Search Results (1,146)

L-shaped steel-reinforced concrete (SRC) columns are commonly used as edge and corner members in bridge piers and high-rise buildings. However, systematic experimental evidence on their dynamic behavior and detailing effects under lateral impact remains limited. This study presents a parametric drop-weight impact program on seven SRC columns with built-in L-shaped steel sections. The effects of impact velocity (v), axial compression ratio (n = 0–0.2), and stirrup spacing in the non-densified region (s = 100–200 mm) were examined in terms of damage evolution, impact-response indices (Fmax, Fave, Δmax, Δres, T), and energy absorption efficiency (η = E_ab/E). The results show that impact velocity was the dominant parameter governing both response amplitude and damage severity. Increasing v from 7.67 to 9.90 m/s increased Δmax and Δres by 92.6% and 144.3%, respectively, while η increased from 60.7% to 74.6%. Within the investigated range, axial compression improved resistance and suppressed residual deformation. As n increased from 0 to 0.2, Fmax and Fave increased by 17.5% and 30.4%, respectively, whereas Δres decreased by 32.1%. The effect of stirrup spacing on η was non-monotonic. The intermediate spacing (s = 150 mm) yielded the highest energy absorption ratio (60.7%) and the most balanced overall response among the tested cases, rather than representing a definitive optimum. No global buckling of the embedded steel section was observed, and all specimens maintained overall structural integrity under high-energy impact. These results provide experimental evidence for the response assessment and preliminary transverse detailing of asymmetric SRC columns under lateral impact. Full article

Retinal detachments following blunt ocular trauma are challenging to predict due to the complex and transient biomechanical responses of the globe. This study combines an in vitro weight-drop experiment and finite element analysis (FEA) to evaluate the mechanical pathways leading to traumatic retinal detachment and to predict the spatial likelihood of injury. In the in vitro model, a cylindrical weight was impacted onto freshly enucleated mouse eyes (16 weeks old) supported on a rigid metal plate. Following impact, the eyes were sectioned and stained using hematoxylin and eosin (H&E) for histological assessment. A finite element model of a mouse eye, including the cornea, sclera, lens, zonule, vitreous body, aqueous humor, and retina, was reconstructed from the histological section and used to simulate the whole sequence of compression and rebound following the blunt impact. The simulation demonstrated that the lens retained a high momentum. It generated an alternating compressive (up to −6.57 × 10⁻³ MPa) and tensile (up to 1.62 × 10⁻³ MPa) radial stress at the posterior pole and sustained compressive stress at the peripheral region (up to −3.12 × 10⁻³ MPa) and tensile-compressive stress variation at the equatorial region of the retina. In addition, the regions experiencing tensile stress overlapped with the region exhibiting retinal detachment in the in vitro experiment. These findings highlight the spatiotemporal mapping of biomechanical stress to predict traumatic retinal detachment following blunt impact and provide an understanding of early biomechanical response following ocular trauma. Full article

Accurate assessment of the State of Health (SOH) is critical for battery management systems in aviation. As a step towards this goal, this study presents a proof-of-concept for a novel SOH estimation method based on an Improved Particle Swarm Optimization-Extreme Learning Machine (IPSO-ELM) model, validated under controlled laboratory cycling conditions. Although traditional Extreme Learning Machines (ELM) are widely used due to their fast computation and good generalization, their random parameter initialization often leads to unstable convergence and limited accuracy. To address these limitations, this paper proposes a novel SOH estimation method based on an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the key parameters of ELM. Three health indicators (HI)—constant-current charging time, equal-voltage-drop discharge time, and average discharge voltage—were extracted from charge–discharge curves as model inputs. The IPSO algorithm dynamically adjusts the inertia weight, introduces a constriction factor and a termination counter to enhance global search capability and avoid local optima. Experimental results on open-source datasets (B005, B007, B0018) and laboratory datasets (A001, A002) demonstrate that the proposed IPSO-ELM model achieves a Root-Mean-Square Error (RMSE) below 0.7% and a Mean Absolute Percentage Error (MAPE) below 0.5%. Compared with standard ELM and PSO-ELM models, it significantly outperforms them in accuracy (e.g., for B0018, RMSE is reduced to 0.21% and MAPE to 0.14%), convergence speed, and robustness, establishing a foundation for future development of aviation-ready SOH estimators. Full article

Steel–concrete composite beams are widely used in building and bridge engineering; however, the impact response of Steel–Coarse Aggregate–Ultra-High Performance Concrete (Steel–CA–UHPC) composite beams remains insufficiently quantified, and no beam-specific dynamic capacity formula is available. To address this gap, companion static testing and drop-weight impact tests were performed on full-scale simply supported steel–CA–UHPC composite beams under single and repeated impacts, followed by development of a strain-rate-dependent dynamic increase factor (DIF) model and a capacity prediction framework. The companion static specimen reached 448 kN, whereas the 5 m impact cases produced peak forces of 930.0–940.4 kN, corresponding to 2.08–2.10 times the static level, with the initial peak forming within 1.0–1.1 ms. Dynamic failure was marked by rapid mid-span cracking of the CA–UHPC slab and brittle shear fracture of studs, while repeated impacts mainly accelerated cumulative damage before the final high-energy strike. Static–dynamic displacement comparison further revealed much more abrupt deformation concentration under impact loading. A revised static capacity formula reduced the prediction error from 4.46% for the code-based method and 1.00% for the literature model to 0.74%. Combined with the fitted DIF–strain-rate relation, the proposed framework reproduced the measured dynamic capacities with errors of −4.63% to 9.75%. The study provides member-level evidence and a practical DIF-based method for evaluating the impact resistance of steel–CA–UHPC composite beams. Full article

This study numerically investigates heat transfer and thermodynamic behavior in twisted square and rectangular air ducts while keeping a constant hydraulic diameter (D_h = 30 mm). Three aspect ratios are considered (AR = 1.00, 0.75, and 0.50). The heated test section (900 mm) is divided into three equal segments, and three pitch patterns are examined: a uniform pitch (400–400–400 mm, P444) and two axial gradients (300–400–500 mm, P345; 500–400–300 mm, P543). All results are compared to a standard reference, the straight square duct (SD-AR1.00), to ensure fair comparisons across all cases with Reynolds numbers between 5000 and 20,000. Among the twisted ducts, the strongest rectangularity combined with the increasing pitch sequence, TSD-AR0.50-P345, provides the best overall balance. Its heat transfer rises from Nu = 39.39 to 88.62, giving Nu/Nu₀ = 1.493 → 1.433, while the pressure penalty increases to f/f₀ = 1.345 → 1.405. Under cube-root weighting of friction, this case maintains the highest thermal performance factor, TPF = 1.352 at Re = 5000 and TPF = 1.279 at Re = 20,000. Second-law trends support the same ranking: exergy destruction decreases from 12.81 W (baseline) to 8.44 W at Re = 5000 (≈34% reduction) and from 6.54 W to 4.84 W at Re = 20,000 (≈26% reduction). The Bejan number remains high at low Reynolds numbers (≈0.998), indicating heat-transfer irreversibility dominance, but drops at higher Reynolds numbers (≈0.87) as frictional effects become more important. In general, the results show that adding a small axial pitch increase to rectangularity can improve near-wall mixing while reducing losses downstream. This leads to a clear improvement in both first-law performance and exergy-based measures. Full article

Background/Objectives: The clinical utility of summarizing long-term C-reactive protein (CRP) trends with a single mean remains unclear. We systematically characterized annual changes in CRP test volume and CRP level distributions using large-scale laboratory data collected at Dankook University Hospital (2008–2023) across the coronavirus 2019 pandemic period. Methods: Overall, 1,845,258 CRP values were analyzed; annual arithmetic, harmonic, and geometric means were calculated; long-term trends were assessed using weighted least squares (WLS) regression weighted by annual test volume; and temporal changes around the pandemic period were examined using a WLS-based interrupted time-series (ITS) segmented model with a prespecified 2020 break. Results: The annual test volume rose from 2008 to 2013 and 2019, dropped in 2020, increased in 2022, and declined in 2023. The arithmetic mean showed no long-term trend, whereas the harmonic and geometric means declined. ITS models exhibited no statistically significant immediate level-change term in 2020; however, post-2020 slope changes indicated a decline in the arithmetic mean and attenuation of the prior decline in the harmonic mean. As only four annual observations were available after 2020, these post-2020 trend estimates should be interpreted cautiously. Conclusions: Within this single-center tertiary-care dataset, different CRP summary measures showed different long-term patterns and post-2020 trend changes, without evidence of an abrupt shift in 2020, suggesting stratum-specific shifts that may be invisible to arithmetic mean-based surveillance. These findings are best interpreted as institution-specific and hypothesis-generating, and broader interpretive or operational implications require validation in multicenter settings with differing case-mix and care structures. Full article

Airborne electromagnetic (AEM) surveys map subsurface electrical structures by deploying transmitter and receiver coils on an airborne platform. However, platform-induced vibrations are transmitted to the sensors, generating strong motion-induced noise that severely degrades signal quality. To mitigate such noise, this study proposed the use of granular materials as a cushioning medium. An impact model based on the Discrete Element Method (DEM) was developed and validated against drop-weight experiments. Both granular material properties and impactor characteristics were investigated. The study examined the cushioning effects on both the base plate and the impactor under impact loading, and the sensitivity of key parameters was evaluated. The results showed that granular properties had minimal influence on the impactor peak force. Increasing particle Young’s modulus, density, or friction coefficient led to higher peak forces on the base plate, with Young’s modulus and density having significantly stronger effects than friction coefficient. Additionally, both the impactor size and velocity correlate positively with the peak forces transmitted to the base plate and experienced by the impactor. Under thin layer conditions, the impactor force was more sensitive to impact parameters, while in thick layers it was mainly determined by particle rearrangement and energy dissipation mechanisms. These findings reveal the mechanisms governing granular cushioning and provide a theoretical basis for vibration isolation design in AEM systems to preserve high-fidelity signals. Full article

Human Activity Recognition (HAR) in resource-constrained settings has been studied mainly through architecture design, compression, and deployment, while the role of the training objective has received less attention. This paper introduces Top-Confidence Gapped Cross-Entropy (TCG-CE), a lightweight modification of categorical cross-entropy in which each sample is weighted by the gap between the two most probable predicted classes. TCG-CE adds no trainable parameters and can be used as a drop-in replacement for standard cross-entropy. The method is evaluated on the UCI-HAR and WISDM benchmarks using compact recurrent models, namely TinyRNN, TinyGRU, and TinyLSTM. The evaluation focuses on macro-averaged predictive performance and also reports empirical runtime and memory observations under a fixed execution environment. Across datasets and models, TCG-CE improves balanced classification metrics, with the clearest gains observed on WISDM and in more capacity-limited settings. These results indicate that top-1/top-2 confidence-gap modulation is a practical loss-design strategy for improving macro-level predictive performance in compact HAR classification. Full article

Aiming to address the complex coupling of transient impulses and steady-state components in vibration signals of scroll compressors in air conditioning systems, this study proposes a physically driven heterogeneous dual-path adaptive weighting network (PDW-Net). The approach constructs a physics-inspired weighting module based on kurtosis and energy criteria, enabling adaptive reconstruction of transient impulses and steady-state vibration components. Feature extraction and decision-level fusion are achieved through a heterogeneous dual-branch network comprising a Fast Fourier Transform (FFT)-based one-dimensional convolutional neural network (1D-CNN) and a Short-Time Fourier Transform (STFT)-based two-dimensional convolutional neural network (2D-CNN). In experimental validation covering four typical fault conditions—condenser failure, refrigerant deficiency, refrigerant overcharge, and main shaft wear—the PDW-Net achieved an average diagnostic accuracy of 97.87% (standard deviation: 2.60%), with 100% accuracy in identifying refrigerant deficiency and normal operating states, demonstrating significant superiority over existing mainstream methods. Ablation studies reveal that the adaptive weighting mechanism contributes most substantially to performance, as its removal results in a 34.24 percentage point drop in accuracy. Replacing the heterogeneous dual-branch structure with a homogeneous counterpart reduces accuracy by 16.18 percentage points, robustly validating the efficacy of the physics-guided and heterogeneous fusion design. Full article

Wind-assisted ship propulsion (WASP) systems are gaining importance due to their contribution to reducing greenhouse gases and saving fuel. Existing studies mostly focus on the aerodynamics of sailing systems, the integration of sails and ship dynamics, and the prediction of fuel savings. The present study extends the use case of sailing systems by proposing a new control logic that improves maneuvering performance. Determining the spin ratio of rotor sails not only with thrust but also with side forces and moments is also included as an objective function. Using numerous random weights for each term and environmental conditions, the turning performance of the target ship was evaluated. Then, an artificial neural network (ANN) model was trained to decide on the optimal weights, depending on the environmental conditions. Finally, the performance of the new control approach was evaluated based on turning and zigzag test simulations. It was found that the advance, transfer, and tactical diameters dropped by up to 5%, 7% and 7%, respectively, compared to those of a conventional ship. When it comes to the zigzag performance, it was revealed that the overshoot angles dropped even though there was no simulation data about zigzag tests in the trained ANN model. Thus, it was shown that sails improve the maneuverability of ships in addition to providing additional thrust if a proper control approach is adopted. Full article

Background: Intrinsic foot muscle strengthening and orthotic devices such as toe spacers are commonly used to improve foot alignment and function. However, evidence regarding the combined effects of strengthening exercises and interdigital spacers remains limited. Objective: To examine whether adding a silicone toe spacer to a foot strengthening exercise program provides additional benefits compared with exercise alone. Design: Randomized controlled trial. Setting: University biomechanics laboratory. Participants: Twenty-five healthy adults (mean age 23.8 ± 1.3 years) without lower limb injury or neurological disorders were randomly allocated to one of two intervention groups. Interventions: Participants performed a six-week foot strengthening program (22 sessions). One group performed exercises alone, while the second group performed the same exercises while wearing a silicone interdigital toe spacer. Main outcome measures: The primary outcome was hallux valgus angle. Secondary outcomes included active and passive hallux range of motion (ROM), ankle dorsiflexion ROM (weight-bearing lunge test), navicular drop, and postural stability during single-leg stance assessed using center-of-pressure (CoP) measures. Results: Both groups demonstrated improvements over time in hallux valgus angle (p = 0.001, η² = 0.361), active hallux range of motion (p < 0.001, η² = 0.545), and ankle dorsiflexion (p < 0.001). However, no significant between-group differences were observed for the primary outcome or most secondary outcomes. A significant time × group interaction was observed only for passive hallux range of motion (p = 0.040, η² = 0.170), indicating greater improvement in the exercise-only group. Navicular drop and postural stability variables did not change significantly. Conclusions: A six-week foot strengthening program improved hallux alignment, hallux mobility, and ankle dorsiflexion in healthy adults. The addition of a silicone toe spacer did not provide additional short-term benefits compared with exercise alone. Full article

Disaster resilience has become a key focus of risk reduction efforts, but measuring it remains complex due to differences in hazards, development paths, and data systems. This study modifies the Baseline Resilience Indicators for Communities (BRIC) approach, based on the Disaster Resilience of Place (DROP) framework, to evaluate community resilience in Serbia and highlight regional differences. An initial list of 186 indicators was created from international BRIC studies and resilience research, then tailored to Serbian conditions through contextual review and data checks. Indicators were normalized using min–max scaling (0–1), and indicators with negative orientation were inverted to ensure that higher values indicate greater resilience. Scores for each dimension were calculated as equally weighted averages across six areas: social, economic, social capital, institutional, infrastructural, and environmental. The overall BRIC index was derived as the average of these dimension scores. Z-scores facilitated the classification of resilience levels and the comparison between regions. The results show clear regional disparities: in the complete model, Belgrade has the highest resilience (BRIC = 0.557), while Southern and Eastern Serbia have the lowest (BRIC = 0.414). Patterns across dimensions show that Belgrade excels in social and economic capacity but lags in environmental indicators; Vojvodina has the strongest institutional and infrastructural capacity; and Šumadija and Western Serbia perform best in environmental indicators. Correlation analysis revealed multicollinearity, leading to the removal of 14 redundant indicators and the refinement to a set of 57. After this reduction, regional rankings change, with Vojvodina (BRIC = 0.530) and Šumadija and Western Serbia (BRIC = 0.522) emerging as higher-resilience regions, while Southern and Eastern Serbia remain the least resilient (BRIC = 0.456). The adapted BRIC-DROP model offers a clear, locally relevant tool for mapping resilience and guiding targeted policies in Serbia, enabling region-specific efforts to address structural resilience gaps. Full article

Polymer nanocomposites offer significant potential for improving the strength-to-weight ratio and dynamic behavior of drone blades. This study examines the vibration characteristics of tapered aramid (Kevlar)/epoxy composite blades reinforced with nanocarbon fillers—graphene (2D), multi-walled carbon nanotubes (MWCNTs, 1D), and fullerene (0D)—to determine the most effective filler for enhancing stiffness and operational stability. The laminated blades (300 mm length, 200 mm width, root thickness 13 mm, tip thickness 8 mm) incorporate ply drop-offs and a central honeycomb core. Modeling was performed using classical laminate plate theory integrated with the finite element method (FEM) in MATLAB (R2016a). Under clamped–free–free–free boundary conditions, the study considered rotational speeds of 750–2250 rpm, setting angles of 30–60°, various fiber orientations, and nanofiller contents of 0–10 wt.%. The results indicate that while the setting angle minimally affects natural frequency, it significantly influences damping in modes (1,2) and (2,1). Increasing nanofiller content improves stiffness, with optimal performance observed near 5 wt.%. At 1500 rpm in mode (1,1), MWCNTs provided the greatest enhancement. Overall, MWCNTs exhibited superior stiffness improvement and rotational stability compared to other fillers. Full article

The widespread commercialization of wireless chargers for electric vehicles generally suffers from one main problem, which is the perfect alignment between the two coils, leading to a decrease in mutual inductance, which causes a drop in magnetic coupling and even a failure to transfer power. To address this persistent problem, this work proposes a comprehensive and integrated method for optimizing the coils and control architecture for reliable and safe battery charging. To address the challenges of a complex, nonlinear design space and the need for misalignment-tolerant geometries, we employ a memetic algorithm (MA) that hybridizes Particle Swarm Optimization (PSO) for broad global exploration with Mesh Adaptive Direct Search (MADS) for precise local refinement. This combination effectively avoids poor local solutions—a limitation of standalone PSO or GA approaches reported in recent studies—while efficiently converging to coil geometries that maintain strong magnetic coupling under misalignment. After the coils have been designed, electromagnetic validation is tested using finite element analysis (FEA), which allows the magnetic field distribution to be evaluated, as well as the coupling coefficient under different scenarios of misalignment and variation in the air gap between the ground side and the vehicle side. At the same time, a comprehensive control strategy for the primary side of the system has been developed. This control method ensures power management on the primary side, enabling system interoperability for charging multiple types of vehicles, as well as reducing vehicle weight for greater range. All this is combined with an innovative pulsed current charging method, chosen for its advantages in terms of thermal stability, ensuring safe and efficient recharging that is mindful of battery health. Simulation and experimental validation demonstrate that the proposed framework maintains stable wireless power transfer and achieves over 87% DC–DC efficiency under lateral misalignments up to 100 mm, fully complying with SAE J2954 alignment tolerance requirements. Full article

Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning. Full article