Search Results (1,115)

In car–pedestrian collisions (CPC), most studies have focused on injury analysis using youth models. However, the mechanical performance of bones and muscles naturally deteriorates with age. Compared with younger people, the elderly are more vulnerable to bone fracture when subjected to the same impact. In this study, a digital twin model of the elderly lower limb (DTM-ELL) and a digital twin model of the youth lower limb (DTM-YLL) for Chinese 50th-percentile males were developed based on CT images, in which 150 material properties were assigned to the bones according to their Hounsfield units. The digital twin model of the lower limb (DTM-LL) was validated through quasi-static three-point bending simulation and dynamic lateral loading simulation of the knee joint. A family-car lateral impact simulation was used to verify the consistency between the developed model and THUMS (Total HUman Model for Safety). Through collision simulations between each of three car models and the DTM-LL, it was found that the maximum average stress in the femur of the DTM-ELL is 62.64% of that of the DTM-YLL under the same CPC conditions. Surrounding soft tissues, acting as effective cushioning during CPC, reduce the maximum femoral stress by 29.56% relative to the model without surrounding soft tissues. Finally, a real CPC accident was reconstructed in finite element software using the DTM-LL developed from the CT images. The reconstruction results showed that the DTM-LL had acceptable biofidelity and can be used to study femoral injuries in elderly pedestrians involved in CPC. Full article

With the growing penetration of renewable energy, accurately characterizing the frequency support performance of grid-following wind and photovoltaic (PV) units has become increasingly important. However, conventional methods for assessing frequency support parameters often overlook practical dynamic effects, making it difficult to determine the support parameters actually realized during disturbances. To address the time-domain coupling, differential noise amplification, and parameter distortion problems in the online identification of virtual primary frequency regulation and virtual inertia coefficients, this paper establishes a frequency response model for wind and PV units that incorporates these support mechanisms together with practical physical constraints. On this basis, a two-stage forgetting-factor recursive least squares (FF-RLS) method is proposed to identify realized frequency support parameters. Exploiting the difference in response time scales between primary frequency regulation and inertial support, a quasi-steady-state frequency regulation window and a transient inertia window are constructed to decouple the two parameters. Meanwhile, Tustin phase compensation and band-limited differentiation are introduced to mitigate measurement noise and the phase mismatch between frequency and power responses. Finally, a stable window criterion is developed to adaptively extract reliable identification intervals. Simulation studies on a modified IEEE 24 bus system, together with comparisons against conventional identification methods, demonstrate the effectiveness and accuracy of the proposed method. Full article

The zinc oxide nanoparticles (ZnO NP) were synthesized from zinc acetate using potato starch as a capping agent through alkaline sol–gel precipitation, without calcination. The produced nanoparticles were characterized using various analytical techniques, including UV–visible absorption spectroscopy (UV-vis), zeta potential analysis, dynamic light scattering (DLS), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and transmission electron microscopy (TEM). The ZnO NP exhibited an absorption maximum at 361 nm in the UV-vis spectrum, a z-average value of 138.1 nm and a zeta potential value of −23.8 mV as measured by DLS. The XRD pattern revealed distinctive diffraction peaks corresponding to the hexagonal wurtzite crystal structure characteristic of ZnO. FTIR analysis indicated that NP were capped with hydroxyl functional groups from the starch. The produced NP were quasi spherical, with sizes ranging from 20.7 to 38.1 nm and a mean particle size of 30.6 ± 5.1 nm. The potential application of ZnO NP as a photocatalyst was studied under UV light at 365 nm for the decolourization of tartrazine, a model azo food dye. The effects of varying concentrations of the catalyst (900–4500 µg/mL), tartrazine (2.5–10 µg/mL) and reaction time (30–120 min) on tartrazine removal were monitored using UV-vis at 426 nm. Under optimum conditions of 2700 µg/mL NP, 7.5 µg/mL tartrazine and a reaction time of 90 min, a 92% removal was achieved, with a rate constant (k) of 0.0135 min⁻¹. Additionally, the bactericidal activity of the NP against Escherichia coli and Bacillus subtilis was investigated using the resazurin broth method. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values against E. coli and B. subtilis were 2700 and 1800 µg/mL and 3600 and 2700 µg/mL, respectively. Thus, the current study highlights the calcination-free, potato starch-capped sol–gel synthesis and the bifunctionality of ZnO NP as a recyclable photocatalyst and a bactericide for the decolourization of dyes and pigments, as well as for the bacterial disinfection of food industry wastewater effluents. Full article

Predator–prey interactions are governed not only by direct consumption but also by behavioral responses of prey to the perceived risk of predation. In this work, we formulate a discrete-time predator–prey model in which predator-induced fear modifies prey reproduction through an exponential suppression mechanism, while predator consumption follows a Holling type-II functional response. The resulting map combines Ricker-type prey growth, density-dependent predation, and a non-consumptive effect associated with predator presence. We establish fundamental dynamical properties of the system by proving positivity, boundedness, and persistence under appropriate parameter restrictions. The existence of biologically meaningful equilibria is determined, and their local behavior is characterized using the Jacobian matrix and Jury stability criteria. To examine the influence of ecological parameters on the dynamics, we employ a global sensitivity analysis based on the Partial Rank Correlation Coefficient (PRCC) approach. The analysis identifies the parameters that most strongly affect prey and predator abundance. Numerical investigations reveal a wide range of dynamical regimes, including stable coexistence, periodic oscillations, higher-period attractors, quasi-periodic motion, and chaos. In particular, flip and Neimark–Sacker bifurcations are observed as ecological parameters vary. Basin-of-attraction computations and two-parameter iso-spike diagrams further demonstrate the presence of multistability and strong dependence on parameter combinations and initial conditions. Finally, period-doubling control and pole-placement techniques are employed to suppress undesirable chaotic oscillations and recover stable coexistence. The results emphasize that predator-induced fear can substantially modify population fluctuations and may act as an important mechanism regulating coexistence and complex dynamics in discrete ecological systems. Full article

Pressure-dependent pore-volume deformation is a critical but poorly constrained variable in dynamic reserve assessment for ultra-deep fractured-vuggy carbonate reservoirs, where fractures, dissolution pores, and vugs respond differently to effective-stress loading. In this work, a pore-structure-aware evaluation strategy was developed by integrating high-temperature and high-pressure volumetric measurements with data-driven regression. Twelve carbonate core plugs from the Ordovician Yijianfang and Yingshan formations of the Fuman Oilfield were selected to represent matrix-pore, dissolution-pore, fracture-vug, and fracture-dominated pore systems. Stepwise net-pressure experiments were performed under simulated reservoir conditions, and pore-volume compressibility (Cp) was calculated from corrected pore-volume changes. Measured C_p values reveal a distinct stress-sensitive response: C_p declines sharply during the low-net-pressure stage and then tends toward a quasi-stable level as net pressure increases, indicating progressive closure of mechanically compliant fractures, narrow throats, and weakly supported dissolution pores. Although porosity is positively associated with Cp, samples with comparable porosity display markedly different compressibility values, confirming that pore-space geometry and fracture-related compliance must be considered. Eight representative regression algorithms were then compared, using net pressure, porosity, permeability, initial pore volume, surface porosity, temperature, and a pore-structure index as model inputs. To further assess model generalization to completely unseen core plugs, additional core-ID-based leave-one-core-out (LOCO) validation was performed for k-nearest neighbors and AdaBoost. Under this grouped validation, k-nearest neighbors yielded an RMSE of 13.5978 × 10⁻⁴ MPa⁻¹ and an R² of 0.8408, whereas AdaBoost achieved an RMSE of 10.0160 × 10⁻⁴ MPa⁻¹ and an R² of 0.9136, indicating greater cross-core robustness of AdaBoost. Permutation-importance analysis of the split-specific KNN model indicated that net pressure, porosity, surface porosity, and pore-structure index made the largest predictive contributions within that model. Moreover, the predicted normalized Cp values reproduced the experimentally observed decreasing trend with increasing net pressure, supporting the physical consistency of the k-nearest neighbors predictions. The proposed experimental–machine learning framework offers a pressure-dependent method for estimating pore-volume compressibility within the geological and petrophysical domain represented by the investigated Fuman Oilfield cores, and provides more representative inputs for material-balance analysis, dynamic reserve evaluation, and production adjustment. Full article

This review examines the ballistic resistance of fiber-reinforced cement composites (FRCs) and related cementitious systems for protective structures, with emphasis on projectile–target interaction, penetration and scabbing mechanisms, and the governing roles of material and structural parameters. The synthesis indicates that ballistic resistance is controlled not only by compressive strength but also by the combined effects of dynamic tensile behavior, fracture energy, crack-bridging efficiency, aggregate characteristics, target thickness, projectile characteristics, and structural configuration. Steel and hybrid fiber systems generally provide the most consistent improvements in scabbing suppression and residual integrity, while hard aggregates and multilayer hard–soft–tough arrangements enhance penetration resistance, stress-wave attenuation, and staged energy dissipation. The review also evaluates current numerical approaches, including rate-sensitive constitutive models, cohesive and continuum damage formulations, smoothed particle hydrodynamics, and data-driven methods. Several calibrated and experimentally validated simulations reproduced penetration depth and major damage trends with useful engineering agreement; however, significant challenges remain in representing multi-hit degradation, fiber-scale pull-out, fragment ejection, and interface debonding. In addition, the review highlights the limited suitability of existing ballistic standards for quasi-brittle cementitious systems and emphasizes the need for FRC-specific testing, large-scale validation, and more sustainable protective material design. Full article

Against the backdrop of the coordinated advancement of the dual carbon goals and new-type urbanization, the traditional high-carbon development model has become a major constraint on urban green transformation. As a critical vehicle for digital technologies to empower low-carbon governance, smart cities have yet to receive a systematic evaluation of their long-term policy effects based on quasi-natural experiments. Using panel data from 280 prefecture-level cities from 2003 to 2023, this study takes the smart city pilot policy as a quasi-natural experiment. It adopts Interpretive Structural Modeling (ISM) to identify the key influencing factors and transmission paths of carbon emissions, and employs the progressive difference-in-differences (DID) model to assess the carbon emission reduction effects, dynamic evolutionary characteristics and urban heterogeneity of smart city construction. Furthermore, the mediation effect model is applied to clarify its underlying mechanisms. The empirical results show that smart city construction significantly curbs urban carbon emissions, and this finding remains valid after a series of robustness tests, including the parallel trend test, placebo test and PSM-DID. The emission reduction effect of the policy exhibits an obvious time lag: the effect is insignificant in the first and second years after policy implementation but turns significantly negative and continues to strengthen starting from the third year. Noticeable urban heterogeneity is also observed, with a more prominent emission reduction effect in eastern regions, central cities with high administrative ranks and large-sized cities. Mechanism analysis reveals that the conventional industrial pollution reduction pathway does not serve as the primary transmission channel. Instead, a suppression effect is identified, suggesting that smart cities achieve carbon abatement primarily through the digital empowerment of energy allocation efficiency—a pathway distinct from traditional end-of-pipe governance approaches. Unlike previous studies, this study combines ISM with a staggered DID framework to reveal the dynamic effects and transmission mechanisms of smart city policies. Full article

Traditional UAVs often suffer severe structural damage during high-speed collisions. Recent research has explored sensing, control, and design strategies to enhance collision resilience. This work presents fully passive, soft continuum-lattice arms that combine visco-hyperelastic materials with nonlinear lattice geometries to trigger controlled buckling of the beams and absorb impact energy. Visco-hyperelastic parameters are identified from quasi-static and dynamic tensile tests on the material and then implemented in finite element models to optimize lattice porosity and distribution for force mitigation under quasi-static and dynamic loading conditions. Experimental validation of the simulations for the optimized beam configuration shows good agreement, with MAE 6.7% and RMSE 7% in quasi-static loading and MAE 12.1% and RMSE 14% in dynamic loading. Controlled drop tests on a quadrotor prototype demonstrate superior impact energy absorption and force mitigation compared with a bulk material design (up to a 99% increase in specific energy absorption and up to a 65% reduction in peak impact force). Furthermore, the latticed UAV is flown voluntarily and then dropped onto the ground, maintaining low thrust losses of 0.42 ± 0.26% and structural integrity after impact. Full article

Pneumatic artificial muscles, including McKibben-type actuators, offer high power-to-weight ratio, compliance, and inherent safety, but their nonlinear pressure–contraction behavior, hysteresis, saturation, and load-dependent dynamics make accurate position control challenging. This study develops a practical data-driven workflow that derives a branchwise feedforward compensator and an LQI or PID controller from one open-loop characterization experiment. Quasi-static characterization first identifies a conservative control-ready voltage window. A bounded random excitation within this window is replayed with $4\mathrm{s}$ holds to expose terminal and transient behavior. The same experiment supplies branchwise discrete plant models and a feedforward lookup. Two open-loop-derived transient layers, voltage creep compensation and dynamic pressure referencing, are applied to the raw lookup before simulation. Four controller variants are compared on a common simulated closed-loop benchmark built from the identified plant: a feedforward-only baseline, a branchwise proportional–integral–derivative (PID) baseline, a base linear quadratic integral (LQI) controller with displacement and pressure feedback, and a velocity-state LQI extension with a filtered velocity estimate. A multi-metric optimization score balances tracking RMS, settled oscillation, command activity, saturation, and gain magnitude. The score selects the base LQI within the LQI family. The selected gains and transient layers are deployed in a real-time implementation with manually reduced position gains. The controllers are then evaluated on a common reference stream against the physical actuator. Although simulation metrics cannot be transferred directly to the real system, the combined-metric ranking of the controllers remains unchanged. Full article

There is a growing need for high-efficiency power electronic converters that can effectively convert energy, regulate voltages, and enhance power quality in standalone power generators, as the use of renewable energy sources and battery energy storage devices increases. The quasi-impedance-source inverter (qZSI) has attracted significant interest due to its single-stage buck-boost operation, continuous input current, reduced reliance on passive elements, and increased reliability. In this paper, the control strategy and implementation of the qZSI with battery assistance for standalone photovoltaic energy generation are discussed. To analyze the operational characteristics and design the control strategy of the qZSI, the system equations are linearized around the nominal operating point to develop a small-signal model, from which the direct current (DC) side and alternative current (AC) side transfer functions are derived and used as the basis for controller design. Using the proposed model, hybrid controllers are designed to control the shoot-through duty cycle, maintain DC link voltage stability, and battery charging to achieve stable power generation. Furthermore, the SPWM technique is applied to produce AC power with minimal harmonic content and higher efficiency. Application results show stable dynamic behavior, effective battery energy management, improved voltage regulation, and reduced harmonic distortion in the output waveform. The main contribution is a low-complexity coordinated PI and PR control framework for standalone battery-assisted qZSI operation, experimentally validated under DC- and AC-side disturbances without requiring an additional battery-side power-conversion stage. Full article

Addressing the dual threats of malicious jamming and time-varying fading faced by wireless communication links in complex dynamic electromagnetic adversarial environments, existing intelligent anti-jamming methods predominantly focus on single-dimensional resource optimization under quasi-static channels. This focus neglects the nonlinear superposition effects of multi-path deep fading and dynamic strong jamming in the time-frequency domain, making it challenging for systems to balance transmission reliability and system energy efficiency in physical environments where fading and suppression coexist. To address this issue, this study proposes a joint intelligent anti-jamming method for channel switching and transmit power control based on a Deep Q-Network (DQN). Initially, a composite communication environment model incorporating Markov time-varying fading and jamming is constructed. Subsequently, the joint resource scheduling problem is formulated as a Markov Decision Process. The environment state space is reconstructed by integrating continuous channel state estimation and jamming observation features, accompanied by the design of a highly aggregated two-dimensional discrete action space for both channel and power. Finally, a composite reward function evaluating both communication success rates and power consumption costs is proposed to guide the agent in multi-dimensional resource joint optimization. Simulation results demonstrate that the proposed algorithm effectively extracts implicit features under the composite state of fading and jamming. When encountering extreme deep fading or full-band blocking, the agent strategically triggers a silent mechanism to avoid exorbitant invalid energy consumption penalties, while precisely matching interference-free channels with the minimum effective transmit power during favorable communication windows. Simulation results show that compared with traditional xx algorithms, the proposed method significantly improves the dynamic successful transmission rate and system energy efficiency in complex, highly dynamic scenarios, achieving an effective optimization of anti-jamming reliability and low power overhead. Full article

This study investigates the tensile behavior of polytetrafluoroethylene (PTFE) tube material across a wide range of temperatures, displacement rates, and sampling directions. Temperature has a dominant effect: flow stress decreases and ductility increase markedly as temperature rises, while displacement rate and sampling direction show only minor effects. A temperature-coupled three-part superposition quasi-static stress (TPS) model is developed, using Arrhenius-type relationships to describe how the model parameters evolve with temperature. This model outperforms the Johnson–Cook, Ogden, and Zhu–Wang–Tang (ZWT) models across the full strain range and the entire tested temperature range. Molecular dynamics simulations further show that rising temperature increases free volume and chain-segment mobility while reducing chain orientation, providing a molecular-scale explanation for the observed thermal softening. Full article

Over the years, granular packing models have evolved from linear to non-linear models in order to improve their predictive capabilities. Expressed in terms of specific volumes, the paper shows that the models have been structured around three main approaches: a “top-down” approach based on the “worst-case scenario” from a completely segregated mixture and two “bottom-up” approaches, one based on an “ideal scenario” comprising filling and embedment mechanisms and the other based on a “quasi-ideal scenario” given two virtually unavoidable granular interactions: the wall effect and the loosening effect. These three approaches are illustrated by the use of three models (Chang’s, Westman’s, Roquier’s), evaluated on binary packings of spheres generated by Molecular Dynamics (MD). The minimum value of the specific volume is obtained at isostatic equilibrium under isotropic stress in the limit of frictionless rigid particles. Access to the coordination numbers between fine and coarse components leads to the definition of coarse-to-coarse, coarse-to-fine & fine-to-coarse, and fine-to-fine contact proportions, which allow to characterise, respectively, the zone dominated by coarse grains, the mixed zone and the zone dominated by fine grains. The three packing models are evaluated in terms of estimates of specific volumes with reference to these three zones for two size ratios. Full article

Complex underwater environments induce difficult-to-quantify ranging errors, constraining the localization accuracy and robustness of heterogeneous networks. To address this, a node localization method based on a Geometric Topology Perception-Enhanced Multi-Stage Reinforcement Learning Strategy is proposed. First, an uncertainty quantification model under multi-source interference is established to characterize time-varying noise and accurately quantify the ranging errors of heterogeneous links. Subsequently, using the resulting ranging variance, an adaptive weight allocation mechanism based on Minimum Variance Unbiased Estimation is constructed to dynamically adjust link weights, achieving the robust fusion of multi-modal observation data. Finally, a Weighted Least Squares objective function is formulated, and the GP-AC strategy is developed. By utilizing Gaussian Process Regression and local Geometric Dilution of Precision, a multi-stage reward mechanism is constructed to circumvent topological traps and accurately estimate the single-epoch three-dimensional coordinates of static or quasi-static underwater sensor nodes. Simulation results demonstrate that system robustness is improved by 91.9%, average accuracy is enhanced by 54.9%, and the measured average localization time is 7.45 s. Full article

The manuscript proposes a novel bounded proportional autoregressive (PAR) process based on the quasi-Lindley half-logistic unit (QHU) distribution, termed the QHU-PAR(1) process. Fundamental probabilistic properties of the process are established, including its Markov structure, moments, and stationarity conditions. A pseudo-innovation-based parameter estimation procedure is performed, demonstrating strong consistency and asymptotic normality of the resulting estimators. A Monte Carlo study is also conducted, showing satisfactory performance of the proposed estimators on finite samples, while the practical utility of the model is illustrated through the analysis of normalized crime-related time series. Comparative results show that the proposed QHU-PAR(1) process outperforms some competing specifications, highlighting its flexibility and efficiency for modeling bounded and asymmetric stochastic phenomena. Full article