Search Results (876)

During the operation of full-ceramic bearings, defects such as cracks and spalls inevitably develop on the bearing balls. These defects reduce bearing service life and compromise the stable operation of mechanical systems. To address this issue, a 12-degree-of-freedom (DOF) dynamic model of a full-ceramic bearing-rotor system (BRS) is established, considering ball crack and spalling defects. The model incorporates variations in equivalent stiffness and contact forces induced by these two defect types. Subsequently, the proposed model is solved by the Newmark–β method. Bifurcation diagrams, time-domain waveforms, and frequency spectra are employed to investigate the system’s dynamic responses. In the frequency-domain analysis, particular attention is paid to characteristic frequency components, including the rotational frequency f_s, the ball spin frequency f_BSF, their harmonics, and combination frequencies. Finally, an experimental test platform is constructed to validate the accuracy of the developed model. The results indicate that crack and spalling defects exert distinctly different effects on the dynamic behavior of the system. Defect width has a significant quantitative influence on the vibration response. Under various defect conditions, the prediction errors of the developed model remain within an acceptable range, with the maximum relative error of 9.05%. The developed model offers a theoretical foundation for analyzing bearing dynamics and supporting fault diagnosis applications. Full article

We apply the recently proposed analytic extension of formal indicial umbral calculus to the evaluation and structural interpretation of Bessel moments, replacing formal symbolic constructions with Mellin–Barnes analytic transmutations. The classical umbral representation of $J_0$ converts products of Bessel functions into Gaussian integrals involving sums of independent symbolic operators. This formal mechanism may reproduce correct identities in suitable convergence chambers, but it may also lead to non-admissible hypergeometric expansions at the physically relevant parameter values. The cubic moment already exhibits this difficulty: the formal Appell $F_4$ expansion associated with the equilateral case lies outside its domain of convergence. We address this obstruction by replacing the formal expansion with Mellin–Barnes representations of the corresponding umbral pairings. In this formulation, Ramanujan’s Master Theorem selects the analytic ground state associated with a Bessel product. The factorisation $J_0^3=J_0{J}_0^2$ fuses the elementary Bessel state with the square state and gives the cubic moment as a one-dimensional Meijer–Barnes function. The same mechanism yields a scaled cubic formula and a fourth-moment Meijer–Barnes representation whose residues give a convergent harmonic-number expansion. The fifth moment marks the first higher-rank case: the natural grouping $J_0^5=J_0^2{J}_0^2{J}_0$ leads to a bivariate Barnes transmutation rather than to an ordinary Meijer G-function. Finally, real powers $J_0^{\alpha }$, $\alpha >2$, are interpreted through Mellin-selected ground states, which need not reduce to finite Gamma products. Thus, Bessel moments provide a concrete hierarchy of analytic umbral representations, from rank-one Meijer–Barnes functions to higher-rank Barnes structures, and distinguish the global analytic meaning of an umbral construction from the local convergence of its residue expansions. Full article

This paper presents a frequency domain harmonic and modal analysis of a four-bus Zeta DC–DC converter, targeting the identification of resonance phenomena up to 200 MHz. The method is based on nodal admittance modeling and eigenvalue decomposition of the impedance matrix, enabling extraction of modal impedance and dominant resonance modes. Participation factors and sequential current injection at each bus are employed to evaluate spatial sensitivity and voltage amplification under different excitation scenarios. A two-stage frequency sweep, combining a coarse global scan with fine local refinement around detected peaks, ensures efficient and accurate resonance characterization. Simulation results demonstrate strong dependence of resonance severity on the injection location and highlight the dominant contribution of specific modes and reactive elements. The proposed framework provides physical insight and supports resonance-aware design of power electronic converters. Full article

Grid-connected photovoltaic (PV) systems paired with battery storage are becoming a core element of low-carbon distribution networks. This paper develops a complete closed-form formulation together with an independent, simulation-based verification of a single-phase grid-connected PV-battery microgrid sized for high-irradiance residential conditions in Saudi Arabia, using measured solar-resource and tariff data for Riyadh. A 6.25 kW monocrystalline array feeds a 400 V DC link through a perturb-and-observe boost stage; a bidirectional converter couples a 13.5 kWh LiFePO₄ battery; and an IEEE 1547 smart inverter interfaces a 230 V grid through an LCL filter. Governing equations for every subsystem are derived and evaluated numerically, and a Python re-implementation of the phasor power-flow model verifies the analysis over a 24 h cycle run to periodic steady state, reproducing the reference design values with a mean absolute error of 0.5%. Using measured monthly solar-resource and temperature data for Riyadh, a full twelve-month analysis gives an annual self-sufficiency of 51.8% and a PV self-consumption of 72.9% for the optimised energy-management scheme. A dedicated time-domain switching simulation with FFT analysis shows that the LCL filter limits grid-current total harmonic distortion to 0.8%, far below the L-filter value of 6.2% and below the 5% current-distortion reference of IEEE 519 (full compliance additionally requires the PCC short-circuit ratio). Twelve-month, battery-size and load-sensitivity studies confirm robustness, and a techno-economic assessment based on the Saudi Electricity Company residential tariff quantifies levelized cost, payback and battery degradation, showing that economic viability hinges on tariff reform. Full article

Open-vocabulary semantic segmentation (OVSS) of unmanned aerial vehicle (UAV) imagery aims to recognize arbitrary text-specified categories in aerial scenes, but existing OVSS models often suffer from UAV-domain shifts. To provide a reproducible testbed, this paper constructs a unified UAV OVSS benchmark by reorganizing multiple UAV segmentation datasets into cross-dataset transfer settings with explicit category harmonization and seen/unseen vocabulary analysis. Based on this benchmark, we propose UAV-OVSeg, a Cost Aggregation-style dense matching framework enhanced in two complementary directions: an LLM-guided Category Expansion Module that converts raw category names into structured UAV-aware descriptions, and a DINO-enhanced Geometric Feature Fusion Module that injects local structure into dense visual–text matching. Under SynDrone training, UAV-OVSeg achieves 65.5% mean intersection over union (mIoU) and 78.0% mean accuracy (mACC), improving upon the CAT-Seg baseline by 2.6 mIoU and 3.2 mACC. Under Aeroscapes training, it achieves 64.5% mIoU and 76.8% mACC, improving upon CAT-Seg by 2.6 mIoU and 3.1 mACC. Additional analyses of LLM variants, object scales, prompt sensitivity, boundary quality, and computational cost further verify the effectiveness and reproducibility of the proposed UAV-oriented text–visual enhancement strategy. Full article

Reconfigurable radio-frequency (RF) front ends employ N-path filters to achieve digitally tunable frequency selectivity, high linearity, and low static power. However, their linear periodically time-varying (LPTV) operation complicates analysis because an input tone is translated to multiple output harmonics. This tutorial review synthesizes the principal methods for analyzing N-path filters, comparing continuous-time window function analysis, discrete-time ordinary differential equation (ODE) modeling, and adjoint network methods. We evaluate and compare their underlying assumptions, outputs, and computational burdens. Additionally, we present an educational time-domain interpretation based on orthogonal sine/cosine excitation. This viewpoint connects capacitor averaging and path-to-path phase cancellation with harmonic transfer functions (HTFs). Rather than replacing rigorous HTF formulations, this interpretation provides a physically intuitive explanation for the fundamental coefficient $H_0\left(f\right)$ and the gain-null condition at $f_{\mathrm{in}}=kN{f}_s$. The numerical integration of the switched-RC equations serves as a consistency check. For a four-path example with $\Gamma =\tau /\left(RC\right)=0.02$, the numerical values of $|H_0\left(f_s\right)|$ and $|H_0\left(2f_s\right)|$ differ from the intuitive limits by less than $0.001$ dB. The residual responses at $4f_s$ and $8f_s$ are $-49.95$ dB and $-55.97$ dB, respectively. Finally, we extend the orthogonal-excitation relationship to extract higher-order HTFs. This tutorial synthesis clarifies how these established analytical methods relate and guides selection for specific applications. Full article

In AC motor field-oriented control systems, employing a single DC-bus current sensor with a phase-shifting strategy can effectively reduce system complexity and hardware costs. The vector-symmetry-based phase shift (VSPS) strategy has been shown to offer advantages in extending the linear modulation range and reducing the total harmonic distortion (THD) of phase currents. However, the harmonic suppression mechanism and the effective operating region of this method remain unclear. To fill this theoretical gap, this article develops a time-domain current ripple model for the VSPS method, analyzes the relationship between ripple distribution and vector symmetry, and reveals that VSPS reduces the root-mean-square value of the current ripple through improved PWM waveform symmetry. Furthermore, the boundary conditions under which VSPS can effectively reduce THD over a range of modulation indices are derived. Simulation results validate the correctness of the theoretical model. Experimental results show that the variation trend in the THD difference between VSPS and conventional phase shift compensation with modulation index agrees with the theoretical predictions, although the numerical improvement is relatively small. This work provides a theoretical basis and practical guidance for the engineering application of the VSPS method. Full article

Accurate identification of electromagnetically induced stator vibration frequency components is essential for the online condition monitoring of hydro-generators, particularly for assessing the dynamic state of the stator core under normal operating conditions. In engineering practice, the fast Fourier transform (FFT) is widely used for vibration spectrum analysis; however, because the measured vibration response is simultaneously affected by electromagnetic excitation, mechanical rotation, hydraulic disturbance, and external harmonic interference, FFT-based spectra often contain multiple frequency components whose structural relevance is difficult to determine directly. To address this issue, this paper proposes a coupled continuous wavelet transform and power spectral density transmissibility (CWT-PSDT) method for identifying key vibration frequency components with stable time-frequency energy and inter-sensor transmissibility in hydro-generator stator vibration signals. In the proposed framework, the analytic Morlet wavelet is first employed to localize dominant energy bands in the time-frequency domain, and PSDT is then used to screen frequency components with relatively stable inter-sensor transmissibility characteristics, thereby reducing the ambiguity caused by excitation-dominated spectral components. A clamped-clamped beam model is first used for numerical validation, and the maximum identification error of the first five natural frequencies is 4.22%. Experiments on a Francis turbine-generator test rig under five operating conditions further show that the proposed method can distinguish the mechanical rotational component near 10.3 Hz from the electromagnetic-related component near 50.8 Hz, while retaining higher-order electromagnetic-related components around 150 Hz and 250 Hz. The results demonstrate that the proposed CWT-PSDT method provides a physically interpretable and data-efficient approach for extracting stator-core-related spectral features, and offers a theoretical basis for spectrum-based online monitoring and future abnormal-condition comparison of hydro-generator stator responses. Full article

Consumer smartwatches are increasingly used to monitor health, physical activity, rehabilitation, and performance in real-world environments. Although these devices provide continuous and scalable data, many user-facing outputs are not direct physiological measurements, but estimates generated from sensor signals, proprietary algorithms, user characteristics, and contextual assumptions. This review article provides a practical framework for evaluating smartwatch-derived metrics by distinguishing between relatively direct sensor measurements and higher-level algorithmic outputs. We review how common and emerging metrics are generated, including cardiovascular measures, energy expenditure, aerobic capacity, sleep and readiness scores, body composition, movement mechanics, cuffless blood pressure, sweat loss and hydration, and non-invasive glucose monitoring. Across these domains, validity varies substantially by device, algorithm, population, activity type, environment, and intended application. Smartwatch-derived data may be most useful for tracking within-person trends and complementing laboratory, clinical, or self-reported assessments, but caution is warranted when using these outputs for precise physiological quantification, diagnostic classification, or cross-device comparisons. Future progress will require stronger validation frameworks, greater algorithmic transparency, standardized reporting, harmonized data infrastructure, and careful alignment between wearable metrics and meaningful health, rehabilitation, and performance decisions. Full article

Animal locomotion exhibits highly structured temporal dynamics despite significant inter-species biomechanical and anatomical discrepancies. While wearable inertial measurement unit (IMU) sensors and deep learning have advanced animal activity recognition, existing systems remain largely species-dependent, requiring large-scale labeled datasets for each deployment. To address this, we propose a biomimetic cross-species behavioral representation learning framework that extracts transferable locomotor structures from heterogeneous IMU signals. The proposed methodology integrates behavioral ontology harmonization, imbalance-aware augmentation, and semi-supervised domain-adversarial adaptation to reduce inter-species distributional discrepancies. Unlike conventional classification models, our dual-head architecture enables simultaneous processing of multi-label and single-label behavioral structures across anatomically diverse species. Extensive experiments conducted on dog, goat, and horse datasets demonstrate that the proposed framework significantly improves cross-species transferability, achieving a mean Macro-F1 score of 0.711 compared to 0.345 for direct transfer learning. Furthermore, we show that sparse target supervision is critical for stabilizing adversarial adaptation. K-Means head adaptation partially mitigated fully unsupervised negative transfer. However, its performance remained below semi-supervised Domain-Adversarial Neural Network (DANN), indicating that sparse target supervision is still necessary for stable cross-species adaptation. These findings suggest that biologically motivated locomotor similarity can support cross-species behavioral transfer, although external validation on additional species, sensor placements, and deployment conditions is still required. Full article

The integration of artificial intelligence (AI) techniques into biomass pyrolysis research has attracted increasing attention in recent years; however, the existing literature remains fragmented across diverse methodological approaches and application domains. This study presents a systematic literature review of AI applications in biomass pyrolysis, combining bibliometric and qualitative analyses to map the current state of the art, identify prevailing research trends, and highlight existing knowledge gaps. Following a structured search conducted in the Scopus database, 33 peer-reviewed journal articles published in English between 2003 and 2026 were selected according to predefined eligibility criteria. The final portfolio was prioritized using an adapted version of the Normalized Index for Ranking Papers (NIRP 2.0), while the review procedure followed, whenever applicable, the recommendations of PRISMA, PRISMA for Abstracts, and PRISMA-S guidelines. The ranking methodology incorporated four scientometric indicators: Field-Weighted Citation Impact, average citations per year, SNIP, and CiteScore. The bibliometric analysis revealed a significant intensification of research activity during the last five years, with China, India, and Pakistan emerging as the most productive countries in the field. Machine learning techniques, particularly ensemble learning methods such as Extreme Gradient Boosting, Random Forest, and Gradient Boosting Decision Trees, were identified as the dominant approaches, especially in applications related to product yield prediction (biochar, bio-oil, and gas), kinetic and thermodynamic modeling, co-pyrolysis optimization, and process parameter estimation. Recent studies have also demonstrated growing interest in explainable artificial intelligence methods aimed at improving model interpretability and supporting physical understanding of pyrolysis systems. Despite the promising predictive and optimization capabilities demonstrated by AI-based models, important challenges remain, including limited dataset sizes, data heterogeneity, inconsistent terminology, reduced model generalizability, and the absence of physically informed constraints in many machine learning frameworks. The findings of this review indicate that future advances in the field will strongly depend on the development of standardized and publicly accessible databases, harmonized reporting protocols, and the integration of physics-informed artificial intelligence approaches capable of providing reliable, interpretable, and transferable predictions for biomass pyrolysis processes. Full article

Polyphase electrical motors exhibit a high degree of symmetry in both their mechanical configuration and electrical structure, which is reflected in analytical models, including dynamic and phasor-based representations. The aim of the present paper is to provide a short overview of polyphase electric motors, with a special emphasis on 12-phase permanent-magnet synchronous motors (12-phase PMSMs), from operating principles to application. While vector space decomposition and generalized Clarke–Park transforms have been documented for multiphase machines with five, six, and nine phases, the explicit derivation of these transformation matrices for the 12-phase case (arranged as four three-phase winding triads) is scattered across specialized sources or omitted altogether. This paper addresses that gap by deriving, in closed form, the complete set of transformation matrices (practical-to-fundamental, generalized Clarke, and generalized Park) for the 12-phase PMSM, and by consolidating them with the corresponding time-domain and Park-domain constitutive equations into a single self-contained tutorial reference. This article focuses on the analytic modeling of these motors using vector space decomposition (VSD), which allows for the differentiation of all odd-order harmonics. The Clarke and Park transforms are also employed, which simplifies the analysis of multiphase systems by reducing the number of equations to be dealt with and better managing the reference frame compared to a traditional Cartesian coordinate system. This paper is meant to deliver a short review of the subject matter focused on mathematical aspects as a contribution of tutorial value. Full article

Immune checkpoint inhibitor (ICI)-based combinations have become central systemic treatment options for advanced hepatocellular carcinoma (HCC) and are now being integrated into selected intermediate-stage settings. As tumor responses have improved, some patients who were not initially candidates for curative-intent treatment may later become candidates for resection, ablation, or liver transplantation. However, radiographic response alone does not define curative-intent candidacy, and no shared framework currently guides how post-immunotherapy response should be translated into a treatment decision. Terminology also differs regionally: Asian literature frames a resection-anchored paradigm, whereas Western practice uses transplant-anchored downstaging. This narrative review proposes a multidisciplinary framework for immunotherapy-based conversion to curative-intent treatment in HCC. We first clarify the lexicon of conversion, downstaging, bridging, neoadjuvant therapy, post-ICI transplantation, and drug-free or treatment-free status. We then summarize conversion-relevant evidence across key clinical decision settings, including transarterial chemoembolization (TACE)-unsuitable intermediate-stage disease, portal vein tumor thrombus or macrovascular invasion, borderline-resectable or locally advanced disease, and transplant downstaging or bridging. The central framework defines curative-intent transition through the intersection of three domains: technical suitability, oncologic suitability, and physiologic or liver-reserve suitability. Biomarkers, imaging response, tumor-marker kinetics, liver function, and treatment-related toxicity are discussed as inputs into candidacy rather than as response measures alone. Finally, we propose a multidisciplinary workflow and highlight lessons from pancreatic cancer, biliary tract cancer, and colorectal liver metastases. As an expert-opinion-based framework, this approach should structure multidisciplinary discussion rather than serve as validated selection criteria; harmonized terminology, prospective conversion registries, and conversion-specific endpoints are needed for prospective validation. Full article

To address the challenge of achieving a unified dynamic evaluation of in-service performance for reflector antennas subjected to coupled wind disturbances, structural flexibility, and servo control, an end-to-end performance evaluation method based on integrated modeling is proposed. A disturbance–structure–electromagnetic–control integrated modeling framework is constructed, in which the fluctuating wind load, structural dynamics model, cascaded servo control, and end-to-end performance mapping model are unified within a state-space closed-loop system, thereby enabling time-domain dynamic evaluation from environmental excitation inputs to performance index outputs. The Davenport spectrum and harmonic superposition method are adopted to establish a stochastic fluctuating wind model, and structural disturbance inputs are formed through wind pressure linearisation and modal projection. A low-order flexible dynamic model of the reflector antenna is developed using finite element modal condensation, and a main-axis closed-loop control model is formulated by incorporating fuzzy active disturbance rejection control and notch filtering. By combining the best-fit parabolic surface, the weighted half-path-length difference, and the Ruze formula, an end-to-end mapping model that relates structural nodal displacements to electromagnetic performance degradation is established. The research demonstrates that the proposed method can effectively reveal the influence of wind speed, elevation angle, and flexible mode coupling on antenna performance. Furthermore, a performance evaluation system developed based on this method integrates parameter input, simulation computation, and result output, providing an effective tool for antenna design optimization and performance assurance. Full article

Increasing power density of electrical devices in electrified transportation is an irreversible trend which involves power electronic-type supply, higher voltage and temperature. However, fast converter-switch rise times, high modulation and carrier frequencies, harmonics, and increased design field and temperature constitute potential causes of accelerated electrothermal aging of insulation, especially if harmful phenomena, as partial discharges (PDs), incept. This paper focuses on solving issues related to PD monitoring under power electronics waveforms, dealing with effective and automatic tools for noise rejection and for the identification of the type of source generating PD, the latter being fundamental for quality control, diagnostic and condition maintenance. It is shown that innovative techniques are available, which allow PD to be measured even under fast switching (rise time) and high frequency, separating, in the time domain, PD pulses from switching noise. This approach can be carried out automatically by the PD detector software presented here, not requiring experts for measurement management and, thus, making it a feasible tool also for on-line PD monitoring and condition-based maintenance. PD monitoring results from accelerated aging tests on a motor under pulse-width modulation (PWM supply) are presented. In order to assess the insulation health condition, progressive degradation of the motor is quantified using a dynamic health index (DHI), primarily based on key PD parameters, i.e., PD magnitude, repetition rate, and likelihood of discharge type (surface or internal). The proposed DHI approach not only provides meaningful metrics for translating PD data into a diagnostic tool, but it also offers insights into residual life estimation and failure risk prediction. Full article