Search Results (97)

Despite the growing use of FLNG in offshore gas development, LNG hose safety during tandem offloading remains a critical challenge. Existing studies often analyze mooring dynamics and hose mechanics separately, lacking a unified framework that integrates multiple failure modes. This fragmented approach leads to unclear safety boundaries and inadequate risk control. Therefore, this study proposes a multi-parameter safe operating zone threshold method based on coupled dynamic analysis. First, a three-dimensional time-domain dynamic analysis model is developed using OrcaFlex, which integrates the floating bodies, hoses, and mooring system into a unified coupling framework based on hydrodynamic theory, simulating the dynamic response of the offloading system under combined wind, wave, and current actions. Second, tension, bending moment, and curvature are selected as safety evaluation parameters. These three parameters correspond to the core criteria of typical failure modes, namely axial overload failure, ultimate bending failure, and local joint failure, respectively. By comparing them with their allowable values, the safety status of the hose under various operating conditions is determined. Finally, a coupled safety threshold analysis method incorporating both “sea state return period” and “operational vessel distance” is proposed. The results indicate that, at a fixed vessel distance, the dynamic response of the hose increases significantly with worsening sea states. Tension satisfies the safety factor requirements under most sea conditions. However, the bending moment first exceeds the limit starting from the 5-year return period, making it the primary failure control indicator. Curvature exceeds the limit notably under the 50-year return period and beyond, becoming the main risk source under extreme sea states. The safe operational vessel distances under different sea states are also calculated, systematically revealing the response patterns and failure sequences of tension, curvature, and bending moment of the LNG hose under combined wind, wave, and current actions. Furthermore, by integrating safety margin calculations, an operational classification standard comprising a safe zone, a warning zone, and a danger zone is proposed, along with the upper limits of safe vessel distance and operational windows for each sea state. The threshold determination method established in this paper can provide effective engineering support for FLNG offloading operation planning, hose selection, and operational risk management. Full article

Rana dybowskii is an economically important amphibian in Northeast China, valued for its medicinal oviduct (Oviductus Ranae). However, the low proportion of females in artificial culture severely restricts industrial productivity. To elucidate the molecular mechanisms of hormone-induced sex reversal, based on our previous research results, we exposed tadpoles to 17β-estradiol (E₂) or testosterone propionate (TP). We then monitored gonadal histology, conducted multi-stage transcriptome sequencing, and validated key genes via qRT-PCR. The results show that induction treatment with 40 μg·L⁻¹ E₂ and 80 μg·L⁻¹ TP resulted in 93.33% female and 100% male populations, respectively. Both hormones promoted germ cell proliferation at the undifferentiated stage and directed stable ovarian or testicular development without intersex abnormalities. Ovarian differentiation involved progressive multi-wave transcriptional reprogramming, whereas testicular differentiation exhibited a concentrated gene expression burst during gonadal maturation. Steroid hormone biosynthesis, Wnt, and PPAR pathways formed the core regulatory network. Three female-biased genes (3α-hsd, Adcy3, Rspo1) and three male-biased genes (Ptgs2, Sox9, Shbg) were identified, with their expression dynamics aligned with histological progression. This study defines the optimal parameters and molecular signatures of bidirectional sex reversal in R. dybowskii. It provides a practical foundation for sex-control breeding and offers amphibian-based evidence for the conservation and plasticity of vertebrate sex determination. Full article

High-resolution seismic reconstruction is a challenging inverse problem because field seismic traces are inherently band-limited and their high-frequency components are further degraded by source bandwidth limitations, acquisition conditions, random noise, and attenuation during wave propagation. Classical resolution enhancement methods can partially sharpen seismic events, but they usually rely on restrictive assumptions about stationarity, minimum-phase wavelets, or accurate attenuation models. In this study, we propose a structure-preserving bidirectional bandwidth translation network for seismic resolution enhancement. Instead of formulating the task as a one-way paired regression problem, the proposed approach interprets resolution enhancement as unpaired translation between low-bandwidth and high-bandwidth seismic domains. A cycle-consistent adversarial objective is combined with an SSIM-based structural constraint so that the model simultaneously improves spectral recovery, waveform fidelity, and reflector continuity. To reduce the domain gap between synthetic and field data, we further construct a hybrid training corpus by combining field-extracted wavelets with synthetic reflectivity sequences and train a lightweight one-dimensional residual generator–discriminator architecture tailored to oscillatory seismic traces. Comprehensive experiments are conducted on synthetic data, a field seismic profile, and the public SEG Open Data benchmark. In addition to comparisons with conventional deconvolution and time-varying frequency deconvolution, the manuscript reports quantitative comparisons with representative learning-based baselines, together with ablation studies, parameter sensitivity analysis, robustness evaluation under different noise levels and bandwidth settings, and computational cost analysis. The results show that the proposed method consistently achieves a favorable balance between spectral extension and structural preservation, demonstrating its potential as a practical data-driven solution for seismic resolution enhancement. Full article

Accurate spot-centroid localization is fundamental for determining optical metrics such as modulation transfer function (MTF) and effective focal length (EFL). Conventional methods struggle under non-ideal conditions—asymmetric spots, high noise, and vibration—and mid-wave infrared (MWIR) vibration has received little attention. To address these gaps, we propose multi-scale Gaussian fitting with subpixel edge reconstruction (MSGF-SER), combining image pyramid fitting, Zernike-moment edge extraction, and adaptive eccentricity-weighted fusion. Validated on simulated spots with varying SNRs and experimental sequences (visible off-axis aberration, long-wave infrared (LWIR) high-noise, MWIR micro-vibration), MSGF-SER achieved a noise-free RMSE of 0.03 pixel and 0.84 pixel at 5 dB SNR. On real MWIR vibration sequences, the Y-direction standard deviation (STD) dropped to 0.098 pixel, and the trajectory displacement variance was more than an order of magnitude lower than that of conventional methods. MTF deviations remained within 0.01, and the deviation of the measured mean EFL from the nominal focal length was better than 0.05 mm, and the STD was below 0.02 mm. These results demonstrate that MSGF-SER substantially improves centroid localization accuracy, repeatability, and smoothness under challenging conditions, providing reliable support for high-precision optical system parameter measurement. Full article

In this paper, a new class of special polynomials, called the Sheffer-type general-$\lambda$-matrix polynomials, is introduced within the framework of the monomiality principle. This family is obtained by combining the structure of Sheffer sequences with the theory of general-$\lambda$ matrix polynomials, which leads to a unified formulation encompassing several polynomial families. Fundamental properties of the proposed polynomials are established, including their generating function, explicit series representation, summation formulas, quasi-monomial structure, differential relations, and determinant representation. The proposed framework addresses an important problem in the theory of special functions: the systematic construction of matrix-valued polynomial families that simultaneously generalize both classical scalar polynomials and existing matrix polynomial hierarchies. Such a unified structure is of broad significance, with applications in quantum mechanics (wave function expansions), mathematical physics (matrix differential equations and spectral problems), approximation theory, and the study of special functions in the matrix domain. Several hybrid forms of the proposed family are derived through appropriate choices of the defining functions, which yield polynomial subclasses related to classical families such as Hermite, Laguerre, Bessel, and Poisson–Charlier polynomials. These subclasses illustrate how the proposed framework provides a systematic approach for constructing and studying generalized polynomial structures. In each case, the matrix parameter L introduces a new layer of structural richness not present in the scalar setting, enabling the modelling of phenomena governed by matrix-valued spectral data. Furthermore, a numerical and graphical investigation of selected hybrid forms is carried out using Mathematica (version 14.3, 2025; Wolfram Research, Inc.). Surface plots, distributions of complex zeros, and real-zero patterns are presented for different parameter values, highlighting the influence of the parameters on the behavior and structural characteristics of the polynomials. Full article

Background: Alcohol-related liver disease is frequently associated with systemic vascular dysfunction and increased arterial stiffness. This may contribute to adverse clinical outcomes. Modulation of the gut microbiota through fecal microbiota transplantation (FMT) has emerged as a potential therapeutic strategy in liver cirrhosis, but its influence on vascular stiffness in humans remains insufficiently characterized. Methods: This prospective study evaluated arterial stiffness in patients with alcohol-related liver cirrhosis undergoing FMT. A control group received standard care. Vascular stiffness was assessed non-invasively using an oscillometric arteriograph based on pulse wave analysis. Measurements were performed at baseline and at one and three months after FMT under standardized conditions. The main indices assessed included aortic pulse wave velocity, augmentation index, ejection duration and return time. Direct microbiome sequencing and metabolomic profiling were not performed. Results: At baseline, the study and control groups had comparable vascular stiffness profiles. Only minor differences in selected hemodynamic parameters were observed. At one month after intervention, no statistically significant differences in arterial stiffness indices were observed between groups. Longitudinal analysis within the FMT group also showed no significant changes in direct markers of arterial stiffness across the three-month follow-up period. A non-significant tendency toward reduced ejection duration was noted. Conclusions: In patients with advanced alcohol-related liver cirrhosis, FMT did not produce measurable short-term improvements in arterial stiffness. These findings suggest that short-term vascular effects of microbiota modulation may be difficult to detect in patients with advanced alcohol-related liver cirrhosis. Larger studies including earlier-stage patients, longer follow-up and direct microbiome and metabolomic assessment are needed to clarify potential vascular effects of FMT. Full article

The inversion of wind and wave parameters in high-frequency ocean remote sensing radar relies heavily on the sea echo Doppler power spectrum. However, the accuracy of parameter inversion is often compromised by radio frequency interference (RFI), which distorts the Doppler spectral power distribution. Existing RFI suppression algorithms primarily focus on enhancing the signal-to-interference-plus-noise ratio post-mitigation, while insufficient attention has been paid to the spectral power fluctuations induced by these suppression processes. To address this issue, this study proposes a narrowband RFI suppression scheme that combines inter-pulse phase agility (IPA) with orthogonal projection (OP). An optimized aperiodic sequence is used to modulate the inter-pulse phases of the transmitted waveform, thus uniformly dispersing the sea echo power across the entire Doppler spectrum. Spatial OP is then applied to suppress RFI stripes on the range-Doppler spectrum, a process in which only the sea echo samples masked by the RFI stripes are affected. Finally, phase compensation restores the sea echo coherence and disperses residual RFI power uniformly into the Doppler domain, minimizing its localized impact. Simulations and semi-synthetic tests involving real-world interference verify that the proposed scheme effectively suppresses RFI while alleviating spectral distortion in the sea-echo Doppler spectrum. Full article

We study equilibrium and stability properties of self-gravitating bosonic configurations in the nonrelativistic regime by numerically solving the nonlinear Gross–Pitaevskii–Poisson (GPP) equations system. Using an appropriate coordinate transformation, the equations are written in a dimensionless form independent of the physical model parameters, so that each configuration is determined only by the central value of the wave function. We compute sequences of stationary solutions including ground and radially excited states and identify bifurcation points between them. The virial relation is used as a diagnostic condition for equilibrium, leading to the determination of a critical central density and a maximum particle number above which no stationary solutions are found. Excited configurations satisfying the virial relation are expected to be metastable since they violate stability conditions resulting from radial perturbation analyses. From the critical particle number, we estimate the maximum stable mass and radius. For axion-like bosons with mass ${10}^{-5}$ eV, the resulting configurations have masses of the order of tens of Earth masses and meter-scale radii. Full article

To further explore the energy-saving potential of power-split hybrid electric vehicles, this paper addresses issues in traditional Radial Basis Function (RBF) neural network-based vehicle speed prediction methods, which rely solely on time-varying information from historical speed sequences of the host vehicle, leading to problems such as idle overestimation, large local prediction errors, and low prediction accuracy across different time horizons. An improved RBF neural network-based vehicle speed prediction method that integrates multi-sensor information is proposed. This method identifies the driver’s driving intention through a fuzzy inference system, extracts historical speed sequences within a fixed time window in a rolling manner, and integrates inter-vehicle motion characteristic parameters obtained through fusion of millimeter-wave radar and camera data. These multi-dimensional influencing factors are used as inputs to the RBF neural network for vehicle speed prediction. Based on this, an energy management optimization model for the vehicle is established, with the goal of optimizing fuel economy. The model predictive control (MPC) strategy is employed, and the Dynamic Programming (DP) algorithm is used to solve for the real-time optimal torque distribution among various power sources within a limited time horizon. Finally, simulation validation is conducted on the MATLAB/Simulink platform under the CHTC-B driving cycle, CCBC driving cycle, and actual road driving cycle. The results show that, compared with the traditional method adopting Radial Basis Function (RBF) neural network-based vehicle speed prediction and rule-based energy management, the proposed method improves the vehicle’s fuel economy by 4.11%. Full article

To address the vulnerability of traditional linear frequency-modulated continuous wave (FMCW) fuze to jamming due to fixed modulation parameters, this paper proposes a novel fuze waveform design scheme using chaotic code-based frequency and phase composite modulation along with a Normalized Rate-Invariant Ranging algorithm (NRIR). Leveraging the ergodicity and initial value sensitivity of the Logistic chaotic map, a dual-dimensional composite modulation system is constructed. In the frequency domain, the frequency modulation slope undergoes periodic binary variation according to chaotic states to break the signal periodicity. In the phase domain, phase encoding is implemented based on chaotic binary sequences to further improve waveform entropy and complexity, effectively destabilizing the parameter stability required for coherent jamming. To resolve the distance–Doppler coupling challenges and spectral dispersion issues caused by variable-slope modulation, the NRIR algorithm is developed. By introducing a resampling transformation operator, the non-stationary rate-varying beat frequency signal is mapped to a normalized “constant-slope” space, enabling coherent accumulation and ranging of targets. Using the ambiguity function as an analytical tool, theoretical analyses, simulation experiments, and test results demonstrate that this design scheme exhibits excellent performance in suppressing DRFM jamming and sweep-frequency jamming, providing theoretical support and technical approaches for fuze anti-jamming design. Full article

Time-resolved (TR) resonance Raman (RR) spectroscopy with continuous-wave excitation is a fundamental technique that has contributed substantially to the understanding of the structure and dynamics of retinal proteins. However, the underlying principles were developed about fifty years ago for instrumentation that is hardly in use anymore. Thus, the adaptation of the technique to the current state-of-the-art equipment is needed to satisfy the increasing demand for the spectroscopic characterization of novel retinal proteins. In this work, we focus on pump–probe TR RR experiments with a confocal spectrometer using a rotating cell. We define the parameters ensuring fresh-sample condition and the photochemical innocence of the probe beam as a prerequisite for studying retinal proteins that undergo a cyclic photoinduced reaction sequence. For the measurements of intermediate states and reaction kinetics, pump–probe experiments are required in which the two laser beams hit the flowing sample with a defined but variable delay time. An appropriate set-up for such two-beam experiments with a confocal spectrometer is proposed and tested in TR experiments of bacteriorhodopsin. The comparison with the results obtained with classical slit spectrometers using a 90-degree scattering illustrates the advantages and disadvantages of the confocal arrangement. It is shown that modern confocal spectrometers substantially decrease the spectra acquisition time but require a more demanding optical set-up. Furthermore, the extent of photoconversion by the pump beam is lower than for the 90-degree-scattering arrangement, which reduces the accuracy of kinetic measurements. Full article

The integration of sedimentological and micropaleontological data in the Zanclean and Gelasian shallow-marine deposits of the Crotone Basin (southern Italy) has allowed documentation of meter-to-decameter-scale high-frequency sequences bounded by wave-ravinement surfaces (WRSs), which in turn are composed of meter-scale sedimentological cycles, referred to as bedsets. In contrast to high-frequency sequences, bedsets have a more subtle appearance, and their boundaries exhibit limited lateral extent compared to WRSs. Moreover, the micropaleontological analyses have allowed the definition of three parameters: distal/proximal (D/P: ratio between distal and proximal benthic foraminifera); fragmentation (Fr: percentage of fragmentation of benthic foraminifera); and P/B (ratio between planktonic and benthic foraminifera). In particular, the D/P and Fr allow to recognize uncertainty intervals containing the maximum flooding surface (MFS) of high-frequency sequences, whereas the P/B documents water-depth changes. Unlike in high-frequency sequences, the D/P, Fr and P/B parameters usually do not show appreciable variations associated with bedsets, confirming that the latter are unrelated to shoreline shifts and water-depth variations, but are rather controlled by minor sediment supply and/or wave regime changes. However, in rare cases, the micropaleontological parameters seem to indicate that subtle transgressive-regressive trends and water-depth variations can also be associated with bedset deposition, alluding to a ‘grey area’ of transition between high-frequency sequences of very small scale and bedsets. Further research is, therefore, needed to constrain the boundary between sedimentology and stratigraphy. Full article

Air–ground joint jamming enables three-dimensional, distributed jamming configurations, making it effective against air–ground communication networks with complex, dynamically adjustable links. Once the jamming layout is fixed, dynamic jamming power scheduling becomes essential to conserve energy and prolong jamming duration. However, existing methods suffer from poor applicability in such scenarios, primarily due to their sparse deployment and adversarial nature. To address this limitation, this paper develops a set of mathematical models and a dedicated algorithm for air–ground communication countermeasures. Specifically, we (1) randomly select communication nodes to determine the jammer operation sequence; (2) schedule the number of active jammers by sorting transmission path losses in ascending order; and (3) estimate jamming effects using electromagnetic wave propagation characteristics to adjust jamming power dynamically. This approach formally converts the original dynamic, stochastic jamming resource scheduling problem into a static, deterministic one via cognitive certainty of dynamic parameters and deterministic modeling of stochastic factors—enabling rapid adaptation to unknown, dynamic communication power strategies and resolving the coordination challenge in air–ground joint jamming. Experimental results demonstrate that the proposed Transmission Loss Ordering Algorithm (TLOA) extends the system operating duration by up to 41.6% compared to benchmark methods (e.g., genetic algorithm). Full article

The high peak-to-average power ratio (PAPR) in classical high-speed digital data transmission systems with orthogonal frequency division multiplexing (OFDM) limits energy efficiency and communication range. This paper proposes a method for randomizing OFDM signals via frequency coding using synthesized pseudorandom sequences with improved autocorrelation properties, obtained through machine learning, to minimize PAPR in complex, non-stationary hydroacoustic channels for communicating with underwater robotic systems. A neural network architecture was developed and trained to generate codes of up to 150 elements long based on an analysis of patterns in previously found best short sequences. The obtained class of OFDM signals does not require regular and accurate estimation of channel parameters while remaining resistant to various types of impulse noise, Doppler shifts, and significant multipath interference typical of the underwater environment. The attained spectral efficiency values (up to 0.5 bits/s/Hz) are relatively high for existing hydroacoustic communication systems. It has been shown that the peak power of such multi-frequency information transmission systems can be effectively reduced by an average of 5–10 dB, which allows for an increase in the communication range compared to classical OFDM methods in non-stationary hydrological conditions at acceptable bit error rates (from 10⁻² to 10⁻³ and less). The effectiveness of the proposed methods of randomization with synthesized codes and frequency coding for OFDM signals was confirmed by field experiments at sea on the shelf, over distances of up to 4.2 km, with sea waves of up to 2–3 Beaufort units and mutual movement of the transmitter and receiver. Full article

Traffic state estimation (TSE) is a core task in intelligent transportation systems (ITSs) that seeks to infer key operational parameters—such as speed, flow, and density—from limited observational data. Existing methods often face challenges in practical deployment, including limited estimation accuracy, insufficient physical consistency, and weak generalization capability. To address these issues, this paper proposes a hybrid estimation framework that integrates multi-agent reinforcement learning (MARL) with the Lighthill–Whitham–Richards (LWR) traffic flow model. In this framework, each roadside detector is modeled as an agent that adaptively learns fundamental diagram (FD) parameters—the free-flow speed and jam density—by fusing local detector measurements with global CAV trajectory sequences via an interactive attention mechanism. The learned parameters are then passed to an LWR solver to perform sequential (rolling) prediction of traffic states across the entire road segment. We design a reward function that jointly penalizes estimation error and violations of physical constraints, enabling the agents to learn accurate and physically consistent dynamic traffic state estimates through interaction with the physics-based LWR environment. Experiments on simulated and real-world datasets demonstrate that the proposed method outperforms existing models in estimation accuracy, real-time performance, and cross-scenario generalization. It faithfully reproduces dynamic traffic phenomena, such as shockwaves and queue waves, demonstrating robustness and practical potential for deployment in complex traffic environments. Full article