under active confining pressure shows elastic aftereffect characteristics. The dynamic compressive strength, dynamic elastic modulus, and deformation modulus increase with the increase in impact pressure and fiber content. When there is no confining pressure, the maximum dynamic compressive strength, dynamic elastic modulus, and modulus of deformation of SBFC4 reached 53.1 ± 2.2 MPa, 4.51 ± 0.3 GPa, and 2.55 ± 0.1 GPa, respectively, representing increases of 37.20%, 264.01%, and 59.37% compared with the control group. The dynamic compressive strength increases with the average strain rate, demonstrating a favorable strain rate effect. The energy–time history curves can be roughly divided into initial, growth, and stable stages. Under the same impact load conditions, as the fiber content gradually increases, the incident energy, dissipated energy, and energy utilization rate of the specimens all show a gradual upward trend. SEM and XRD results show that steel fibers and basalt fibers maintain good bonding with the cement matrix, contribute to the formation of gel and crystalline products within the specimens, effectively delay the initiation and propagation of cracks, and thereby improve the mechanical properties of the concrete specimens. Full article

The widespread loess in western China poses significant challenges to transportation infrastructure construction due to its water sensitivity and collapsibility. This study investigates the interface mechanical properties of bamboo geogrid-reinforced loess under static loading through large-scale indoor pull-out tests and DEM–FDM coupled numerical simulations. The effects of vertical stress, the pull-out rate, the number of transverse ribs, burial depth, and reinforcement material on interface behavior were systematically evaluated. Results show that peak pull-out force increases with vertical stress, the number of transverse ribs, and burial depth, with all curves exhibiting pronounced strain hardening followed by softening characteristics. The pull-out rate exhibits a non-monotonic effect, with peak resistance higher at both lower and higher rates compared to intermediate rates. Bamboo geogrids demonstrate substantially superior performance over geogrids, with approximately four times higher peak pull-out resistance and greater initial stiffness. Numerical analysis reveals increased porosity and decreased coordination number in the grid vicinity, the horizontal stratification of the slip rate along the reinforcement, and concentration of strong force chains ahead of transverse ribs, elucidating the model-derived mechanisms underlying the macroscopic reinforcement effects. The findings confirm that bamboo geogrids provide effective and sustainable reinforcement for loess subgrades, offering a scientific basis for environmentally friendly engineering applications in loess regions. Although potential long-term durability under field environmental conditions requires further verification, the superior mechanical interface performance demonstrated here positions treated bamboo geogrids as a promising sustainable reinforcement option. Full article

This study addresses a critical methodological gap in evaluating building envelope performance in hot, arid climates, the overreliance on annual energy indicators, which fail to capture transient thermal behavior during peak-load periods. In such environments, instantaneous heat gains, their intensity, and temporal distribution are decisive factors for cooling demand, occupant comfort, and grid stability. To overcome this limitation, a dynamic evaluation framework—the Thermal Adaptation Rating (TAC) system—is proposed. TAC integrates three interrelated indices—peak temperature reduction (ΔT_peak), relative peak cooling load reduction (ΔP_peak, %), and peak thermal delay (Δt_delay), representing thermal damping, load intensity mitigation, and temporal redistribution, respectively. A typical residential building in Karbala was modeled in DesignBuilder using the EnergyPlus engine, with inputs documented and calibration performed against real consumption data following ASHRAE standards (MBE and CV(RMSE)) to ensure reliability. The study examined advanced envelope systems, including thermochromic glass (TG), phase-change materials (PCMs), aerogel materials (AMs), and hybrid combinations. Results revealed that while AM achieved the greatest annual energy savings, its impact on instantaneous cooling load was limited. PCM, by contrast, effectively mitigated and delayed peak loads, enhancing thermal comfort (PMV/PPD). Hybrid systems, particularly TG-PCM, delivered the most balanced performance, simultaneously reducing peak cooling load and shifting its occurrence to reshape the cooling demand curve during critical periods. These findings demonstrate that annual indices alone are insufficient for evaluating envelope performance in extreme climates. Peak-condition analysis, expressed in terms of instantaneous cooling load, as operationalized through TAC, provides a more accurate representation of thermal behavior and offers a practical tool to guide envelope design decisions in hot, dry regions. Full article

With the high penetration of distributed photovoltaics (PV) in distribution areas, transformer capacity limits and source–load fluctuations have become key factors constraining PV accommodation. To accurately assess the PV hosting capacity under energy storage regulation, this paper proposes an assessment method based on operating region analysis. First, a coordinated operation model for the distribution area is established, incorporating the transformer capacity, energy storage constraints, and power balance. On this basis, the calculation boundaries for the PV hosting capacity are discussed in two scenarios: Model 1 ignores power curve uncertainty, characterizing the geometry of the conventional operating region to find the maximum deterministic hosting capacity ($S_1$) that keeps the region non-empty. Model 2 introduces box-type uncertainty sets for the source and load, proposes the concept of a “Self-Balanced Operating Region”, and constructs a robust feasibility determination model ($f_3$) based on a Min–Max–Min structure. To solve this multi-layer nested non-convex model, an iterative algorithm based on duality theory and Benders decomposition is employed to determine the robust hosting capacity under uncertainty ($S_2$) at the critical point where $f_3$ shifts from zero to non-zero. Case studies show that source–load uncertainty leads to a significant contraction of the operating region, and the robust hosting capacity under uncertainty requirements is strictly less than the deterministic hosting capacity ($S_1>S_2$). This method quantifies the reduction effect of uncertainty on the accommodation capability, providing a theoretical basis for planning high-renewable penetration distribution areas and energy storage configuration. Full article

Sometimes only a portion of the surface of a concrete element is loaded, which causes stress concentration in that region. To safely transfer concentric loads to concrete components such as column bases, short cantilevers, superstructure piers, post-tensioned elements, and support anchors, it is imperative to investigate the local compressive characteristics of concrete. To learn more about this subject, further research is required, as there are currently insufficient studies in this field. Therefore, the local compressive behavior of concrete under concentric stresses is the main focus of this work. Concrete is represented as block samples with dimensions of 200 × 200 × 250 mm. A stiff steel plate is used to apply concentric loading on the surface of the samples. The primary parameters are the bearing plate dimensions, shape (square, rectangle, and circular with varying areas), and rectangularity. Additionally, the bearing plate’s movement is examined. The stress-slip curves, ultimate bearing strengths, failures, and related slippages of the tested samples are discussed. The findings revealed that the upper surface of the concrete samples exhibited localized deterioration beneath the bearing plate. Additionally, the ultimate bearing strength of the sample loaded with the 6 × 6 cm square plate was 163% greater than that of the sample loaded with the 10 × 10 cm square plate. Furthermore, the sample loaded with the circular plate with a diameter of 4 cm had an ultimate bearing strength that was 181% greater than the sample loaded with the circular plate with a diameter of 11 cm. It is clear that the samples loaded with a circular plate of varying diameters had an ultimate bearing strength that was 8.5–11% higher than the samples loaded with a square plate of varying lengths. Full article

This paper presents an iterative graph-analytical procedure for determining the roll center height, one of the most critical design parameters influencing vehicle dynamic behavior during cornering. The conventional approaches generally determine roll center locations from suspension kinematics and then evaluate vehicle behavior using multibody or numerical vehicle dynamics models. By contrast, the proposed method is intended for the preliminary design stage and provides a direct correlation between front and rear target roll center heights using tire test data, load transfer and axle-level equilibrium conditions. The main advantage of the method is that it helps define a feasible design space before detailed geometry optimization or MBD validation is performed. The objective is to achieve stable and neutral handling (avoiding intrinsic understeer or oversteer tendencies) during steady-state cornering at a predefined target lateral acceleration. The methodology integrates (i) lateral force equilibrium at the axle level, (ii) a dynamic load transfer model based on axle roll stiffness and roll center heights, and (iii) experimental tire grip characteristics (lateral force–slip angle curves under varying vertical loads), processed through numerical interpolation. The procedure is demonstrated using a vehicle model with specific geometric and mass parameters. The results indicate that the methodology does not yield a single unique solution, but rather a set of correlated roll center heights, allowing the designer to select the most feasible geometric configuration while maintaining neutral handling. As an example, the paper presents a convergent solution for the front and rear roll center heights that satisfy neutrality conditions at a slip angle of approximately 4°. This study provides a fundamental framework for the geometric design of suspension systems and serves as a basis for subsequent numerical and experimental validation. Full article

Coastal reclaimed areas are characterized by complex strata and high groundwater levels, and pile foundations in such areas often suffer from insufficient uplift resistance. Compared with conventional cast-in-place piles, squeezed branch piles exhibit superior uplift performance; however, studies on squeezed branch piles in reclaimed areas remain limited. To investigate the uplift bearing performance of squeezed branch piles in the complex strata of coastal reclaimed areas, in situ full-scale uplift tests were conducted in the Shenzhen Binhai Avenue (Headquarters Base Section) traffic reconstruction project. Based on the actual physical and mechanical properties of the soil strata, a three-dimensional numerical model was established and validated against the load–displacement curves obtained from the in situ full-scale uplift tests. On this basis, the uplift bearing performance of squeezed branch piles, the differences in uplift bearing performance between branch and plate structures, and their applicable strata were analyzed. The plate structure and different branch configurations of squeezed branch piles exhibit distinct symmetric configuration characteristics, and these configuration differences influence the overall uplift bearing performance. The results show that the load–displacement curves of the uplift piles are generally smooth, without obvious abrupt rises or drops, exhibiting a gradual variation pattern, and the maximum pile-head displacements are all less than 100 mm. The mobilization of the bearing capacity of the branch and plate structures exhibits a distinct temporal and sequential pattern, with the plate structures at shallower embedment depths mobilized earlier than those at greater depths. Compared with conventional cast-in-place pile foundations, the presence of branches and plates endows squeezed branch piles with better elastic mechanical behavior and higher rebound ratios during unloading. Under identical stratum and loading conditions, the uplift bearing performance of the plate is 133% higher than that of the six-radial-branch configuration, while that of the six-radial-branch configuration is 34% higher than that of the four-radial-branch configuration. It is recommended to adopt the six-radial-branch configuration in clayey sandy gravel strata and the plate configuration in gravelly clayey soil and completely weathered coarse-grained granite strata, whereas neither branches nor plates are recommended in soil-like strongly weathered coarse-grained granite strata. Full article

This study presents a predictive manufacturing framework for customizing the biomechanical response of a 3D printed ergonomic armrest based on relaxed Voronoi metamaterials. A double curved armrest geometry was combined with parametric lattice generation, stereolithography printing in BioMed Elastic 50A resin, uniaxial compression testing of cylindrical lattice specimens, and homogenized finite element simulations using a CT derived forearm model under 15, 30, and 45 N loading. The results showed that both cell size and ligament thickness strongly affected compressive behavior, with smaller cells and thicker ligaments producing higher stiffness and earlier densification. Among the uniform configurations selected for simulation, the E-9-1.5 lattice provided the most balanced response, maintaining contact pressure below about 70 kPa up to 45 N, whereas the stiffer E-7-1.5 configuration exceeded 160 kPa and the E-7-1 configuration surpassed 100 kPa at higher load. Based on these findings, a functionally graded Voronoi concept was developed to combine a more compliant central zone with a stiffer peripheral support region while preserving conformity to the complex armrest boundary. Overall, the results show that relaxed Voronoi lattices offer a computationally efficient route toward anatomically conforming and mechanically tunable cushioning interfaces. Full article