Search Results (30)

Molecular dynamics (MD) simulations provide atomistic insights into nucleation and crystallization in polymers, yet interpreting their complex spatiotemporal data remains a challenge. Existing order parameters face limitations, such as failing to account for directional alignment or lacking sufficient spatial resolution, preventing them from accurately capturing the anisotropic and heterogeneous characteristics of nucleation or the surface phenomena of polymer crystallization. We introduce a novel set of local order parameters—namely, directional entropy bands— that extend scalar entropy-based descriptors by capturing first-order angular moments of the local entropy field around each particle. We compare these against conventional metrics (entropy, the crystallinity index, and smooth overlap of atomic positions (SOAP) descriptors) in equilibrium MD simulations of polymer crystallization. We show that (i) scalar entropy bands demonstrate advantages compared to SOAP in polymer phase separation at single-snapshot resolution and (ii) directional extensions (dipole projections and gradient estimates) robustly highlight the evolving crystal–melt interface, enabling earlier nucleation detection and quantitative surface profiling. UMAP embeddings of these 24–30D feature vectors reveal a continuous melt–surface–core manifold, as confirmed by supervised boundary classification. Our approach is efficient and directly interpretable, offering a practical framework for studying polymer crystallization kinetics and surface growth phenomena. Full article

This study presents the electrical and optical properties of 35P₂O₅–xV₂O₅–(65–x) Sb₂O₃ glasses for 0 ≤ x ≤ 65 mol%. The direct current (DC) resistivity was measured by the Van der Pauw method and optical absorption spectra were taken in the Ultraviolet–Visible-Near-Infrared (UV–VIS–NIR) range. Electrical transport is attributed to simultaneous hopping of small polarons (SPs) between V⁴⁺ and V⁵⁺ (vanadium ion) sites and small bipolarons (SBPs) between the Sb³⁺ and Sb⁵⁺ (antimony ion) sites. The resistivity exhibits a non-linear dependence on the ionic fraction of vanadium (n_v), whereas the resistivity exhibits a minimum in the composition range 0 ≤ n_V ≤ 0.3, and a resistivity maximum was observed in the range 0.3 ≤ n_V ≤ 0.5. On further increasing n_v, the resistivity exhibits a monotonic decline. In the composition range 0 ≤ n_V ≤ 0.3, where the hopping distance between V ions decreases, while that between the Sb ions increases, the resistivity minimum has been shown to be the consequence of decreasing tunneling distance of SPs between the V⁴⁺ and V⁵⁺ ion sites. In the composition range 0.3 ≤ n_V ≤ 0.5, the resistivity, activation energy for DC conduction, glass transition temperature, and density exhibit their respective maxima even though the separation between the V⁴⁺ and V⁵⁺ sites continues to decrease. This feature is explained by enhanced localization of electrons on account of increased disorder (entropy) among the SPs and SBPs, like that of Anderson localization. This argument is further supported by a shift in the polaronic optical absorption bands associated with the SPs and SBPs toward higher energies. The transport behavior of all the glasses except the x = 0 composition has been explained by adiabatic transport, principally, by the SPs on V ions while the Sb ions contribute little to the total transport process. The results provide a clear relation between composition, polaron/bipolaron contributions, and conduction in these glasses. Full article

This work explores the physical properties of the MAX-phase material Cr₃AlC₂ through the application of density functional theory (DFT). The refined lattice parameters were determined through the minimization of the total energy. In order to explore the electronic properties and bonding features, we carried out computations on the band structure and charge density distribution. The calculated elastic constants (C_ij) validated the mechanical stability of Cr₃AlC₂. To assess the material’s ductility or brittleness, we calculated Pugh’s ratio, Poisson’s ratio, and Cauchy pressure. The hardness was determined. This study examined the anisotropic behavior of Cr₃AlC₂ using directional analyses of its elastic properties and by computing relevant anisotropy indicators. We examined several key properties of Cr₃AlC₂, including the Grüneisen parameter, acoustic characteristics, Debye temperature, thermal conductivity, melting point, heat capacity, Helmholtz free energy, entropy, and internal energy. Phonon dispersion spectra were analyzed to assess the dynamic stability of Cr₃AlC₂. Full article

In this paper we investigate the non-Markovian dynamics of a giant atom coupled to a one-dimensional photonic lattice with synthetic gauge fields. By engineering a complex-valued hopping amplitude, we break reciprocity and explore how chiral propagation and phase-induced interference affect spontaneous emission, bound-state formation, and atom–field entanglement. The giant atom interacts with the lattice at multiple, spatially separated sites, leading to rich interference effects and decoherence-free subspaces. We derive an exact expression for the self-energy and perform real-time Schrödinger simulations in the single-excitation subspace, for the atomic population, von Neumann entropy, field localization, and asymmetry in emission. Our results show that the hopping phase $\varphi$ governs not only the directionality of emitted photons but also the degree of atom–bath entanglement and photon localization. Remarkably, we observe robust bound states inside the photonic band and directional asymmetry, due to interference from spatially separated coupling points. These findings provide a basis for engineering non-reciprocal, robust, and entangled light–matter interactions in structured photonic systems. Full article

The core characteristics of autism spectrum disorder (ASD) are atypical neurodevelopmental disorders. Transcranial direct current stimulation (tDCS), as a non-invasive brain stimulation technique, has been applied in the treatment of various neurodevelopmental disorders. Entropy analysis methods can quantitatively describe the complexity of EEG signals and information transfer. This study recruited 24 children with ASD and 24 age- and gender-matched typically developing (TD) children, using multiple entropy methods to analyze differences in brain complexity and effective connectivity between the two groups. Furthermore, this study explored the regulatory effect of tDCS on brain complexity and effective connectivity in children with ASD. The results showed that children with ASD had lower brain complexity, with excessive effective connectivity in the δ, θ, and α frequency bands and insufficient effective connectivity in the β frequency band. After tDCS intervention, the brain complexity of children with ASD significantly increased, while effective connectivity in the δ and θ frequency bands significantly decreased. The results from behavioral-scale assessments also indicated positive behavioral changes. These findings suggest that tDCS may improve brain function in children with ASD by regulating brain complexity and effective connectivity, leading to behavioral improvements, and they provide new perspectives and directions for intervention research in ASD. Full article

Spatial cognition, a critical component of human cognitive function, can be enhanced through targeted training, such as virtual reality (VR)-based interventions. Recent advances in electroencephalography (EEG)-based functional connectivity analysis have highlighted the importance of network topology features for understanding cognitive processes. In this paper, a framework based on a cross fuzzy entropy network graph (CFENG) is proposed to extract spatial cognitive EEG network topological features. This framework involves calculating the similarity and symmetry between EEG channels using cross fuzzy entropy, constructing weighted directed network graphs, transforming one-dimensional EEG signals into two-dimensional brain functional connectivity networks, and extracting both local and global topological features. The model’s performance is evaluated and interpreted using an XGBoost classifier. Experiments on an EEG dataset from group spatial cognitive training validated the CFENG model. In the Gamma band, the CFENG achieved 97.82% classification accuracy, outperforming existing methods. Notably, the asymmetrically distributed EEG channels Fp1, P8, and Cz contributed most to spatial cognitive signal classification. An analysis after 28 days of training revealed that specific VR games enhanced functional centrality in spatial cognition-related brain regions, reduced information flow path length, and altered information flow symmetry. These findings support the feasibility of VR-based spatial cognitive training from a brain functional connectivity perspective. Full article

Wind speed, wind direction, humidity, temperature, altitude, and other factors affect wind power generation, and the uncertainty and instability of the above factors bring challenges to the regulation and control of wind power generation, which requires flexible management and scheduling strategies. Therefore, it is crucial to improve the accuracy of ultra-short-term wind power prediction. To solve this problem, this paper proposes an ultra-short-term wind power prediction method with MIVNDN. Firstly, the Spearman’s and Kendall’s correlation coefficients are integrated to select the appropriate features. Secondly, the multi-strategy dung beetle optimization algorithm (MSDBO) is used to optimize the parameter combinations in the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, and the optimized decomposition method is used to decompose the historical wind power sequence to obtain a series of intrinsic modal function (IMF) components with different frequency ranges. Then, the high-frequency band IMF components and low-frequency band IMF components are reconstructed using the t-mean test and sample entropy, and the reconstructed high-frequency IMF component is decomposed quadratically using the variational modal decomposition (VMD) to obtain a new set of IMF components. Finally, the Nons-Transformer model is improved by adding dilated causal convolution to its encoder, and the new set of IMF components, as well as the unreconstructed mid-frequency band IMF components and the reconstructed low-frequency IMF, component are used as inputs to the model to obtain the prediction results and perform error analysis. The experimental results show that our proposed model outperforms other single and combined models. Full article

Its outstanding mechanical and thermodynamic characteristics make SrAl₂O₄ a highly desirable ceramic material for high-temperature applications. However, the effects of elevated pressure on the structural and other properties of SrAl₂O₄ are still poorly understood. This study encompassed structural, elastic, electronic, dynamic, and thermal characteristics. Band structure calculations indicate that the direct band gap of SrAl₂O₄ is 4.54 eV. In addition, the Cauchy pressures provide evidence of the brittle characteristics of SrAl₂O₄. The mechanical and dynamic stability of SrAl₂O₄ is evident from the accurate determination of its elastic constants and phonon dispersion relations. In addition, a comprehensive analysis was conducted of the relationship between specific heat and entropy concerning temperature variations. Full article

The magnetic properties, band structure results, and magnetocaloric effect of GdCo_1.8M_0.2 with M = Fe, Mn, Cu, and Al are reported. The band structure calculations demonstrate that all the samples have a ferrimagnetically ordered ground state, in perfect agreement with the magnetic measurements. Calculated magnetic moments and variation with the alloy composition are strongly influenced by hybridisation mechanisms as sustained by an analysis of the orbital projected local density of states. The XPS measurements reveal no significant shift in the binding energy of the investigated Co core levels with a change in the dopant element. The Co 3s core-level spectra gave us direct evidence of the local magnetic moments on Co sites and an average magnetic moment of 1.3 µ_B/atom was found, being in good agreement with the theoretical estimation and magnetic measurements. From the Mn 3s core-level spectra, a value of 2.1 µ_B/Mn was obtained. The symmetric shapes of magnetic entropy changes, the Arrott plots, and the temperature dependence of Landau coefficients clearly indicate a second-order phase transition. The relative cooling power, RCP(S), normalized relative cooling power, RCP(∆S)/∆B, and temperature-averaged entropy change values indicate that these compounds could be promising candidates for applications in magnetic refrigeration devices. Full article

The purpose of this study was to identify changes in the temporomandibular joint disc affected by effusion by using texture analysis of magnetic resonance images (MRIs). Methods: A total of 223 images of the TMJ, 42 with joint effusion and 181 without, were analyzed. Three consecutive slices were then exported to MaZda software, in which two oval ROIs (one in the anterior band and another in the intermediate zone of the joint disc) were determined in each slice and eleven texture parameters were calculated by using a gray-level co-occurrence matrix. Spearman’s correlation coefficient test was used to assess the correlation between texture variables and to select variables for analysis. The Mann–Whitney test was used to compare the groups. Results: The significance level was set at 5%, with the results demonstrating that there was no high correlation between the parameter directions. It was possible to observe a trend between the average parameters, in which the group with effusion always had smaller values than the group without effusion, except for the parameter measuring the difference in entropy. Conclusion: The trend towards lower overall values for the texture parameters suggested a different behavior between TMJ discs affected by effusion and those not affected, indicating that there may be intrinsic changes. Full article

The chalcogenides of p-block elements constitute a significant category of materials with substantial potential for advancing the field of electronic and optoelectronic devices. This is attributed to their exceptional characteristics, including elevated carrier mobility and the ability to fine-tune band gaps through solid solution formation. These compounds exhibit diverse structures, encompassing both three-dimensional and two-dimensional configurations, the latter exemplified by the compound In₂Se₃. Sesqui-chalcogenides were synthesized through the direct reaction of highly pure elements within a quartz ampoule. Their single-phase composition was confirmed using X-ray diffraction, and the morphology and chemical composition were characterized using scanning electron microscopy. The compositions of all six materials were also confirmed using X-ray photoelectron spectroscopy and Raman spectroscopy. This investigation delves into the thermodynamic properties of indium and gallium sesqui-chalcogenides. It involves low-temperature heat capacity measurements to evaluate standard entropies and Tian–Calvet calorimetry to elucidate the temperature dependence of heat capacity beyond the reference temperature of 298.15 K, as well as the enthalpy of formation assessed from DFT calculations. Full article

The NbTiAlZrHfTaMoW refractory high-entropy alloy (RHEA) system with the structure of the B2 matrix (antiphase domains) and antiphase domain boundaries was firstly developed. We conducted the mechanical properties of the RHEAs at 298 K, 1023 K, 1123 K, and 1223 K, as well as typical deformation characteristics. The RHEAs with low density (7.41~7.51 g/cm³) have excellent compressive-specific yield strength (${\mathsf{\sigma }}_{\mathrm{YS}}$/$\mathsf{\rho }$) at 1023 K (~131 MPa·cm³/g) and 1123 K (~104.2 MPa·cm³/g), respectively, which are far superior to most typical RHEAs. And, they still keep appropriate plastic deformability at room temperature (ε > 0.35). The superior specific yield strengths are mainly attributed to the solid solution strengthening induced by the Zr element. The formation of the dislocation slip bands with [111](10$\stackrel{\_}{1}$) and [111](11$\stackrel{\_}{2}$) directions and their interaction provide considerable plastic deformation capability. Meanwhile, dynamic recrystallization and dislocation annihilation accelerate the continuous softening after yielding at 1123 K. Full article

Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD. Full article

Objective: Phase transfer entropy ($T{E}^{\theta }$) methods perform well in animal sensory–spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. Method: This paper proposes the performance baseline of the $T{E}^{\theta }$ methods. Specifically, four $T{E}^{\theta }$ methods are applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the $T{E}^{\theta }$ methods in identifying the directional coupling. Then, the most suitable method is selected based on the performance baseline and used on the local field potential recorded from pigeons to detect the interaction between the hippocampus (Hp) and nidopallium caudolaterale (NCL) in visual–spatial associative learning. Results: (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The $T{E}^{\theta }$ method identifies an information flow preferentially from Hp to NCL of pigeons at the $\mathsf{\theta }$ band (4–12 Hz) in visual–spatial associative learning. Significance: These outcomes provide a reference for the $T{E}^{\theta }$ methods in detecting the interactions between brain areas. Full article

D-band (110–170 GHz) is a promising direction for the future of 6th generation mobile networks (6G) for high-speed mobile communication since it has a large available bandwidth, and it can provide a peak rate of hundreds of Gbit/s. Compared with the traditional electrical approach, photonics millimeter wave (mm-wave) generation in D-band is more practical and effectively overcomes the bottleneck of electrical devices. However, long-distance D-band wireless transmission is still limited by some key factors such as large absorption loss and nonlinear noises. Deep neural network algorithms are regarded as an important technique to model the nonlinear wireless behavior, among which the study on complex-value equalization is critical, especially in coherent detection systems. Moreover, probabilistic shaping is useful to improve the transmission capacity but also causes an imbalanced machine learning issue. In this paper, we propose a novel complex-valued neural network equalizer coupled with balanced random oversampling (ROS). Thanks to the adaptive deep learning method for probabilistic shaping-quadrature amplitude modulation (PS-QAM), we successfully realize a 135 GHz 4Gbaud PS-16QAM with a shaping entropy of 3.56 bit/symbol wireless transmission over 4.6 km. The bit error ratio (BER) of 4Gbaud PS-16QAM can be decreased to a soft-decision forward error correction (SD-FEC) with a 25% overhead of 2 × 10⁻². Therefore, we can achieve a net rate of an 11.4 Gbit/s D-band radio-over-fiber (ROF) delivery over 4.6 km air free wireless distance. Full article