Search Results (347)

This study proposes an L1 discretization scheme (an accurate second-order finite difference method) for time-fractional diffusion equations involving the Caputo-type Erdélyi–Kober operator, which models anomalous diffusion. Our key contributions include the following: (i) reformulation of the original problem into an equivalent fractional integral equation to facilitate analysis; (ii) development of a novel L1 scheme for temporal discretization, which is rigorously proven to realize second-order accuracy in time; (iii) derivation of positive definiteness properties for discrete kernel coefficients; (iv) discretization of the spatial derivative using the classical second-order centered difference scheme, for which its second-order spatial convergence is rigorously verified through numerical experiments (this results in a fully discrete scheme, enabling second-order accuracy in both temporal and spatial dimensions); (v) a fast algorithm leveraging sum-of-exponential approximation, reducing the computational complexity from $O\left(N^2\right)$ to $O(NlogN)$ and memory requirements from $O\left(N\right)$ to $O(logN)$, where N is the number of grid points on a time scale. Our numerical experiments demonstrate the stability of the scheme across diverse parameter regimes and quantify significant gains in computational efficiency. Compared to the direct method, the fast algorithm substantially reduces both memory requirements and CPU time for large-scale simulations. Although a rigorous stability analysis is deferred to subsequent research, the proven properties of the coefficients and numerical validation confirm the scheme’s reliability. Full article

Recently, a new class of magnetic material, termed altermagnets, has caught the attention of the magnetism and spintronics community. The magnetic phenomenon arising from these materials differs from traditional ferromagnetism and antiferromagnetism. It generally lacks net magnetization and is characterized by unusual non-relativistic spin-splitting and broken time-reversal symmetry. This leads to novel transport properties, such as the anomalous Hall effect, the crystal Nernst effect, and spin-dependent phenomena. Spin-dependent phenomena such as spin currents, spin-splitter torques, and high-frequency dynamics emerge as key characteristics in altermagnets. This paper reviews the main aspects pertaining to altermagnets by providing an overview of theoretical investigations and experimental realizations. We discuss the most recent developments in altermagnetism and prospects for exploiting its unique properties in next-generation devices. Full article

This study investigates the influence of chromatic dispersion on the performance of Brillouin optical time-domain analysis (BOTDA) sensors, particularly under high-pump-power conditions, where nonlinear effects become significant. By incorporating dispersion terms into the coupled amplitude equations of stimulated Brillouin scattering (SBS), we theoretically analyzed the dispersion-induced pulse broadening effect and its impact on the Brillouin gain spectrum (BGS). Numerical simulations revealed that dispersion leads to a moderate broadening of pump pulses, resulting in slight changes to BGS characteristics, including increased peak power and reduced linewidth. To explore the interplay between dispersion and nonlinearity, we built a gain-based BOTDA experimental system and tested two types of fibers, namely standard single-mode fiber (SMF) with anomalous dispersion and dispersion-compensating fiber (DCF) with normal dispersion. Experimental results show that SMF is more prone to modulation instability (MI), which significantly degrades the signal-to-noise ratio (SNR) of the BGS. In contrast, DCF effectively suppresses MI and provides a more stable Brillouin signal. Despite SMF exhibiting narrower BGS linewidths, DCF achieves a higher SNR, aligning with theoretical predictions. These findings highlight the importance of fiber dispersion properties in BOTDA design and suggest that using normally dispersive fibers like DCF can improve sensing performance in long-range, high-power applications. Full article

Molybdenum disulfide is more attractive and valuable at the molecular level due to its unique structure and exceptional properties. Here, new-type MoS₂-ring chains are constructed and theoretically investigated for relevant electronic properties influenced by the length of the chain and the bias. Different from traditional wires, our findings demonstrate that the conductance of such a new-type chain presents unusually non-exponential decay with the length of the chain, with a particularly anomalous length of seven rings, which shows stronger equilibrium conductance than a shorter four-ring chain. Multi-peaks of electron transmission and delocalized electronic states contribute such uniqueness. Mo atoms play a vital role in electron transport. Essentially, a narrower “HOMO-LUMO” (the two closest energy levels to the Fermi level of MoS₂-ring chain) gap compensates for the lower device density of states of new-type molybdenum disulfide-ring chains. The usual electronic structure of a seven-ring chain is derived from its slightly arched structure and mainly originates from interference, which is the resonance occurring between the electrodes. Noticeably, the bias could greatly enhance conductance, which could reach 1000 times more than the equilibrium conductance. At a certain bias, the conductance of a seven-ring chain even exceeds the shortest one- or two-ring chain. Furthermore, the threshold voltage (at which the maximum conductance appears) gradually decreases with the length of the chain and eventually remains at 0.7 V. The valuable negative differential resistance (NDR) effect could be found in such a molecular chain, which becomes more obvious as the length rises until the seven-ring chain reaches the peak. Our findings shed light on the relations between electronic properties and the length of a new-type molybdenum disulfide-ring chain, and provide support for such new-type chains in applications of innovative low-power and controllable electronics. Full article

This paper proposes a broadband and high-efficiency anomalous reflection device based on a dielectric catenary metasurface, addressing the bottleneck problems of low efficiency and narrow bandwidth in traditional discrete metasurfaces. By designing a silicon-based equal-strength catenary structure, the efficient control of circularly polarized light beams within a wide angular range in the infrared band has been achieved. Simulation results show that the designed metasurface exhibits excellent beam steering performance when the deflection angle reaches 65°. Furthermore, to characterize the diffraction efficiency of the metasurface within a large angular range, the results indicate that under oblique incidence (0–60°), the diffraction efficiency of the ±1st order exceeds 80%, and the undesired higher-order diffractions are significantly suppressed. This ultrahigh working efficiency is attributed to the nearly perfect polarization conversion and continuous phase profile of the dielectric catenary structure. By combining catenary optics with the low-loss properties of the dielectric material, this design provides a new solution for the design of efficient, broadband, and wide-angle planar optical devices. Full article

As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. Full article

This work presents a comprehensive mathematical framework for symmetrized neural network operators operating under the paradigm of fractional calculus. By introducing a perturbed hyperbolic tangent activation, we construct a family of localized, symmetric, and positive kernel-like densities, which form the analytical backbone for three classes of multivariate operators: quasi-interpolation, Kantorovich-type, and quadrature-type. A central theoretical contribution is the derivation of the Voronovskaya–Santos–Sales Theorem, which extends classical asymptotic expansions to the fractional domain, providing rigorous error bounds and normalized remainder terms governed by Caputo derivatives. The operators exhibit key properties such as partition of unity, exponential decay, and scaling invariance, which are essential for stable and accurate approximations in high-dimensional settings and systems governed by nonlocal dynamics. The theoretical framework is thoroughly validated through applications in signal processing and fractional fluid dynamics, including the formulation of nonlocal viscous models and fractional Navier–Stokes equations with memory effects. Numerical experiments demonstrate a relative error reduction of up to 92.5% when compared to classical quasi-interpolation operators, with observed convergence rates reaching $\mathcal{O}\left(n^{-1.5}\right)$ under Caputo derivatives, using parameters $\lambda =3.5$, $q=1.8$, and $n=100$. This synergy between neural operator theory, asymptotic analysis, and fractional calculus not only advances the theoretical landscape of function approximation but also provides practical computational tools for addressing complex physical systems characterized by long-range interactions and anomalous diffusion. Full article

In this study, we investigate the presence of the Griffiths-like anomaly in the geometrically frustrated antiferromagnet ${\mathrm{HoBaCo}}_4$${\mathrm{O}}_{7+\delta }$ and globally its absence in ${\mathrm{ErBaCo}}_4$${\mathrm{O}}_{7+\delta }$, despite only small differences in the ionic radii, f-electron occupancy, and the corresponding crystal structures of the ${\mathrm{Ho}}^{3+}$ and ${\mathrm{Er}}^{3+}$-members. Previous studies have identified the Griffiths phase in the Dy-analog, ${\mathrm{DyBaCo}}_4$${\mathrm{O}}_{7+\delta }$, suggesting certain inherent features of this class of materials that regularly give rise to such anomalies. To explore the curious disappearance of such an anomalous feature in ${\mathrm{ErBaCo}}_4$${\mathrm{O}}_{7+\delta }$, we prepared a series of compounds with varying compositions ${\mathrm{Ho}}_{1-x}$${\mathrm{Er}}_x$${\mathrm{BaCo}}_4$${\mathrm{O}}_{7+\delta }$ ($0\le x\le 1$) and systematically studied the evolution of various physical properties as a function of Er-doping. Our experimental studies, including X-ray diffraction (XRD), magnetic, X-ray absorption spectroscopy (XAS), X-ray photoelectron spectroscopy (XPS), heat capacity, and muon spin relaxation spectroscopy ($\mu$SR spectroscopy), revealed that while the Griffiths-like anomaly indeed disappears with doping at the macroscopic level, signatures of inhomogeneity are retained in ${\mathrm{ErBaCo}}_4$${\mathrm{O}}_{7+\delta }$ too, at least at the local level. Overall, our results highlight the significant role of ionic radius and local structural distortions in stabilizing the Griffiths phase in this class of systems. Full article

Nanofluids—engineered suspensions of nanometer-sized particles—have attracted significant attention due to their reportedly enhanced thermal properties, making them promising candidates for advanced heat transfer applications. However, despite extensive studies, uncertainties remain regarding the magnitude and origin of these effects, limiting their practical implementation. To address this, we present a comprehensive study on nanofluid formulations based on the commercial refrigerant HFE-7500, incorporating surfactant-stabilized dispersions of several metal oxide and nitride nanoparticles. We measured key physicochemical properties, including zeta potential, particle size, viscosity, and thermal conductivity. Our results show that while the nanofluids exhibited high stability, their particle sizes in suspension were significantly larger than the primary nanoparticle sizes measured by TEM. Notably, alumina-based suspensions demonstrated the greatest enhancement, exhibiting approximately 10–15% increases in thermal conductivity as a function of volume percentage. These surpassed the 5–10% improvements observed with other metal oxides, an effect that may be linked to their comparatively larger particle sizes. However, the observed enhancements were lower than some previously reported values that claimed anomalously high thermal conductivity increases. Furthermore, steady shear viscosity increased with particle concentration, showing enhancements of 10–20%, which suggests a potential trade-off for practical implementation. Our findings refine the understanding of nanofluid behavior in refrigerants and establish a foundation for optimizing their performance in thermal management applications. However, viscosity increases must be carefully considered when designing next-generation nanofluids for real-world use. Full article

High-Cu-content (Cu-content > 1.3 at.%) nanocrystalline alloys exhibit wide heat-treatment windows and favorable soft magnetic properties due to the presence of pre-existing α-Fe nanocrystals. By fabricating ribbons with varying thicknesses to tailor cooling rates, distinct structural characteristics were achieved in Fe₈₂B_16.5Cu_1.5 alloy ribbons. Notably, the face-centered cubic (fcc) γ-Fe phase was identified in Fe-based nanocrystalline alloys. The precipitation of the fcc γ-Fe phase originates from a phase-selection mechanism under specific cooling conditions, while its retention in the as-quenched ribbon with a thickness of 27 μm is attributed to kinetic suppression during rapid cooling and the nanoscale stabilization effect. The formation of the fcc γ-Fe phase significantly reduced the saturation flux density (B_s) and increased coercivity (H_c), concurrently destabilizing the residual amorphous matrix. By suppressing the precipitation of the γ-Fe and Fe₃B phases through precise control of ribbon thickness and annealing parameters, the alloy ribbon with a thickness of 16 μm achieved an optimal combination of B_s (1.82 T) and H_c (8.3 A/m). These findings on anomalous fcc γ-Fe phase precipitation provide novel insights into metastable phase engineering and offer structural design guidelines for alloys containing pre-existing α-Fe nanocrystals. Full article

The rapid advancements in optical communication and sensing technologies have significantly increased the demand for advanced tunable spectral systems. This study presents a dual-band terahertz transmission and manipulation approach by leveraging the topologically protected properties of valley-topological photonic crystal edge states. The designed structure facilitates the excitation of the K valley within the range of 0.851–0.934 THz and the K′ valley from 1.604 to 1.686 THz, while also demonstrating anomalous refraction and birefringence. The calculated emission angles, derived through momentum matching, enable transitions between single-wave and dual-wave emissions and allow for precise angle control. The introduction of the liquid crystal material NJU-LDn-4 enables continuous tuning of the dual-band spectral range under a varying electric field, broadening the operating frequency bands to the ranges of 0.757–0.996 THz and 1.426–1.798 THz, respectively. These findings suggest promising applications in tunable filter design, optical communication, photonic computing, optical sensing, and high-resolution imaging, particularly in novel optical devices requiring precise control over spectral characteristics and light propagation. Full article

Introducing magnetic dopants into topological insulators (TIs) provides a pathway to realizing novel quantum phenomena, including the quantum anomalous Hall effect (QAHE) and axionic states. One of the most commonly used $3d$ transition metal dopants is Mn, despite its known tendency to be highly mobile and to cause phase segregation. In this study, we present a detailed magnetotransport investigation of Mn-overdoped Bi₂Te₃ thin films using field-effect transistor architectures. Building on our previous structural investigations of these samples, we examine how high Mn content influences their electronic transport properties. From our earlier studies, we know that high Mn doping concentrations lead to the formation of secondary phases, which significantly alter weak antilocalization behavior and suppress topological surface transport. To probe the gate response of these doped films over extended areas, we fabricate field-effect transistor structures, and we observe uniform electrostatic control of conduction across the magnetic phase. Inspired by recent developments in intrinsic topological systems such as the MnTe-Bi₂Te₃ septuple-layer compounds, we explore the influence of embedded ferromagnetic chalcogenide inclusions as an alternative route to engineer magnetic topological states and potentially expand the operational temperature range of QAHE-enabled devices. Full article

This study aims to overcome the technical bottleneck of non-invasive differentiation between the rammed earth layer and pebble layer in complex shallow subsurface environments, particularly focusing on the challenge of detecting highly heterogeneous pebble layers with complex wavefield characteristics. Using the western city wall of the Baodun site (Xinjin, Sichuan, China) as a case study, we introduce a high-resolution seismic detection technique combined with controllable high-frequency seismic source excitation to investigate the response characteristics of high-frequency components and energy variations of seismic waves in different strata, thereby revealing differences in physical properties between the rammed earth layer and pebble layer. Through high-frequency data acquisition, specialized processing, and interpretative analysis of seismic data, we successfully distinguish the two strata and delineate pebble-related anomalous zones. The results also indicate that, due to complex geological conditions, the reflection and refraction patterns of seismic waves in the pebble layer are exceptionally intricate. Moreover, the interplay of abrupt seismic velocity variations, interference waves, and other contributing factors leads to pronounced heterogeneity and strong scattering characteristics in the seismic data across the time, frequency, and phase domains. This research overcomes the limitations of conventional geophysical methods and confirms the applicability of high-frequency seismic techniques to complex near-surface archaeological contexts. It provides robust scientific support for the archaeological study of the Baodun site and offers a methodological reference for subsurface mapping of pebble layer in prehistoric urban landscapes. Full article

Experimental and theoretical design on thermal and structural properties of Cu-Zn-Al alloys are established. As such, from an experimental point of view, differential thermal analysis has been performed with the help of a DSC Netzsch STA 449 F1 Jupiter calorimeter with high levels of sensitivity, and the structural analysis has been accomplished through X-ray diffraction and SEM analysis. An unusual specific property for a metallic material has been discovered, which is known as “rubber-type behavior”, a characteristic determined by micro-structural changes. From the theoretical point of view, the thermal transfer in Cu-Zn-Al is presented by assimilating this alloy, both structurally and functionally, with a multifractal, situation in which the order–disorder transitions assimilated with thermal “dynamics” of Cu-Zn-Al, are mimed through transitions from non-multifractal to multifractal curves. In such a context, the thermal expansion velocity contains both the propagation speed of the phase transformation (be it a direct one: austenitic–martensitic transformation, or an indirect one: martensitic–austenitic transformation) and the thermal diffusion speed. Then, through self-modulations of the thermal field, the Cu-Zn-Al alloy will self-structure in channel-type or cellular-type thermal patterns, which can be linked to obtained experimental data. Consequently, since the thermal conductivity becomes a function of the observation scale, and heat transfer is modified to reflect the multifractal, non-differentiable paths in the material, it leads to anomalous diffusion and complex thermal behaviors. Full article

Outlier mining constitutes an essential aspect of modern data analytics, focusing on the identification and interpretation of anomalous observations. Conventional density-based local outlier detection methodologies frequently exhibit limitations due to their inherent lack of data preprocessing capabilities, consequently demonstrating degraded performance when applied to novel or heterogeneous datasets. Moreover, the computation of the outlier factor for each sample in these algorithms results in considerably higher computational cost, especially in the case of large datasets. This paper introduces a local outlier detection method named FOLOF (FCM Objective Function-based LOF) through an examination of existing algorithms. The approach starts by applying the elbow rule to determine the optimal number of clusters in the dataset. Subsequently, the FCM objective function is employed to prune the dataset to extract a candidate set of outliers. Finally, a weighted local outlier factor detection algorithm computes the degree of anomaly for each sample in the candidate set. For the analysis, the Golden Section method was used to classify the outliers. The underlying causes of these outliers can be revealed by exploring the anomalous properties of each outlier data point through the outlier factors of each dimension property. This approach has been validated on artificial datasets, the UCI dataset, and an NBA player dataset to demonstrate its effectiveness. Full article