Search Results (3,655)

The present paper addresses the experimental measurement of vibration frequencies using an earth pressure sensor embedded in a full-scale (1:1) test structure. The vibration frequencies within the tested structure were induced by static load tests carried out at different elevation levels (corresponding to varying thicknesses of the crushed aggregate layer) in accordance with the methodology applied on German railways (DIN 18 134). The aim of the research was to verify the stress state at individual partial levels of the tested structure on the basis of the measured vibration frequencies, and to determine the depth of influence and the load dispersion angle generated by the static load test (SLT). The measured parameters also serve as input data for parallel research focused on the assessment of transition zones between railway embankments and artificial structures along railway lines. The results presented in this paper indicate that the stress induced by the SLT decreases with increasing structural thickness of the tested construction. For a structural layer thickness of 150 mm, the resulting stress corresponds to approximately 63% of the stress value (force effect) induced on a rigid circular plate (σ = 0.50 MPa), whereas for a layer thickness of 900 mm, the stress corresponds to approximately 12% of that value. The force (stress) effects of the SLT cease to act at a depth between 900 and 950 mm (only stress due to the self-weight of the overlying material was recorded), and the load dispersion angle is approximately 40°. Full article

Understanding the intrinsic Li–H₂ interaction, decoupled from substrate effects, is essential to rationalize the performance of lithium-decorated hydrogen storage materials. To address the current lack of a clean theoretical baseline, we characterized the sequential H₂ adsorption on the gas-phase Li₆ superatomic cluster using high-level density functional theory (DFT), complemented by Energy Decomposition Analysis (EDA), QTAIM, and NICS(0) calculations. Li₆ acts as a structurally rigid platform (RMSD < 0.032 Å) where ligand-induced polarization progressively strengthens its σ-aromaticity (NICS(0) from −2.917 to −13.98 ppm) and increases the HOMO–LUMO gap up to 5.05 eV. EDA identifies the binding as field-activated physisorption, electrostatically dominated (65–67%) and mechanistically distinct from Kubas coordination, as confirmed by QTAIM closed-shell interaction parameters. Negative cooperativity governs an effective loading capacity of n = 2 molecules under cryogenic conditions (T_eq = 143.76 and 114.64 K), while an entropic bottleneck renders higher loading non-spontaneous at all temperatures. These results establish Li₆(H₂)_n as a foundational gas-phase reference, providing a systematic, contamination-free descriptor set for the intrinsic Li–H₂ interaction. This framework is essential for isolating the electronic role of the lithium superatom and unambiguously identifying substrate-induced modulations in supported hydrogen storage materials. Full article

Nabkhas are a common type of biogenic aeolian landform in arid and semi-arid regions. Their morphological characteristics, surface sediment grain size composition, and spatial distribution patterns can, to some extent, be associated with the interactions between vegetation and the aeolian environment. In this study, nabkhas formed around Caragana tibetica shrubs in the desert steppe of Damao Banner, Inner Mongolia, were selected as the research object. Based on field investigations, UAV image identification, grain size analysis, and spatial point pattern analysis, the characteristics of nabkhas were comparatively analyzed among a control plot without shrubs (CK) and three shrub-covered plots: a low coverage plot (LCP), a medium coverage plot (MCP), and a high coverage plot (HCP). The results showed that (1) some morphological parameters of nabkhas varied among plots with different vegetation cover, but the responses of various indicators were not entirely consistent. The MCP exhibited relatively higher values in indicators such as shrub long axis (L_g), short axis (W_g), and windward slope length (L_y). (2) The surface sediments of nabkhas were mainly composed of silt and fine sand, followed by very fine sand. Compared with the CK, the silt content was generally lower in the shrub-covered plots, whereas the contents of fine sand and very fine sand were higher. The mean grain size (Mz, Φ value) tended to decrease, while the skewness (SK_G) and kurtosis (K_G) tended to increase, and the sorting coefficient (σ_G) showed relatively limited variation. (3) In the LCP, MCP, and HCP, the fractal dimension (D) was significantly positively correlated with the Mz and σ_G (p < 0.05), and significantly negatively correlated with the SK_G and K_G (p < 0.01), suggesting that the D may be associated with variations in sediment grain size structure. (4) Overall, the nabkhas around Caragana tibetica shrubs exhibited a spatial distribution pattern characterized by aggregation at small scales and randomness at large scales, with small-scale clustering being more evident in the MCP and HCP. In general, nabkhas around Caragana tibetica shrubs under different vegetation cover conditions showed observable differences in morphological characteristics, surface sediment grain size composition, and spatial distribution patterns, providing a comparative case reference for the study of nabkhas in desert steppe areas. Full article

Achieving selective cleavage of specific chemical bonds using ultrafast laser pulses remains a central challenge in ultrafast strong-field molecular physics. Here, we theoretically investigate the coherent control of strong-field dissociation of the heteronuclear molecular ion HD⁺ initially prepared in vibrationally excited states driven by an ultrashort pulse with a quadratic spectral phase. Our results reveal a pronounced sensitivity of both the total dissociation probability and the branching ratio (H⁺ + D vs. H + D⁺) to the chirp rate of the laser pulse. To uncover the underlying physical mechanism, we analyze the population dynamics in the coupled $1s\sigma$ and $2p\sigma$ electronic states and identify pronounced Rabi oscillations arising from the coherent interplay between multiphoton excitation and field-induced stimulated emission. By tuning the laser chirp rate, these oscillations can be suppressed via quantum interference, thereby reshaping the dissociation dynamics and significantly enhancing the dissociation probability of the H + D⁺ channel. These findings demonstrate that spectral-phase engineering provides a robust and versatile strategy for selective control of branching ratios in strong-field molecular dissociation. Full article

There are some 300 naturally occurring nuclides. In addition, over 3000 radioactive isotopes have become known. The s(low) and r(apid) processes of neutron capture synthesize the nuclides heavier than iron. The synthesis, namely the increase in the atomic numbers Z, is actually governed by $\beta$ decays. A “flow” of successive neutron captures in the chart of the nuclides is intercepted by a nucleus whose $\beta$ decay half-life is short enough. In this review, I discuss the s-process exclusively. The neutron capture rate to be compared with the $\beta$ decay rate is represented by $\lambda =n_{\mathrm{n}}{v}_T<\sigma >$, where $n_{\mathrm{n}}$ is the neutron number density, $v_T$ is the neutron thermal velocity at the temperature T, and $<\sigma >$ is the Maxwellian averaged (around $v_T$) radiative neutron capture cross-section, which depends on the nucleus of interest. The classical analysis of the solar system abundances of nuclides leads to canonical combinations like $n_{\mathrm{n}}\sim {10}^8/{\mathrm{cm}}^3$ and $T\sim 3\times {10}^8$ K for the s-process. The s-process flow becomes intricate when the neutron capture and $\beta$ decay timescales are comparable, causing a branch of the flow. Subsequently, an evaluation of $\beta$ decay rates is required, which is difficult to do straightforwardly. In this review, I will discuss the historical developments and the current status of predicting $\beta$ decay rates under s-process environments (specified basically by temperature, density, and composition). Those conditions are inaccessible in the laboratory. Embedded in high-temperature environments, even a very massive atomic species could be highly ionized, and its atomic and nuclear excited states could be thermally populated. I will exemplify the consequent difficulties of $\beta$ decay rate evaluations for s-process studies. Full article

Metal mesh reflective surfaces are widely used in deployable antennas mounted on satellites where lightweight and stowability are required; however, quantitative characterization of reflective performance is difficult due to complex woven/knitted structures. This paper presents a modeling method that characterizes the reflection coefficient of complex mesh fabrics by combining a per-band effective wire radius r_eff estimation procedure with the Casey surface impedance model. The lattice spacing is fixed from the specimen geometry, the electrical conductivity is set to the material property of gold ($\sigma$ = 45.2 MS/m), and r_eff is determined as a single parameter that minimizes the error against the measured reflection coefficient in each frequency band. For validation, waveguide-contact measurements were performed on three Atlas-series mesh specimens fabricated with gold-coated molybdenum wire (diameter: 30 μm), measuring each specimen across all three waveguide standards (WR-340, WR-90, WR-28) with nine repeated trials per configuration, totaling 162 measurement runs. The estimated r_eff ranged from 10.1 to 44.5 μm depending on band and polarization, with RMSE below 0.021 dB in all native-band fits. Even for the same specimen, directional r_eff values differed by up to 1.78× due to the anisotropy of the weave structure, confirming that polarization dependence must be considered in mesh reflector antenna design. Full article

The automatic extraction of seismic reflection events is fundamental to seismic interpretation and structural identification in oil and gas exploration, particularly for large-scale regional surveys and preliminary basin-scale assessments. Although the B-COSFIRE (Bar-Combination of Shifted Filter Responses) method has demonstrated strong capability in detecting ridge-like structures, its application in large-scale seismic processing is limited by high computational cost and complex filter bank configuration. Conventional edge detectors such as the Canny operator are computationally efficient but often produce fragmented and noise-sensitive results in low signal-to-noise ratio (SNR) seismic data because they rely solely on local gradient information and ignore the spatial continuity of geological horizons. To overcome these limitations, this study proposes a lightweight and computationally efficient framework for rapid seismic event extraction. The method simplifies the B-COSFIRE architecture by replacing its configurable filter bank with a Difference-of-Gaussian (DoG) operator, which enhances ridge-like reflection features while suppressing background interference through a center–surround mechanism. Furthermore, a Spatial Consistency Constraint (SCC) module is introduced to enforce lateral continuity using directional morphological closing operations. This strategy reconstructs disrupted reflection segments and converts isolated detection responses into spatially coherent linear structures. Adaptive thresholding and skeletonization are then applied to obtain single-pixel-wide reflection contours suitable for geological interpretation and regional structural analysis. The proposed method was evaluated using both synthetic seismic models (Ricker wavelet convolution with Gaussian noise, σ = 0.15) and real post-stack seismic profiles characterized by low SNR conditions. Experimental results demonstrate that the proposed method achieves a Precision of 0.9527, Recall of 1.0000, and F1-score of 0.9758 on synthetic data, outperforming both the standard Canny detector (F1: 0.8972) and B-COSFIRE (F1: 0.7311). The Continuity Index reaches 261.00 pixels, substantially higher than Canny (223.67 pixels) and B-COSFIRE (66.86 pixels). Notably, B-COSFIRE exhibits a severely imbalanced detection profile (Precision: 0.5762, Recall: 1.000), indicating excessive false positives that undermine its practical utility. The proposed method additionally achieves the lowest runtime (0.024 s per profile), representing a 44× speedup over B-COSFIRE (1.039 s), while requiring no training data. Overall, the proposed framework provides a practical and efficient solution for automated seismic event extraction. With only a small number of geologically interpretable parameters and strong robustness across different datasets, the method is well-suited for large-scale seismic data processing and preliminary structural assessment in underexplored regions, enabling rapid first-pass evaluation of extensive survey areas before detailed interpretation and reservoir characterization. These characteristics make the method particularly suitable for computer-assisted interpretation workflows in industrial oil and gas exploration. Unlike prior approaches that treat seismic event extraction as a generic edge detection problem, the proposed framework explicitly encodes geological prior knowledge—specifically, the lateral continuity of stratigraphic interfaces—as a morphological constraint, bridging the gap between image processing methodology and geophysical interpretation requirements. Full article

Theta function identities play an important role in the theory of modular forms and partitions, with Ramanujan’s q-series providing a fundamental analytical framework. This work derives several new identities of theta functions at level 16 by employing classical transformation techniques from the theory of q-series initiated by Ramanujan. Four main theorems are proved, yielding explicit algebraic relations involving the functions $\varphi \left(q\right)$, $\psi \left(q\right)$, and their associated transforms. These identities are applied to partition theory through the introduction of colored partition functions ${\sigma }_i\left(m\right)$ defined by arithmetic conditions modulo 16. The resulting formulas provide direct combinatorial interpretations of the analytic identities and highlight connections between theta functions, modular forms, and partition functions. The results offer a unified analytic combinatorial framework at level 16 and contribute to the study of modular congruences and related arithmetic structures. Full article

This paper develops a rigorous inferential framework for a class of Gaussian stochastic processes driven by white noise with constant drift, whose temporal evolution is governed by a Caputo fractional derivative of order ${\displaystyle \alpha \in (1/2,1)}$. The model belongs to the family of fractional Volterra processes, where memory is generated by the dynamics themselves rather than by correlated noise. We derive explicit analytical expressions for the mean, variance, and covariance structure of the solution, thereby characterizing in a precise manner how the fractional order ${\displaystyle \alpha }$ governs both variance growth and the strength of temporal dependence. In particular, the process exhibits correlated increments and a power-law variance scaling of order ${\displaystyle t^{2\alpha -1}}$, highlighting the dual role of ${\displaystyle \alpha }$ as a regularity and memory parameter. Building on this structural analysis, we address the statistical problem of estimating the parameter vector ${\displaystyle (\mu ,\sigma ,\alpha )}$ from discrete-time observations. Two complementary procedures are proposed for the estimation of the fractional order: a variance-growth method based on log–log regression of empirical variances, and a wavelet-based estimator exploiting multi-scale scaling properties of the process. For the drift and diffusion parameters ${\displaystyle (\mu ,\sigma )}$, we construct explicit Gaussian pseudo-maximum likelihood estimators derived from the Volterra covariance structure of the increment process. We establish unbiasedness, ${\displaystyle L^2}$-convergence, strong consistency, and asymptotic normality for all estimators. Furthermore, we derive Berry–Esseen type bounds that quantify the rate of convergence toward the Gaussian law, providing sharp distributional approximations in a genuinely fractional and non-Markovian setting. A Monte Carlo study is carried out, using high-resolution Volterra discretizations, large-scale simulation budgets, covariance-structured linear algebra, and multi-scale diagnostic tools. The numerical experiments confirm the theoretical convergence rates, demonstrate the finite-sample reliability of the estimators, and illustrate the sensitivity of the process dynamics to the fractional order ${\displaystyle \alpha }$: smaller values of ${\displaystyle \alpha }$ produce stronger memory effects and higher variability, while values closer to one lead to smoother and more stable trajectories. The proposed methodology unifies statistical inference for long-memory Gaussian processes with fractional differential stochastic dynamics, offering a coherent analytical and computational framework applicable in areas such as quantitative finance, anomalous diffusion in physics, hydrology, and engineering systems with hereditary effects. Full article

Micro-scale counter-rotating wind turbines (CRWTs) offer enhanced potential for wake energy recovery. This study proposes an integrated cascade–coupling design framework for high-solidity CRWTs, in which rear rotor geometry and rotor coupling are co-designed based on stereoscopic particle image velocimetry measurements of the front rotor wake. Experiments are conducted at a tip-speed ratio of $\lambda =1.0$, solidity $\sigma =1.25$, spacing ratios of $d=0.6R_T$, $1.0R_T$, and $3.0R_T$, and a tip radius of $R_T=70$ mm. At the physical limit spacing of $d=0.6R_T$, the integrated design increases the system power coefficient by 24.1% while limiting front rotor power reduction to 17.2%, compared to a 10.3% system gain and 34.5% front rotor suppression for the baseline mirrored configuration. Wake measurements confirm near-complete absorption of rotational kinetic energy from the front rotor wake without exacerbating upstream interference. These results demonstrate that cascade-based energy extraction and coupling-based interference mitigation can operate synergistically, enabling compact, high-performance micro-scale CRWTs suitable for space-constrained and urban energy applications. Full article

Understanding how Bitcoin mining is distributed across countries is important for evaluating both the sustainability and resilience of the network. In this study, we examine the evolution of total Bitcoin electricity consumption alongside the geographic distribution of Bitcoin mining. Data are provided by the Cambridge Centre for Alternative Finance (Licensed under CC BY–NC–SA 4.0): Annual data from the Cambridge Bitcoin Electricity Consumption Index (2010–2025) and a monthly panel of country-level Bitcoin hashrate shares for 105 countries (September 2019–January 2022). To assess the degree of decentralization in the global mining network, we employ entropy-based measures, inequality indices, and panel convergence tests. The results indicate that total electricity consumption grew exponentially during the early years of Bitcoin, but later transitioned to a more stable and approximately linear path. Country-level permutation entropy reveals highly volatile and dynamic mining trajectories. The Theil index shows that cross-sectional inequality declines over time, while increasing symbolic entropy reflects a progressively more even cross-country distribution of mining activity. Further evidence from $\sigma$-convergence supports a statistically significant reduction in cross-country dispersion of mining shares. Dynamic panel fixed-effects estimates reveal mean-reverting behavior in relative country shares, consistent with stochastic convergence. Finally, Phillips–Sul analysis points to heterogeneous early transition paths but ultimately supports convergence toward a single global club. The gradual geographical decentralization occurs alongside persistent core–periphery asymmetries in long-run mining shares. Overall, our findings suggest that Bitcoin mining behaves as a globally integrated industry in which computational capacity reallocates rapidly across countries in response to economic and regulatory conditions. Full article

Rapid and robust trajectory planning for the Terminal Area Energy Management (TAEM) phase of horizontal-landing Reusable Launch Vehicles (RLVs) is critical but challenging due to large initial deviations, stringent terminal constraints, and strong model nonlinearities. To address the limitations of existing methods in convergence reliability and computational speed, this paper proposes a novel online trajectory optimization framework based on analytical lateral planning and equivalent dynamic decoupling. First, a cubic Bézier curve is employed to parameterize the lateral ground track, enabling the rapid generation of analytical expressions for the lateral states that strictly satisfy boundary constraints. Leveraging these analytical solutions, the original six-degree-of-freedom dynamics are exactly decoupled and reduced to a lower-dimensional model governing only the longitudinal motion. To further mitigate nonlinearity, the third derivative of height with respect to range is introduced as a virtual control variable, transforming the problem into a smoother form. The resulting equivalent longitudinal optimization problem is then efficiently solved using the Gauss Pseudospectral Method. Numerical simulations demonstrate that the proposed method significantly outperforms traditional approaches in computational efficiency: it generates feasible trajectories satisfying all constraints within 0.26 s (3$\sigma$ value). Furthermore, the method exhibits remarkable insensitivity to initial guesses, achieving stable convergence even with simple linear initialization. This approach provides a robust and real-time capable solution for complex TAEM trajectory optimization problems characterized by high nonlinearity and multiple constraints. Full article

Binder Jetting 3D Printing (BJ3DP) offers an effective pathway for the rapid fabrication of complex high-entropy alloy (HEA) components. In this study, the macroscopic characteristics, microstructural evolution and mechanical properties of Al_0.5Cr_0.9FeNi_2.5V_0.2 HEA green parts prepared via BJ3DP were investigated under various sintering conditions. Results showed that the relative density of the sintered parts increased significantly with temperature, transitioning from a low density (<90%) at 1300–1330 °C to near-fully dense (~98%) at 1340–1350 °C. Consequently, the mechanical properties were remarkably improved. The yield strength (σ_0.2) increased from 300 MPa to 710 MPa (a 136% increase), and the ultimate tensile strength (σ_b) rose from 310 MPa to 780 MPa (a 148% increase) as sintering temperature rose from 1300 °C to 1350 °C. Microstructural analysis revealed that at lower sintering temperatures, the alloy exhibited high porosity and a non-coherent structure composed of an FCC matrix and Cr-rich BCC phase, with Al/Ni intermetallic compounds distributed around pores. Conversely, at the final sintering stage, pore closure was achieved, and a coherent structure consisting of an FCC matrix and scale-like L1₂ precipitates was formed. Optimal mechanical properties (tensile strength ≥ 700 MPa) were achieved when sintering at 1340 °C, primarily attributed to densification and precipitation strengthening. Full article

The complex surface architecture of natural oyster reefs is widely considered to promote biological attachment, yet the underlying mechanisms and the relevance to the design of artificial reefs are not fully understood. Here, we combined field experiments, 3D surface characterization, and numerical modelling to quantify how reef-like roughness regulates biofouling development and near-wall flow around artificial substrates. Surface morphological characteristics of natural oyster reefs were first obtained by 3D scanning and used to fabricate concrete panels with simulated rough textures, while traditional smooth concrete panels served as controls. The two types of panels were simultaneously deployed in the target sea area for a hanging-panel experiment. Samples were collected after 3, 6, 9, and 12 months to track changes in biofouling communities. At each sampling time, the panel surfaces were quantified by canopy roughness ($R_C$), surface heterogeneity ($\sigma$), and fractal dimension ($D$), and these metrics were integrated into numerical simulations combined to resolve the flow field, turbulence kinetic, and near-wall shear stress around the colonized panels. The research results show that, after 12-month immersion, the mean thickness of the biofouling layer on rough and control panels reached 6.39 mm and 5.91 mm, respectively. Rough panels exhibited consistently higher $R_C$ and $\mathsf{\sigma }$ than controls, and these two parameters are strongly linearly correlated ($R^2=0.891)$. Numerical simulations reveal that increased $R_C$ enlarges the oyster settlement shear-stress window (OSSW), indicating more favorable hydrodynamic conditions for oyster settlement and growth on rough panels. Nevertheless, the hydrodynamic differences between the initial rough panels and control panels gradually diminish over time, suggesting that biological growth can progressively naturalize initially smooth substrates. These findings advance the mechanistic understanding of how small-scale roughness and biofouling co-evolve to shape oyster habitat quality and provide a quantitative basis for the eco-engineering design of artificial oyster reefs. Full article

Escherichia coli (E. coli) is a microorganism commonly found in water and food matrices, and its rapid and accurate detection is crucial for maintaining public health and ensuring food safety. However, traditional molecularly imprinted polymer (MIP) sensors often face challenges such as tedious template removal and prolonged sensing times. This study develops a label-free bacterial molecularly imprinted sensor that utilizes the synergistic effect of polypyrrole (PPy) and multi-walled carbon nanotubes (MWCNTs) to achieve highly sensitive detection of E. coli. Based on the large specific surface area and superior conductivity of MWCNTs, as well as the favorable electrochemical polymerization properties of PPy, a PPy/MWCNTs composite film was fabricated via a one-step electropolymerization process. The prepared sensor exhibited excellent kinetic characteristics, with a template removal time of only 15 min, and could be regenerated and used for subsequent detection within 30 min. Under optimized conditions, the biosensor showed a satisfactory linear response over the concentration range of 10²–10⁸ CFU/mL, with a low detection limit of 65 CFU/mL (3σ/S). Furthermore, recovery experiments conducted in tap water and lemon juice samples yielded satisfactory recoveries ranging from 87.1% to 114.8%, demonstrating the reliability and practical applicability of the proposed sensor for bacterial detection in real samples. This sensor offers advantages such as simple preparation, low material cost, and high sensitivity, providing a reliable and practical analytical platform for the rapid and reliable detection of bacteria. Full article