Search Results (74)

Background: Current cosmological fits typically assume a direct relation between cosmic time (t) and the scale factor ($a\left(t\right)$), yet this ansatz remains largely untested across diverse observations. Objectives: We (i) test whether a single power-law scaling ($a\left(t\right)\propto t^{\alpha }$) can reproduce late- and early-time cosmological data and (ii) explore whether a dynamically evolving ($\alpha \left(t\right)$), modeled as a scalar–tensor field, naturally induces directional asymmetry in cosmic evolution. Methods: We fit a constant-$\alpha$ model to four independent datasets: 1701 Pantheon+SH0ES supernovae, 162 gamma-ray bursts, 32 cosmic chronometers, and the Planck 2018 TT spectrum (2507 points). The CMB angular spectrum is mapped onto a logarithmic distance-like scale ($\mu ={log}_{10}{D}_{\ell }$), allowing for unified likelihood analysis. Each dataset yields slightly different preferred values for $H_0$ and $\alpha$; therefore, we also perform a global combined fit. For scalar–tensor dynamics, we integrate $\alpha \left(t\right)$ under three potentials—quadratic, cosine, and parity breaking (${\alpha }^{3}sin\alpha$)—and quantify directionality via forward/backward evolution and Lyapunov exponents. Results: (1) The constant-$\alpha$ model achieves good fits across all datasets. In combined analysis, it yields $H_0\simeq 70\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$ and $\alpha \simeq 1.06$, outperforming $\Lambda$CDM globally ($\Delta \mathrm{AIC}\simeq 401254$), though $\Lambda$CDM remains favored for some low-redshift chronometer data. High-redshift GRB and CMB data drive the improved fit. Numerical likelihood evaluations are approximately three times faster than for $\Lambda$CDM. (2) Dynamical $\alpha \left(t\right)$ models exhibit time-directional behavior: under asymmetric potentials, forward evolution displays finite Lyapunov exponents (${\lambda }_L\sim {10}^{-3}$), while backward trajectories remain confined (${\lambda }_L<0$), realizing classical arrow-of-time emergence without entropy or quantum input. Limitations: This study addresses only homogeneous background evolution; perturbations and physical derivations of potentials remain open questions. Conclusions: The time-scaling approach offers a computationally efficient control scenario in cosmological model testing. Scalar–tensor extensions naturally introduce classical time asymmetry that is numerically accessible and observationally testable within current datasets. Code and full data are available. Full article

Flow cytometry is a powerful and widely used tool for the analysis of various cell populations, but its capabilities are severely limited by the need to apply correction of fluorescent signals from near or similar fluorochromes when analyzing multicolor panels. Spectral flow cytometry extends the capabilities of classical cytometry by reading the full fluorescence spectrum of fluorophores and their subsequent spectral separation. This significantly increases the number of markers analyzed in a single panel and thus allows for more in-depth studies of cell populations. In the age of big data analysis, this represents a serious advantage of spectral cytometry and can significantly increase its use in scientific and clinical practice. This review describes the principle of spectral cytometry, advantages and limitations of the method, and summarizes the newest deep immunophenotyping panels developed and validated for spectral cytometry. Full article

Full-body movement involving multi-segmental coordination has been essential to our evolution as a species, but its study has been focused mostly on the analysis of one-dimensional data. The field is poised for a change by the availability of high-density recording and data sharing. New ideas are needed to revive classical theoretical questions such as the organization of the highly redundant biomechanical degrees of freedom and the optimal distribution of variability for efficiency and adaptiveness. In movement science, there are popular methods that up-dimensionalize: they start with one or a few recorded dimensions and make inferences about the properties of a higher-dimensional system. The opposite problem, dimensionality reduction, arises when making inferences about the properties of a low-dimensional manifold embedded inside a large number of kinematic degrees of freedom. We present an approach to quantify the smoothness and degree to which the kinematic manifold of full-body movement is distributed among embedding dimensions. The principal components of embedding dimensions are rank-ordered by variance. The power law scaling exponent of this variance spectrum is a function of the smoothness and dimensionality of the embedded manifold. It defines a threshold value below which the manifold becomes non-differentiable. We verified this approach by showing that the Kuramoto model obeys the threshold when approaching global synchronization. Next, we tested whether the scaling exponent was sensitive to participants’ gait impairment in a full-body motion capture dataset containing short gait trials. Variance scaling was highest in healthy individuals, followed by osteoarthritis patients after hip replacement, and lastly, the same patients before surgery. Interestingly, in the same order of groups, the intrinsic dimensionality increased but the fractal dimension decreased, suggesting a more compact but complex manifold in the healthy group. Thinking about manifold dimensionality and smoothness could inform classic problems in movement science and the exploration of the biomechanics of full-body action. Full article

Multi-view stereo techniques with traditional cameras have wide applications in robotics and computer vision for scene reconstruction. Their dependence on the visible spectrum, however, poses several limitations that radar sensing could overcome in obstructing conditions such as fog and smoke. We propose a new radar-based multi-view stereo method for scene reconstruction, which combines the power of multi-view stereo techniques with the advantages of radar sensing by extending upon our previous work in this direction, where we demonstrated a time-domain inversion approach by leveraging a set of independent radar echoes acquired at sparse locations to reconstruct the scene’s geometry. Here, we show how radar stretch processing can be incorporated into a similar geometric framework to leverage frequency-domain information. Our method fundamentally differs from classical radar imaging by utilizing an explicit geometric shape representation, allowing the imposition of shape priors and the ability to model visibility and occlusions, and a forward model based on the electric field strength density over the antenna range embedded within the deramped echo. An iterative scheme is then used to evolve an initial shape toward an optimal configuration to best explain the data. We conclude by showing the initial proof of concept for the success of this method through a set of simulated 2D experiments of increasing complexity. Full article

The inherent complexity of wind energy systems has necessitated the development of sophisticated control methodologies to optimize operational efficiency. Artificial neural networks (ANN) have emerged as a powerful tool in wind turbine applications, offering sophisticated control capabilities for addressing the intricate challenges of energy conversion. This study focuses on the critical generator control block, where precise power management is essential to maintaining system stability and preventing operational disruptions. This research introduces an innovative ANN-based Direct Power Control (DPC) approach for a Doubly fed induction generator (DFIG) integrated into a wind power system, introducing a dual-MLP approach for precise power regulation. The proposed DPC-ANN controller proved effective in mitigating current ripples and achieving a near-unity power factor, indicating substantial improvement in power quality. Moreover, the spectrum harmonic analysis revealed that the controller yielded the lowest stator current total harmonic distortion of 1.29%, significantly outperforming traditional DPC-PI (2.76%) and DPC-Classic (2.24%) approaches. The proposed technique was rigorously implemented and validated using a real-time simulator (OPAL-RT) and MATLAB/Simulink (2020–2022) environment, specifically tested under a step wind profile. The real-time experimental validation highlights the practical applicability of this approach, bridging the gap between theoretical ANN-based control and real-world wind energy system implementation. These findings reinforce the potential of intelligent control strategies for optimizing renewable energy technologies, paving the way for more efficient and adaptive wind turbine control solutions. Full article

Variable marine environmental conditions, particularly at the sea surface, present considerable challenges to cross-media laser transmission. This study simulates uplink laser transmission through a seawater–sea surface–air channel via ray tracing and Monte Carlo methods, with an emphasis on the impacts of the sea surface channel. A spatial model of the sea surface is introduced, which uses a wave spectrum and fast Fourier transform technology, and the results are compared against those of a classical statistical model. The validity and applicability of six representative wind wave spectra are assessed for their effectiveness in characterizing the optical sea surface. Among these spectra, the Elfouhaily spectrum, which is refined for low-wind conditions, can most accurately represent the optical properties of the sea surface. The simulations reveal that the spatial model captures power fluctuations due to dynamic sea surface changes. At shorter underwater transmission distances, the spatial model may induce considerable drift, thereby degrading power estimates, where the difference is about 0.9 dB compared with the statistical model. Deeper underwater transmissions can mitigate beam distortions, resulting in a decrease in normalized peak power from −114 dB to −157 dB. Additionally, the laser centroid distribution tends to be elliptical because of the distribution of the sea surface azimuth. These findings underscore the importance of incorporating spatiotemporal dynamics in modeling sea surfaces and provide insights for optimizing underwater air laser transmission links in complex marine environments. Full article

In this paper, we propose a random frequency division multiplexing (RFDM) method for multicarrier modulation in mobile time-varying channels. Inspired by compressed sensing (CS) technology which use a sensing matrix (with far fewer rows than columns) to sample and compress the original sparse signal simultaneously, while there are many reconstruction algorithms that can recover the original high-dimensional signal from a small number of measurements at the receiver. The approach choose the classic sensing matrix of CS–Gaussian random matrix to compress the signal. However, the signal is not sparse which makes the reconstruction algorithms ineffective. We take full account of the great power of deep neural networks (DNN) to detect the signal as it is an underdetermined equation. The proposed RFDM establishes a novel signal modulation and detection method to target better transmission efficiency, and the simulation results show that the proposed method can achieve good BER, offering a new research paradigm to improve the spectrum efficiency of a multi-subcarrier, multi-antenna, multi-user system. Full article

A generalized scientific review with elements of additions and clarifications has been carried out on the methods of theoretical research on the electrophysical properties of crystals with ionic–molecular chemical bonds (CIMBs). The main theoretical tools adopted are the methods of quasi-classical kinetic theory as applied to ionic subsystems relaxing in layered dielectrics (natural silicates, crystal hydrates, various types of ceramics, and perovskites) in an electric field. A universal (applicable for any CIMBs class crystals) nonlinear quasi-classical kinetic equation of theoretical and practical importance has been constructed. This equation describes, in complex with the Poisson equation, the mechanism of ion-relaxation polarization and conductivity in a wide range of polarizing field parameters (0.1–1000 MV/m) and temperatures (1–1550 K). The physical model is based on a system of non-interacting ions (due to the low concentration in the crystal) moving in a one-dimensional, spatially periodic crystalline potential field, perturbed by an external electric field. The energy spectrum of ions is assumed to be continuous. Elements of quantum mechanical theory in a quasi-classical model are used to mathematically describe the influence of tunnel transitions of hydrogen ions (protons) during the interaction of proton and anion subsystems in hydrogen-bonded crystals (HBC) on the polarization of the dielectric in the region of nitrogen (50–100 K) and helium (1–10 K) temperatures. The mathematical model is based on the solution of a system of nonlinear Fokker-Planck and Poisson equations, solved by perturbation theory methods (via expanding solutions into infinite power series in a small dimensionless parameter). Theoretical frequency and temperature spectra of the dielectric loss tangent were constructed and analyzed, the molecular parameters of relaxers were calculated, and the physical nature of the maxima of the experimental temperature spectra of dielectric losses for a number of HBC crystals was discovered. The low-temperature maximum, which is caused by the quantum tunneling of protons and is absent in the experimental spectra, was theoretically calculated and investigated. The most effective areas of scientific and technical application of the theoretical results obtained were identified. The application of the equations and recurrent formulas of the constructed model to the study of nonlinear optical effects in elements of laser technologies and nonlinear radio wave effects in elements of microwave signal control systems is of the greatest interest. Full article

Background: Monoallelic damaging variants in PHIP (MIM*612870), encoding the Pleckstrin Homology Domain Interacting Protein, have been associated with a novel neurodevelopmental disorder, also termed Chung–Jansen syndrome (CHUJANS, MIM#617991). Most of the described individuals show developmental delay (DD)/intellectual disability (ID), obesity/overweight, and variable congenital anomalies, so the condition can be considered as an ID–overweight syndrome. Case Description: We evaluated a child presenting with DD/ID and a craniofacial phenotype reminiscent of a Pitt–Hopkins syndrome (PTHS)-like condition. We performed a clinical exome analysis on his biological sample, as well as an in silico prediction of the obtained data. At the same time, we interrogated the DeepGestalt technology powered by Face2Gene (F2G), using a frontal image of the proband, and clinically reviewed the earlier CHUJANS patients. In this child, we found a novel PHIP pathogenetic variant, which we corroborated through a protein modeling approach. The F2G platform supported the initial clinical hypothesis of a PTHS-like condition, while the clinical review highlighted the lack of the main frequent CHUJANS clinical features in this child. Conclusions: The unusual clinical presentation of this novel patient resembles a PTHS-like condition. However, a novel variant in PHIP has been unexpectedly detected, expanding the phenotypic spectrum of CHUJANS. Notably, PTHS (MIM#610954), which is a different ID syndrome caused by heterozygous variants in TCF4 (MIM*610954), is not classically considered in the differential diagnosis of CHUJANS nor has been cited in the previous studies. This could support other complex diagnoses and invite further patients’ descriptions. Full article

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of “last-mile” transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060–3297 bikes/(h·m) and a pure bicycle capacity of 1538–2460 bikes/(h·m). The density–flow model calculates a pure EV capacity of 2349–2897 bikes/(h·m) and a pure bicycle capacity of 1753–2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion. Full article

The position and linewidth of an emission spectrum reflect the physical properties of the luminophor. So, keeping the spectrum from distortion is very important in its measurement. However, we find that the spectrum linewidth will be broadened when the near-infrared radiation from a sodium lamp passes through a nonselective linear absorbing filter. This counterintuitive linewidth-broadening phenomenon is obvious when the residual light power after the filter is low enough, typically lower than $2.48\times {10}^{-4} \mathsf{\mu }\mathrm{W}$. This novel linewidth-broadening effect is different from the well-known Lorentzian, Doppler, and Voigt broadening, and is likely to be more independent evidence of the discrete wavelet structure of classical plane light waves. The effect is significant in high-sensitivity spectroscopy measurements, for example streak camera spectroscopy and Raman spectroscopy experiments. In addition, this effect may also be significant for cosmological research. Full article

The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue microstructures. However, the existing methods only used the time-domain information of the RF signals for liver fibrosis assessment, and the liver region of interest (ROI) is outlined manually. In this study, we proposed an approach for liver fibrosis assessment using deep learning models on ultrasound RF signals. The proposed method consisted of two-dimensional (2D) convolutional neural networks (CNNs) for automatic liver ROI segmentation from reconstructed B-mode ultrasound images and one-dimensional (1D) CNNs for liver fibrosis stage classification based on the frequency spectra (amplitude, phase, and power) of the segmented ROI signals. The Fourier transform was used to obtain the three kinds of frequency spectra. Two classical 2D CNNs were employed for liver ROI segmentation: U-Net and Attention U-Net. ROI spectrum signals were normalized and augmented using a sliding window technique. Ultrasound RF signals collected (with a 3-MHz transducer) from 613 participants (Group A) were included for liver ROI segmentation and those from 237 participants (Group B) for liver fibrosis stage classification, with a liver biopsy as the reference standard (Fibrosis stage: F0 = 27, F1 = 49, F2 = 51, F3 = 49, F4 = 61). In the test set of Group A, U-Net and Attention U-Net yielded Dice similarity coefficients of 95.05% and 94.68%, respectively. In the test set of Group B, the 1D CNN performed the best when using ROI phase spectrum signals to evaluate liver fibrosis stages ≥F1 (area under the receive operating characteristic curve, AUC: 0.957; accuracy: 89.19%; sensitivity: 85.17%; specificity: 93.75%), ≥F2 (AUC: 0.808; accuracy: 83.34%; sensitivity: 87.50%; specificity: 78.57%), and ≥F4 (AUC: 0.876; accuracy: 85.71%; sensitivity: 77.78%; specificity: 94.12%), and when using the power spectrum signals to evaluate ≥F3 (AUC: 0.729; accuracy: 77.14%; sensitivity: 77.27%; specificity: 76.92%). The experimental results demonstrated the feasibility of both the 2D and 1D CNNs in liver parenchyma detection and liver fibrosis characterization. The proposed methods have provided a new strategy for liver fibrosis assessment based on ultrasound RF signals, especially for early fibrosis detection. The findings of this study shed light on deep learning analysis of ultrasound RF signals in the frequency domain with automatic ROI segmentation. Full article

The complex mechanical system of a rice combine harvester not only has various excitation sources, but also, the vibration transmission path between each working device and the vibration contribution characteristics to the frame are not clear, so it is difficult to perform a reduction vibration design for the sharp vibration of the rice combine harvester frame. Therefore, based on the comparison and improvement of multiple classical transfer path analysis methods, this paper analyzed the vibration transfer characteristics and transfer characteristics of each harvester by the discrete time matrix method and operating path method. In the Experimental section, through the vibration characteristic experiment firstly, this paper obtained the power spectrum variation and the most needed optimized path in the transmission path of each device under each operating condition. Secondly, through frame simulation analysis under the exciting force, we obtained the vibration damping areas that needs to be optimized. Finally, the damping optimization experiment connected with the vibration characteristic experiment, and the excitation force simulation analysis was performed. The results of the damping optimization experiment displayed that the maximum change value of the vibration acceleration of the cutting table decreased from 7.862 m·s⁻² to 3.522 m·s⁻², decreasing by 55.2%, and the peak amplitude of the multipoint test in the cab was 5.4, 5.3, 1.7 and 2.0 μm, respectively, which was significantly reduced, so the optimization effect was significant. This study provides theoretical support for the vibration reduction optimization of a rice combine harvester frame. Full article

With the number of new satellites increasing dramatically, comprehensive space surveillance is becoming increasingly important. Therefore, high-resolution inverse synthetic aperture radar (ISAR) imaging of satellites can provide an in-situ assessment of the satellites. This paper demonstrates that pseudo-noise signals can also be used for satellite imaging, in addition to classical linear frequency-modulated chirp signals. Pseudo-noise transmission signals offer the advantage of very low cross-correlation values. This, for instance, enables the possibility of a system with multiple channels transmitting instantaneously. Furthermore, it can significantly reduce signal interference with other systems operating in the same frequency spectrum, which is of particular interest for high-bandwidth, high-power systems such as satellite imaging radars. A new routine has been introduced to generate a wideband pseudo-noise signal with a peak-to-average power ratio (PAPR) similar to that of a chirp signal. This is essential for applications where the transmit signal power budget is sharply limited by the high-power amplifier. The paper presents both theoretical descriptions and analysis of the generated pseudo-noise signal as well as the results of an imaging measurement of a real space target using the introduced pseudo-noise signals. Full article

A re-examination of some early classic turbulence literature, mainly of isotropic turbulence, is given in this selective review. Some early studies, including original concepts and points, are reviewed or highlighted. Two earliest studies and six major original concepts are found: (i) Lord Kelvin’s pioneering elementary studies of homogeneous, isotropic turbulence; (ii) Kelvin’s early introduction of Fourier Principles into turbulence studies; (iii) the Kelvin elementary concept of the direct energy cascade; (iv) the Kelvin early concept of the symmetry of turbulence; (v) the Taylor concept of the coefficient of eddy viscosity; (vi) the Taylor concept of the ‘age’ of the eddy; (vii) the Taylor–Fage–Townend concept of small eddies or microturbulence or small scale turbulence; and (viii) the Obukhov concept of a function of the inner Reynolds number (i.e., $Re$ dependent coefficient) in both the balance equation and the energy distribution equation (the two-thirds law). Both Kelvin and Taylor should be regarded as the co-founders of the statistical theory of homogeneous, isotropic turbulence. The notion, ‘the Maxwell–Reynolds decomposition of turbulent flow velocity’, should be used. The Kolmogorov–Obukhov scaling laws are reviewed in detail. Heisenberg’s inverse seventh power spectrum is briefly reviewed. The implications or significances of these early studies, original concepts and points are briefly discussed, with special reference to their possible links with modern approaches and theories. Full article