Search Results (84)

We demonstrate that at the rim of the photon sphere of a black hole, the quantum statistics transition takes place in any multi-particle system of indistinguishable particles, which passes through this rim to the inside. The related local departure from Pauli exclusion principle restriction causes a decay of the internal structure of collective fermionic systems, including the collapse of Fermi spheres in compressed matter. The Fermi sphere decay is associated with the emission of electromagnetic radiation, taking away the energy and entropy of the falling matter without unitarity violation. The spectrum and timing of the related e-m radiation agree with some observed short giant gamma-ray bursts and X-ray components of the luminosity of quasars and of short transients powered by black holes. The release of energy and entropy when passing the photon sphere rim of a black hole significantly modifies the premises of the information paradox at the falling of matter into a black hole. Full article

Under partial load operating conditions, vortex rope generation in the draft tube of a Francis turbine is considered one of the main reasons for hydro unit vibration. In this paper, a Francis turbine HLA551-LJ-43 in the laboratory was taken as a prototype. Numerical simulations of the entire flow passage were carried out. Four different hydro-turbines were chosen to analyze the effect of vortex suppression, which were named the prototype turbine (N-J), the turbine with J-grooves installed on its conical section (W-J), the one with extending runner cone (C), and the one that considered the J-grooves and the extending runner cone at the same time (J+C). Under the part load conditions in which the vortex rope is easily generated (0.4–0.8 times design flow Q_BEP), the spectrum characteristics of pressure fluctuation, the morphology of vortex rope, and the energy dissipation based on the entropy production theory in the draft tube were studied. The results show that the three optimized structures W-J, C, and J+C could reduce the pressure pulsation in the conical section of the draft tube, weaken the eccentricity of the vortex rope, and decrease the energy losses in the runner and draft tube. It is worth mentioning that the turbine with a J+C optimized structure had the most potent effect on vortex suppression and energy dissipation. Primarily when operating in deep partial load (DPL) conditions, the efficiency of the turbine with a J+C optimized structure was increased by 13.7% compared to the prototype turbine, and the main frequency amplitude of the pressure pulsation in the draft tube was reduced to 32% of the prototype. Full article

In the realm of underwater active target detection, the presence of reverberation is an important factor that significantly impacts the efficacy of detection. This article introduces the improved permutation entropy algorithm into the analysis of active underwater acoustic signals. Based on the significant difference between the improved permutation entropy in the frequency domain and the time domain, a frequency-domain-improved permutation entropy detection algorithm is proposed. The performance of this algorithm and the energy detection algorithm are compared and analyzed under the same conditions. The results show that the spectral entropy detector is about 2.7 dB better than the energy detector, realized via active small target signal detection under a reverberation background. At the same time, based on the characteristics of improved permutation entropy changing with the length of processed data, the short-time Fourier transform is integrated into frequency domain entropy detection to obtain distance and velocity information of the target. To validate the proposed methods, comparative analysis experiments were executed utilizing actual experiment data. The outcomes of both simulation and actual experiment data processing demonstrated that the sliding entropy feature detection method for signal spectrum has a small computational complexity and can quickly determine whether there is a target echo in the receive data. The two-dimensional entropy feature detection method for short-time signal spectra was found to effectively mitigate the impact of reverberation intensity and while enhancing the prominence of the target signal, thereby yielding a more robust detection outcome. Full article

This study explores the thermodynamic properties of doped graphene using an adapted electronic spectrum. We employed the one-electron tight-binding model to describe the hexagonal lattice structure. The dispersion relation for graphene is expressed in terms of the hopping energies using a compositional parameter that characterizes the different dopant atoms in the lattice. The focus of the investigation is on the impact of the compositions, specifically the presence of dopant atoms, on the energy spectrum, entropy, temperature, and specific heat of graphene. The numerical and analytical results reveal distinct thermodynamic behaviors influenced by the dopant composition, including topological transitions, inflection points in entropy, and specific heat divergences. In addition, the use of Boltzmann entropy and the revision of Nernst’s theorem for doped graphene are introduced as novel aspects. Full article

Background: Wiedemann–Steiner syndrome (WSS), a rare autosomal-dominant disorder caused by haploinsufficiency of the KMT2A gene product, is part of a group of disorders called chromatinopathies. Chromatinopathies are neurodevelopmental disorders caused by mutations affecting the proteins responsible for chromatin remodeling and transcriptional regulation. The resulting gene expression dysregulation mediates the onset of a series of clinical features such as developmental delay, intellectual disability, facial dysmorphism, and behavioral disorders. Aim of the Study: The aim of this study was to investigate a 10-year-old girl who presented with clinical features suggestive of WSS. Methods: Clinical and genetic investigations were performed. Whole exome sequencing (WES) was used for genetic testing, performed using Illumina technology. The bidirectional capillary Sanger resequencing technique was used in accordance with standard methodology to validate a mutation discovered by WES in all family members who were available. Utilizing computational protein modeling for structural and functional studies as well as in silico pathogenicity prediction models, the effect of the mutation was examined. Results: WES identified a de novo heterozygous missense variant in the KMT2A gene KMT2A(NM_001197104.2): c.3451C>G, p.(Arg1151Gly), absent in the gnomAD database. The variant was classified as Likely Pathogenetic (LP) according to the ACMG criteria and was predicted to affect the CXXC-type zinc finger domain functionality of the protein. Modeling of the resulting protein structure suggested that this variant changes the protein flexibility due to a variation in the Gibbs free energy and in the vibrational entropy energy difference between the wild-type and mutated domain, resulting in an alteration of the DNA binding affinity. Conclusions: A novel and de novo mutation discovered by the NGS approach, enhancing the mutation spectrum in the KMT2A gene, was characterized and associated with WSS. This novel KMT2A gene variant is suggested to modify the CXXC-type zinc finger domain functionality by affecting protein flexibility and DNA binding. Full article

Non-cooperative targets, such as birds and unmanned aerial vehicles (UAVs), are typical low-altitude, slow, and small (LSS) targets with low observability. Radar observations in such scenarios are often complicated by strong motion clutter originating from sources like airplanes and cars. Hence, distinguishing between birds and UAVs in environments with strong motion clutter is crucial for improving target monitoring performance and ensuring flight safety. To address the impact of strong motion clutter on discriminating between UAVs and birds, we propose a frequency correlation dual-SVD (singular value decomposition) reconstruction method. This method exploits the strong power and spectral correlation characteristics of motion clutter, contrasted with the weak scattering characteristics of bird and UAV targets, to effectively suppress clutter. Unlike traditional clutter suppression methods based on SVD, our method avoids residual clutter or target loss while preserving the micro-motion characteristics of the targets. Based on the distinct micro-motion characteristics of birds and UAVs, we extract two key features: the sum of normalized large eigenvalues of the target’s micro-motion component and the energy entropy of the time–frequency spectrum of the radar echoes. Subsequently, the kernel fuzzy c-means algorithm is applied to classify bird and UAV targets. The effectiveness of our proposed method is validated through results using both simulation and experimental data. Full article

To better meet communication needs, 6G proposes Integrated Satellite-Terrestrial Networks. Integrated Sensing and Communication (ISAC) is one of the key technologies of Integrated Satellite-Terrestrial Networks, which can reduce the energy consumption of the system, improve communication efficiency, and increase the utilization rate of spectrum resources. In the existing technology, the Modulated Wideband Converter (MWC) system can provide support for the miniaturization and intelligence of wireless device sensing and communication systems. Therefore, the MWC system can be used as a preliminary application of ISAC technology. However, the reconstruction effect of the conventional MWC system under the influence of noise is not stable. Therefore, we propose a signal processing optimization scheme for the MWC system based on spatial information entropy. First, the subsequent reconstruction algorithm is considered to require the dynamic and flexible processing of the sampled signals to reduce the influence of noise. Second, for the shortcomings of the original Orthogonal Matching Pursuit (OMP) algorithm, the concept of the genetic algorithm is used to optimize the algorithm by constructing the feature factor through spatial information gain and spatial information features. According to the simulation results, compared with the traditional MWC system, the scheme proposed in this paper is improved in all indicators. Full article

To accurately evaluate the skidding resistance of asphalt pavements, a texture imaging device was developed to realize the standardized acquisition of pavement images. Based on the gray-level co-occurrence matrix and multifractal theory of texture structure, the influence of segregation degree and gradation type on the texture properties of asphalt pavement was studied. Meanwhile, a comprehensive evaluation index of skidding resistance was proposed for asphalt pavement. Furthermore, the attenuation characteristics of the anti-skidding performance for asphalt mixture were explored, and the corresponding attenuation model of asphalt pavement was established. The results show that the segregation degree and gradation type significantly affected the texture parameters and anti-skidding performance of asphalt mixture. Specially, with an increase in the segregation degree of coarse aggregate, the parameters of energy, entropy, and multifractal spectrum width gradually increased, whereas the inertial moment gradually decreased. The variation range of the multifractal spectrum difference initially increased and subsequently decreased. For the texture parameters such as energy, entropy, inertial moment, and multifractal spectrum width Δα, the values of the asphalt mixture with larger nominal maximum particle were higher than those of the mixture with smaller nominal maximum particle, whereas the multifractal spectrum difference value showed the opposite law. In addition, the texture parameters of energy, entropy, and multifractal spectrum width exhibited good linear correlation with the texture depth (TD) of asphalt mixtures with various segregation levels and gradation types. The index based on the texture parameters of energy, entropy, and multifractal spectrum width effectively evaluated the skidding resistance of asphalt pavements, which showed the same trend as the TD with the increase of the abrasion number. The achievement provides an effective solution for the evaluation of skidding resistance and attenuation characteristics of asphalt mixtures. Full article

The development of a new generation of solid particle solar receivers (SPSRs) with high solar absorptivity (0.28–2.5 μm) and high infrared emissivity (1–22 μm) is crucial and has attracted much attention for the attainment of the goals of “peak carbon” and “carbon neutrality”. To achieve the modulation of infrared emission and solar absorptivity, two types of medium- and high-entropy rare-earth hexaboride (ME/HEREB₆) ceramics, (La_0.25Sm_0.25Ce_0.25Eu_0.25)B₆ (MEREB₆) and (La_0.2Sm_0.2Ce_0.2Eu_0.2Ba_0.2)B₆ (HEREB₆), with severe lattice distortions were synthesized using a high-temperature solid-phase method. Compared to single-phase lanthanum hexaboride (LaB₆), HEREB₆ ceramics show an increase in solar absorptivity from 54.06% to 87.75% in the range of 0.28–2.5 μm and an increase in infrared emissivity from 76.19% to 89.96% in the 1–22 μm wavelength range. On the one hand, decreasing the free electron concentration and the plasma frequency reduces the reflection and ultimately increases the solar absorptivity. On the other hand, the lattice distortion induces changes in the B–B bond length, leading to significant changes in the Raman scattering spectrum, which affects the damping constant and ultimately increases the infrared emissivity. In conclusion, the multicomponent design can effectively improve the solar energy absorption and heat transfer capacity of ME/HEREB₆, thus providing a new avenue for the development of solid particles. Full article

Rotating machinery fault diagnosis is of key significance for ensuring safe and efficient operation of various industrial equipment. However, under nonstationary operating conditions, the fault–induced characteristic frequencies are often time–varying. Conventional Fourier spectrum analysis is not suitable for revealing time–varying details, and nonstationary fault feature extraction methods are still in desperate need. Order spectrum can reveal the rotational–speed–related time–varying frequency components as spectral peaks in order domain, thus facilitating fault feature extraction under time–varying speed conditions. However, the speed–unrelated frequency components are still nonstationary after angular–domain resampling, thus causing wide–band features and interferences in the order spectrum. To overcome such a drawback, this work proposes a rotating machinery fault diagnosis method based on adaptive separation of time–varying components and order feature extraction. Firstly, the rotational speed is estimated by the multi–order probabilistic approach (MOPA), thus eliminating the inconvenience of installing measurement equipment. Secondly, adaptive separation of the time–varying frequency component is achieved through time–varying filtering and surrogate test. It effectively eliminates interference from irrelevant components and noise. Finally, a high–resolution order spectrum is constructed based on the average amplitude envelope of each mono–component. It does not involve Fourier transform or angular–domain resampling, thus avoiding spectral leakage and resampling errors. By identifying the fault–related spectral peaks in the constructed order spectrum, accurate fault diagnosis can be achieved. The Rényi entropy values of the proposed order spectrum are significantly lower than those of the traditional order spectrum. This result verifies the effective energy concentration and high resolution of the proposed order spectrum. The results of both numerical simulation and lab experiments confirm the effectiveness of the proposed method in accurately presenting the time–varying frequency components for rotating machinery diagnosing faults. Full article

This study explores the important role of assessing force levels in accurately controlling upper limb movements in human–computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human–computer interface technologies. Full article

Underwater acoustic technology as an important means of exploring the oceans is receiving more attention. Denoising for underwater acoustic information in complex marine environments has become a hot research topic. In order to realize the hydrophone signal denoising, this paper proposes a joint denoising method based on improved symplectic geometry modal decomposition (ISGMD) and wavelet threshold (WT). Firstly, the energy contribution (EC) is introduced into the SGMD as an iterative termination condition, which efficiently improves the denoising capability of SGMD and generates a reasonable number of symplectic geometry components (SGCs). Then spectral clustering (SC) is used to accurately aggregate SGCs into information clusters mixed-clusters, and noise clusters. Spectrum entropy (SE) is used to distinguish clusters quickly. Finally, the mixed clusters achieve the signal denoising by wavelet threshold. The useful information is reconstructed to achieve the original signal denoising. In the simulation experiment, the denoising effect of different denoising algorithms in the time domain and frequency domain is compared, and SNR and RMSE are used as evaluation indexes. The results show that the proposed algorithm has better performance. In the experiment of hydrophone, the denoising ability of the proposed algorithm is also verified. Full article

In this study, a platform for a welding experiment, used to collect input and output electrical signals, was constructed, and the algorithm for the input pulsating energy interpolation line (IPEI) was given. Experiments with MAG surface straight line welding were conducted at various voltages. Analysis of the IPEI in relation to the welding current was performed while combining real-world welding occurrences with high-speed camera images of droplet transfer. It was established that the IPEI can be employed as a characteristic parameter to assess the stability of the short-circuiting transfer process in MAG welding. The three criteria for assessing the stability were the spectrum, approximation entropy, and coefficient of variation. A comparative analysis was conducted on each of these approaches. It was determined that the most effective technique is approximation entropy. The approximation entropy of the welding current and IPEI are also highly consistent, with a correlation coefficient as high as 0.9889. Full article

We establish a statistical two-body fractal (STF) model to study the spectrum of $J/\psi$. $J/\psi$ serves as a reliable probe in heavy-ion collisions. The distribution of $J/\psi$ in hadron gas is influenced by flow, quantum and strong interaction effects. Previous models have predominantly focused on one or two of these effects while neglecting the others, resulting in the inclusion of unconsidered effects in the fitted parameters. Here, we study the issue from a new point of view by analyzing the fact that all three effects induce a self-similarity structure, involving a $J/\psi$-$\pi$ two-meson state and a $J/\psi$, $\pi$ two-quark state, respectively. We introduce modification factor $q_{TBS}$ and $q_2$ into the probability and entropy of charmonium. $q_{TBS}$ denotes the modification of self-similarity on $J/\psi$, $q_2$ denotes that of self-similarity and strong interaction between c and $\overline{c}$ on quarks. By solving the probability and entropy equations, we derive the values of $q_{TBS}$ and $q_2$ at various collision energies and centralities. Substituting the value of $q_{TBS}$ into distribution function, we successfully obtain the transverse momentum spectrum of low-$p_T$ $J/\psi$, which demonstrates good agreement with experimental data. The STF model can be employed to investigate other mesons and resonance states. Full article

This paper proposes a new method for bearing fault diagnosis using wavelet packet transform (WPT)-based signal representation and informative factor linear discriminant analysis (IF-LDA). Time–frequency domain approaches for analyzing bearing vibration signals have gained wide acceptance due to their effectiveness in extracting information related to bearing health. WPT is a prominent method in this category, offering a balanced approach between short-time Fourier transform and empirical mode decomposition. However, the existing methods for bearing fault diagnosis often overlook the limitations of WPT regarding its dependence on the mother wavelet parameters for feature extraction. This work addresses this issue by introducing a novel signal representation method that employs WPT with a new rule for selecting the mother wavelet based on the power spectrum energy-to-entropy ratio of the reconstructed coefficients and a combination of the nodes from different WPT trees. Furthermore, an IF-LDA feature preprocessing technique is proposed, resulting in a highly sensitive set of features for bearing condition assessment. The k-nearest neighbors algorithm is employed as the classifier, and the proposed method is evaluated using datasets from Paderborn and Case Western Reserve universities. The performance of the proposed method demonstrates its effectiveness in bearing fault diagnosis, surpassing existing techniques in terms of fault identification and diagnosis performance. Full article