Search Results (4,764)

This study demonstrates a Mach–Zehnder-type internal-state atom interferometer in a sodium F = 1 spinor Bose–Einstein condensate (BEC), which is realized by applying a three-pulse radio-frequency sequence ($\pi /2$–$\pi$–$\pi /2$) to manipulate the two magnetic sublevels $|1,-1⟩$ and $|1,0⟩$. Phase-scanning experiments show that the visibility remains at a high level across all three pulse stages ($V>0.77$). In the hold-time scanning measurements, the visibility decays exponentially with hold time, yet the system maintains good coherence. This work establishes a foundation for precision measurements based on internal-state atom interferometers, as the approach simplifies the experimental apparatus while maintaining good quantum coherence and high-contrast interference fringes. Full article

Topological semimetals with nontrivial band structures host a variety of unconventional transport phenomena and have attracted significant attention in condensed matter physics. SnTaS₂, a recently proposed topological nodal-line superconductor with a centrosymmetric layered structure, provides an ideal platform to explore the interplay between topology and electronic transport. Here, we report a comprehensive study of the normal-state magnetotransport and magneto-thermoelectric properties of SnTaS₂ single crystals. We observed large magnetoresistance and nonlinear Hall resistivity at low temperatures, which can be well described by a two-band model, indicating the coexistence of electron and hole carriers. The Seebeck and Nernst coefficients were found to exhibit pronounced and nonmonotonic magnetic field dependences at low temperatures, consistent with multiband transport behavior. Moreover, clear quantum oscillations with a single frequency are detected in both electrical and thermoelectric measurements. Analysis of the oscillations reveals a small effective mass and a nontrivial Berry phase, suggesting that the corresponding Fermi surface arises from a topologically nontrivial band. These findings shed light on the normal-state electronic structure of SnTaS₂ and highlight the important role of topological bands in shaping its transport properties. Full article

A novel ternary transition metal chalcogenide Ba₂Re₆Se₁₁, which crystallizes in the R−3c space group, was synthesized using a high-pressure and high-temperature technique. The lattice is constituted by Re₆Se₈ cube-octahedral clusters connected by additional apical Se anions via the Re-Se-Re pathway, while the Ba atoms reside in the cavities among the Re₆Se₈ units. High-pressure synchrotron X-ray diffraction measurements showed that Ba₂Re₆Se₁₁ maintains a trigonal structure up to a pressure of 60 GPa, with a bulk modulus of 193 GPa. The lattice stability is ascribed to the fully occupied valence bands of the molecular orbital of the Re₆Se₈ cluster with trivalent Re. This fully occupied orbital configuration also gives rise to the diamagnetic state of Ba₂Re₆Se₁₁, which was validated through magnetic measurements. The resistivity of Ba₂Re₆Se₁₁ is as low as several milliohm centimeters, and it follows the thermal activation mechanism at elevated temperatures and the three-dimensional variable-range hopping model at low temperatures, indicating that Ba₂Re₆Se₁₁ is a semiconductor or insulator in close vicinity to a metal–insulator transition. Full article

Bound states in the continuum (BICs) can be transformed into quasi-bound states (quasi-BICs) via intentional symmetry breaking, thereby enabling ultrahigh-Q resonances critical for refractometric sensing applications. To advance detection capabilities for organic analytes, we proposed an all-dielectric metasurface monolithically integrated within a microfluidic channel. Mirror symmetry was intentionally disrupted through a cylindrical perturbation applied to one of two identical elliptical resonators, which excited a quasi-BIC mode at 1.9591 THz with a numerically validated Q-factor of 1959. This resonance manifested an absorption peak approaching unity, featuring a full-width at half-maximum (FWHM) of merely 1 GHz. Multipolar decomposition revealed that the mode originated from a synergistic electric-quadrupole (EQ)–magnetic-dipole (MD) pair, wherein the EQ contribution exceeded the MD counterpart by 20%. Capitalizing on this high-Q resonance, the sensor attained a sensitivity of 240 GHz per refractive-index unit (GHz RIU⁻¹) and a figure of merit (FOM = S/FWHM) of 240, while demonstrating robust performance against fabrication tolerances spanning −4% to +4%. Additionally, we verified that oblique-incidence illumination could activate a quasi-BIC within the identical spectral band, circumventing the need for structural asymmetry and thus expanding operational versatility. Benefiting from its geometric simplicity and competitive performance, this architecture exhibited substantial potential for on-chip sensing of organic compounds. Full article

We consider the direct orbital effect of an external magnetic field on the motion of electrons in reciprocal space in AB-stacked bilayer graphene subjected to a perpendicular magnetic field and an external electric field. For this purpose, the Peierls substitution is implemented using the symmetric gauge for the vector potential, and the electronic dispersion is reconstructed. The Lorentz potential arising from the Lorentz force acting on the electrons is included in the calculations. The effective chemical potential method is employed to incorporate the effects of Hubbard on-site repulsion, the external electric field, and Landau quantization of the allowed electronic states in reciprocal space. Local canted antiferromagnetic states are discussed, and their coexistence with excitonic states is found. Full article

Background/Objectives: The segmentation of three-dimensional radiological images constitutes a fundamental task in medical image processing for isolating tumors from complex datasets in computed tomography or magnetic resonance imaging. Precise visualization, volumetry, and treatment monitoring are enabled, which are critical for oncology diagnostics and planning. Volumetric analysis surpasses standard criteria by detecting subtle tumor changes, thereby aiding adaptive therapies. The objective of this study was to develop an enhanced, interactive Graphcut algorithm for 3D DICOM segmentation, specifically designed to improve boundary accuracy and 3D modeling of breast and brain tumors in datasets with heterogeneous tissue intensities. Methods: The standard Graphcut algorithm was augmented with a clustering mechanism (utilizing k = 2–5 clusters) to refine boundary detection in tissues with varying intensities. DICOM datasets were processed into 3D volumes using pixel spacing and slice thickness metadata. User-defined seeds were utilized for tumor and background initialization, constrained by bounding boxes. The method was implemented in Python 3.13 using the PyMaxflow library for graph optimization and pydicom for data transformation. Results: The proposed segmentation method outperformed standard thresholding and region growing techniques, demonstrating reduced noise sensitivity and improved boundary definition. An average Dice Similarity Coefficient (DSC) of 0.92 ± 0.07 was achieved for brain tumors and 0.90 ± 0.05 for breast tumors. These results were found to be comparable to state-of-the-art deep learning benchmarks (typically ranging from 0.84 to 0.95), achieved without the need for extensive pre-training. Boundary edge errors were reduced by a mean of 7.5% through the integration of clustering. Therapeutic changes were quantified accurately (e.g., a reduction from 22,106 mm³ to 14,270 mm³ post-treatment) with an average processing time of 12–15 s per stack. Conclusions: An efficient, precise 3D tumor segmentation tool suitable for diagnostics and planning is presented. This approach is demonstrated to be a robust, data-efficient alternative to deep learning, particularly advantageous in clinical settings where the large annotated datasets required for training neural networks are unavailable. Full article

This paper presents a mathematical approach for analyzing the thermal behavior of a dual-permanent-magnet consequent-pole magnetically geared machine. The analytical method, referred to as harmonic modeling, employs a complex Fourier series and the Cauchy product to obtain solutions to the partial differential equations governing the temperature distribution in electrical machines. Unlike lumped-parameter thermal networks that provide only average quantities, the proposed approach enables the prediction of spatial temperature distributions. The machine is further investigated under various operating conditions, including different convection coefficients and loss levels. An 11-pole, 18-slot prototype was evaluated by comparison with finite element method (FEM) simulations. The results demonstrate that the proposed method agreed well with the FEM results, with errors below 10%, while requiring less than 2 s per calculation compared with approximately 20 s for FEM simulations. Full article

Extended Kalman Filters (EKFs) are widely employed for attitude estimation using Magnetic and Inertial Measurement Units (MIMUs) in battery-powered sensing systems. In such applications, energy availability influences system operation, yet battery state information is commonly treated by external supervisory mechanisms rather than being integrated into the estimation process. This work presents an EKF-based formulation in which the battery State of Charge (SOC) is explicitly included as a state variable, allowing joint estimation of attitude and energy state within a single filtering framework. SOC dynamics are modeled using a low-complexity estimator based on terminal voltage and current measurements, while attitude estimation is performed using a Simplified Extended Kalman Filter (SEKF) tailored for embedded MIMU-based applications. The proposed approach was evaluated through numerical simulations under constant and time-varying load profiles representative of low-power electronic devices. The results indicate that the inclusion of SOC estimation does not affect the attitude estimation performance of the original SEKF, while SOC estimation errors remain below 8% for the evaluated load conditions with power consumption of approximately 0.1 W, consistent with wearable and small autonomous electronic platforms. By incorporating energy state estimation directly into the filtering structure, rather than treating it as an external supervisory task, the proposed formulation offers a unified estimation approach suitable for embedded MIMU-based systems with limited computational and energy resources. Full article

Quantum neural networks leverage quantum computing to address machine learning problems beyond the capabilities of classical computing. In this study, we demonstrate a quantum neural network that learns the nonlinear exclusive OR function on a desktop quantum computer. The exclusive OR task is a nonlinear benchmark that cannot be solved by a single-layer perceptron, making it an excellent test for quantum machine learning. We trained a variational quantum circuit model in a simulation using the PennyLane framework to learn the two-bit exclusive OR mapping. After obtaining the circuit parameters in the simulation, the trained quantum neural network was deployed on a two-qubit Nuclear Magnetic Resonance-based desktop quantum computer operating at room temperature to evaluate the actual hardware performance. The experimental quantum state fidelity reached approximately 98.85%$\left(R_y\right)$ and 99.35%$\left(R_x\right)$, and the overall average purity was 95.16%$\left(R_y\right)$ and 97.43%$\left(R_x\right)$, indicating excellent agreement between the expected and measured results. These positive outcomes underscore the feasibility of quantum machine learning on small-scale quantum hardware, marking a minimal yet physically meaningful benchmark. Full article

We investigate the role of single-ion anisotropy in stabilizing higher-order skyrmion crystal phases in centrosymmetric magnets under D_3d symmetry. Using a classical spin model that incorporates both a local single-ion anisotropy arising from the two-dimensional crystal symmetry and a D_3d-type magnetic anisotropy originating from the D_3d point-group symmetry, we perform simulated annealing calculations to explore the ground-state spin configurations. We find that a skyrmion crystal with a skyrmion number of two is stabilized over a wide range of parameters of single-ion anisotropy and D_3d-type anisotropy. We also show that the skyrmion core position shifts from an interstitial site to an on-site location as the magnitude of the easy-axis single-ion anisotropy increases. Furthermore, we demonstrate that the magnetic field drives a variety of topological phase transitions depending on the sign and magnitude of the single-ion and D_3d-type anisotropies. These results provide a possible microscopic understanding of how complex topological spin textures can be stabilized in centrosymmetric D_3d magnets, suggesting that multiple phases with topological spin textures could emerge even in the absence of the Dzyaloshinskii–Moriya interaction. Full article

Conventional transformer protection schemes are limited by the difficulty in distinguishing inrush currents from internal and external faults, which restricts operational accuracy to below 70%. Existing solutions are constrained by a trade-off: sensitivity is compromised when setting values are increased, while speed is sacrificed when time delays are introduced. To address these limitations, a novel deep learning-based method for transformer fault identification is proposed. First, a feature model is constructed utilizing the time-domain sum of voltage and current fault components alongside current polarity characteristics. Subsequently, a channel attention-based Capsule Network (SE-CapsuleNet) is employed to automatically extract deep features across normal operation, inrush currents, and fault types. Simulation results demonstrate that inrush conditions are accurately differentiated from fault states. Robustness is maintained under high fault resistance (400 Ω) and 20 dB noise interference, while immunity to current transformer (CT) saturation and core residual magnetism is exhibited. The proposed protection relay simultaneously meets the requirements of rapid response, high sensitivity, and safety stability. Full article

The expanding insect farming industry generates up to 67,000 tons of frass per year. Its potential use as fertilizer is promising, but has not yet been widely studied. This study aimed to characterize the chemical composition, organic matter structure, ecotoxicity, and phytotoxicity of frass from four insect species in order to evaluate its potential as a fertilizer. We compared four types of insect frass (IF) (Tenebrio molitor, Galleria mellonella, Hermetia illucens, and Acheta domesticus) to Eisenia fetida vermicompost (EFV). We used physicochemical analyses (pH, electrical conductivity (EC), macro-micronutrients and dissolved organic carbon (DOC), spectroscopy (solid-state ¹³C nuclear magnetic resonance (NMR), and Fourier-transform infrared spectroscopy (FTIR)) and thermogravimetry/differential scanning calorimetry (TGA/DSC: R₁, R₂, Tmax), together with phytotoxicity (germination index, %GI) and ecotoxicity (toxicity units, TU) bioassays. Composition was species-dependent: A. domesticus showed the highest levels of nitrogen (N), phosphorus (P), and potassium (K); the concentration of DOC was higher in insect frass (IF) than in EFV, with the highest concentration found in IF of T. molitor. ¹³C NMR/FTIR profiles distinguished between frass (carbohydrates/proteins and chitin signals) and EFV (humified, oxidized matrix). Thermal stability followed: G. mellonella (R₁ ≈ 0.88) ≥ A. domesticus (0.79) > H. illucens (0.73) > EFV (0.67) > T. molitor (0.50). In bioassays, T. molitor and A. domesticus exhibited phytotoxicity (%GI < 30), whereas G. mellonella and H. illucens did not. EFV exhibited the highest %GI. Dilution increased %GI in all materials, especially in T. molitor and A. domesticus, and reduced acute risk (TU). Frass is not a uniform input: its agronomic performance emerges from the interaction between EC (ionic stress), the availability of labile C (DOC, C/N and low-temperature exotherms), and structural stability (R₁/R₂ and aromaticity). In terms of formulation, IF can provide nutrients that mineralize rapidly, whereas EFV contributes stability. Controlling the inclusion and dilution of materials (e.g., limiting the amount of T. molitor in blends) and considering the mixing matrix helps to manage phytotoxicity and ecotoxicity, and realize the fertilizer value of the product. Full article

A hierarchical cooperative control strategy guided by a virtual leader is proposed to enhance the speed regulation and robustness of dual permanent magnet synchronous motor (PMSM) systems. The upper layer employs a virtual leader with model predictive speed control (MPSC) to achieve coordinated tracking, while the lower layer utilizes model predictive current control (MPCC) for regulation. A theoretical complexity analysis demonstrates that this decoupled architecture reduces the computational burden by approximately 75% compared to centralized MPC. Furthermore, a load disturbance observer is designed to estimate and compensate for external torques. Simulation and experimental results, covering both forward and reverse rotations, validate the effectiveness of the proposed strategy. Comparative results show that, compared with a conventional PI controller, the proposed method reduces speed overshoot by approximately 20% under sudden load changes, exhibiting superior steady-state performance and strong robustness against load variations. Full article

The tip-of-the-tongue (TOT) phenomenon is a transient state in which speakers momentarily fail to retrieve a known word despite preserved semantic knowledge and a strong sense of imminent recall. This review integrates cognitive and neural evidence with emerging neurochemical perspectives to develop a comprehensive biomedical framework for word-finding failures. Cognitive models of semantic–phonological transmission and interloper interference have been refined through structural, functional, and metabolic imaging to elucidate the mechanisms underlying TOT states across the lifespan. Functional neuroimaging implicates a left-lateralized fronto-temporal network, particularly the inferior frontal gyrus (IFG), anterior cingulate cortex (ACC), and temporal pole, in retrieval monitoring and conflict resolution. Structural MRI and diffusion imaging link increased TOT frequency to reduced integrity of the arcuate and uncinate fasciculi and diminished network efficiency. Proton magnetic resonance spectroscopy (¹H-MRS) introduces a neurochemical dimension, with studies of related language tasks implicating lower γ-aminobutyric acid (GABA) and altered glutamate concentrations in frontal and temporal cortices as potential contributors to slower naming and heightened retrieval interference. Together, these findings converge on a model in which transient lexical blocks arise from local disruptions in excitation–inhibition (E/I) balance that impair signal propagation within language circuits. By uniting behavioral, neuroimaging, and neurochemical perspectives, TOT research reveals how subtle perturbations in cortical homeostasis manifest as everyday cognitive lapses and highlights potential biomedical strategies to maintain communicative efficiency across the lifespan. Full article

This paper proposes an event-triggered extension of duty-ratio-based model predictive direct speed control (DR-MPDSC) for permanent magnet synchronous motor (PMSM) drives in electric vehicle (EV) applications. The main contribution is the development of an event-triggered execution framework specifically tailored to DR-MPDSC, in which control updates are performed only when the speed tracking error violates a prescribed condition, rather than at every sampling instant. Unlike conventional MPDSC and time-triggered DR-MPDSC schemes, the proposed strategy achieves a significant reduction in control execution frequency while preserving fast dynamic response and closed-loop stability. An optimized duty-ratio formulation is employed to regulate the effective application duration of the selected voltage vector within each sampling interval, resulting in reduced electromagnetic torque ripple and improved stator current quality. An extended Kalman filter (EKF) is integrated to estimate rotor speed and load torque, enabling disturbance-aware predictive speed control without mechanical torque sensing. Furthermore, a unified field-weakening strategy is incorporated to ensure wide-speed-range operation under constant power constraints, which is essential for EV traction systems. Simulation and experimental results demonstrate that the proposed event-triggered DR-MPDSC achieves steady-state speed errors below 0.5%, limits electromagnetic torque ripple to approximately 2.5%, and reduces stator current total harmonic distortion (THD) to 3.84%, compared with 5.8% obtained using conventional MPDSC. Moreover, the event-triggered mechanism reduces control update executions by up to 87.73% without degrading transient performance or field-weakening capability. These results confirm the effectiveness and practical viability of the proposed control strategy for high-performance PMSM drives in EV applications. Full article