Search Results (4,061)

This study investigates heat transfer enhancement in the cooling channels of gas turbine blade turbulators using modified transverse baffles with isosceles triangular sawtooth perforations. The proposed baffle design aims to improve convective heat transfer by promoting flow mixing and disrupting the thermal boundary layer. Experiments were conducted in a rectangular channel with an aspect ratio of 3.75 under constant heat flux conditions using air (Pr = 0.7) as the working fluid. The effects of Reynolds number (Re = 6000–24,000), sawtooth width ratio (a/W = 0.0, 0.0625, 0.125, 0.25, and 0.5), and sawtooth height ratio (b/e = 0.0, 0.25, 0.5, 0.75, and 1.0) were systematically investigated. The blockage ratio (e/H) and pitch ratio (P/H) were maintained at 0.3 and 1.5, respectively. Heat transfer characteristics were evaluated using the thermochromic liquid crystal (TLC) technique, while thermal–hydraulic performance was assessed in terms of the Nusselt number (Nu), friction factor (f), and thermal performance factor (TPF). The results demonstrate that introducing sawtooth perforations significantly enhances heat transfer compared with a smooth channel, yielding Nusselt number ratios (Nu/Nu_s) between 1.6 and 2.6. The highest heat transfer enhancement was achieved at a/W = 0.0625 and b/e = 0.25, where the relatively small sawtooth openings generated stronger jet impingement, enhanced flow mixing, and more effective disruption of the thermal boundary layer. However, these geometric modifications also increased the pressure loss due to intensified flow blockage and recirculation, resulting in friction factor ratios (f/f_s) ranging from 8.9 to 14.9. The maximum pressure-drop penalty occurred at b/e = 0.25 because the smaller openings produced stronger turbulence and increased flow resistance. Despite the increased friction loss, the optimum configuration (a/W = 0.0625 and b/e = 0.25) achieved the highest thermal performance factor of 1.2 at Re = 6000. Full article

The growing demand for efficient, stable, and environmentally friendly materials for next-generation optoelectronic and photovoltaic applications has attracted significant interest in double perovskite compounds. First-principles density functional theory (DFT) calculations were performed to systematically investigate the structural, mechanical, electronic, and optical properties of Ba₂MReO₆ (M = Li, Na, K, and Rb) double perovskites. Structural optimization confirms that all compounds crystallize in the cubic Fm3̅m symmetry. The thermodynamic and geometric stability of the series is checked with negative formation energies and tolerance factor analyses (t, μ, τ). Mechanical analysis confirms that all compounds are mechanically stable; Ba₂LiReO₆ is the stiffest, while Ba₂RbReO₆ shows moderate stiffness with the highest ductility. Furthermore, ab initio molecular dynamics (AIMD) simulations at room temperature confirm the dynamical stability of all compounds, with negligible fluctuations in total energy under thermal conditions. The calculated band structures using both GGA-PBE and HSE06 hybrid functionals reveal that all compounds possess indirect band gaps, with HSE06 values of 2.236 eV for Ba₂LiReO₆, 2.133 eV for Ba₂NaReO₆, 2.116 eV for Ba₂KReO₆, and 1.395 eV for Ba₂RbReO₆. Optical measurements indicate that it is highly polarizable by dielectric polarizability, has high absorption coefficients (approximately 10⁶ cm⁻¹), and has large optical conductivity in the UV, with large inter-band interactions between 2 and 4 eV. The suitable band gap and favorable optical characteristics suggest that Ba₂RbReO₆ is the most promising candidate for photovoltaic and solar-cell applications. Full article

Hafnium−zirconium oxide (HZO)−based ferroelectric field−effect transistors (FeFETs) are expected to become core devices for new embedded memory and compute−in−memory systems. However, existing simulations rely on finite−element−based TCAD tools, which are computationally intensive and time−consuming, and they struggle to account for the dynamic flipping of ferroelectric domains. This paper utilizes a time−domain simulation framework based on the nucleation−limited switching (NLS) model coupled with the surface potential of a MOSFET, enabling a self−consistent solution for polarization and electrical characteristics; a Monte Carlo method is employed to simulate device variability, and Shmoo plots are used to identify optimal programming and erasure process windows; an integrated solution is proposed for 22 nm FDSOI devices, addressing geometric scaling, modification of the Landau–Khalatnikov (L−K) dynamic model for ultrathin ferroelectric layers, and suppression of short−channel effects. Model validation is limited to selected operating metrics, and predictive accuracy outside the calibrated cases requires additional independent datasets. This method enables end−to−end simulation of FeFETs, from material polarization and device electrical characteristics to performance optimization, thereby providing model−based analytical and design support for the development of advanced, ultra−low−power FeFETs. Full article

Thermal energy storage plays a crucial role in meeting human energy demands and is particularly essential for many solar energy applications. Among the various storage methods, phase change materials (PCMs) have attracted significant attention because their thermal performance can be greatly influenced by the material properties, physical characteristics, and the geometry of the encapsulating container. In this paper, the melting process of phase change materials (PCMs) within an elliptical enclosure using the finite volume method is analyzed. Gallium is selected as a PCM with a low Prandtl number. A physical model employing the enthalpy porosity formulation is elaborated to describe the coupling between natural convection and the melting process of PCMs. Numerical simulations are performed to examine the influence of the aspect ratio (n = b/a), ranging from 1 to 4, and inclination angles from 0° to 90° of the elliptical enclosure on the melting process. It has been found that the use of the elliptical capsule can reduce the melting process time. For a Rayleigh number of 10⁶, the melting time decreases as the aspect ratio increases from 1 (circle) to 4. The horizontal orientation (θ = 0°) is found to be the most efficient, with a melting rate higher than that observed for inclined positions (30°, 45°, 60°, and 90°). For a low Rayleigh number of 10⁴, the inclination angle has an imperceptible effect on the phase change. Empirical correlations are proposed to relate the Nusselt number to the Rayleigh number, with coefficients adapted to different Fourier numbers and geometric parameters. Full article

This paper proposes a set of trajectory generation strategies for industrial robots that use ray-casting over the workpiece CAD model for various manufacturing operations. By employing ray-casting on a triangular-mesh representation of the production part, points can be generated across the entire surface without extracting geometric features such as curves, edges, or planes. This approach enables the development of diverse point-generation methods with distinct characteristics, adaptable to the specific requirements of each part and manufacturing process. The developed algorithms achieve results comparable to existing robot programming methods, and, when integrated into the specialized offline programming environment, they enable flexible trajectory generation for operations such as sanding, milling, adhesive deposition, and painting. Finally, these trajectories are automatically exported in a syntax that ensures rapid integration of the point sequence into a base program compatible with an articulated robot controller. The results show that the proposed methods can effectively generate trajectories for sanding and milling using two different robots. Full article

This study aimed to evaluate the influence of ecological zones and honey bee morphometric characteristics on the physicochemical properties of honey produced in different regions of Kazakhstan. A total of 103 honey samples were collected and analyzed for key quality parameters, including moisture content, sugars, acidity, pH, and diastase activity. Multivariate statistical approaches, including principal component analysis (PCA), linear discriminant analysis (LDA), and multivariate analysis of variance (MANOVA), were applied to assess variability and underlying patterns. Geometric morphometric analysis of wing shape (MorphoJ) revealed a high degree of morphological homogeneity across all samples, with no distinct clustering in PCA space. This was further supported by canonical variate analysis (CVA) using IdentiFly, which assigned the majority of samples to the C lineage (Apis mellifera carnica) with high classification probability, indicating a uniform population structure. MANOVA results demonstrated that neither ecological zone nor morphometric traits exerted a significant global effect on honey physicochemical properties (p > 0.05). However, significant interaction effects were identified between ecological zone and specific morphometric variables, particularly sternite length and cubital index (p < 0.05), suggesting a context-dependent influence. Full article

Geometric asymmetry in recirculating micro-burners remains underexplored in catalytic micro-combustion research, despite its potential to significantly influence ignition behavior. This numerical study investigates the ignition and combustion characteristics of four micro-channel catalytic burners with distinct geometric configurations, aiming to evaluate the role of asymmetry on combustion behavior. Burner 1 is a straight-channel design without heat recirculation. Burners 2–4 are U-bend recirculating configurations: Burner 2 with symmetric channel sizes, Burner 3 with a wider inlet channel, and Burner 4 with a narrower inlet channel. A two-dimensional computational fluid dynamics model with a one-step global propane oxidation mechanism and catalytic wall reactions is employed. The results show that Burner 1 exhibits the highest ignition temperature (555 K) and shortest ignition delay (28.5 s) due to its low thermal mass. Among U-bend burners, Burner 4 achieves the lowest ignition temperature (530 K) and the highest steady-state combustion temperature (1726 K), owing to reduced recirculation velocity and enhanced thermal feedback. Burner 2 shows moderate performance, while Burner 3 gives the weakest combustion intensity among recirculating designs. The heterogeneous reaction contribution is highest in Burner 1 (69.3%) and lowest in Burners 2 and 3 (~55%). Asymmetric channel sizing, particularly a narrow catalytic channel combined with a wide recirculation channel (Burner 4), significantly improves ignition and combustion performance under cold-start conditions. Full article

Recent theoretical studies propose that Hawking radiation may not emerge strictly at the event horizon but rather from the spatially extended region surrounding a black hole, commonly referred to as the quantum atmosphere. In this work, we explore how this concept influences nonlocal quantum correlations in a bosonic bipartite system located at a certain distance from a Schwarzschild black hole. By employing the measurement-induced nonlocality (MIN) as a quantifier of quantum correlations, we analyze the response of bosonic fields to the thermal and geometric characteristics associated with the Hartle–Hawking vacuum. Those features are associated with the coordinate-dependent metric components of the Schwarzschild background. In this manner, we extend previous studies that primarily focused on the fermionic systems. Our results reveal that when the quantum atmosphere is taken into account, the behavior of MIN departs from its conventional near-horizon profile. In particular, bosonic nonlocal correlations are found to exhibit a pronounced degradation at a finite radial distance from the event horizon and to ultimately vanish as the scaled distance increases further. To some extent this behavior contrasts with the previously considered fermionic case, indicating that bosonic fields provide a potentially stronger response to the quantum atmosphere. Full article

Staggered-floor steel-reinforced concrete beam–column joints are extensively applied in turbine buildings of nuclear power plants to meet the requirements of spatial layout and pipeline arrangement. Such joints feature distinct geometric discontinuity and suffer additional torsion effects as well as asymmetric stress distribution when the middle column is lost, which greatly impairs the structural progressive collapse resistance. In this study, three 1/5-scale joint specimens, consisting of two staggered-floor steel-reinforced concrete joints and one reinforced concrete joint, were tested under vertical monotonic static loading. The failure pattern, deformation property, torsional performance, strain development and load-bearing mechanism were comprehensively analyzed. Finite element models considering the coupling effect of torsion, bending and shear were established and validated via ABAQUS. The test results show that the peak load-bearing capacities of the SRC-1, SRC-2, and RC specimens were 148.2 kN, 149.7 kN, and 69.3 kN, respectively. Compared with the RC specimen, the peak load-bearing capacity of the SRC specimens more than doubled, indicating that the embedded H-section steel can significantly improve the load-bearing capacity of staggered beam–column joints. However, when the staggered height distance was increased from 140 mm to 280 mm, the ultimate collapse displacement of the specimens decreased from 340 mm to 310 mm, indicating a reduction in deformation capacity. The finite element model reasonably reproduced the specimens’ primary load–displacement response and damage characteristics, with a peak load error of 8.93% for SRC-1. Finally, corresponding design recommendations are put forward for staggered-floor steel-reinforced concrete joints in nuclear power plant structures. Full article

To address the problems of slow convergence, long planned paths, and excessive turning points in unmanned surface vehicle (USV) path planning under complex environments, this paper proposes a path planning method based on an Enhanced Gold Rush Optimizer (EGRO). A nonlinear adaptive parameter adjustment strategy and a stage-wise dynamic probability mechanism are designed to improve the balance between global exploration and local exploitation at different stages of iteration. In addition, a Gaussian diffusion mechanism combined with a local search operator is introduced to enhance the algorithm’s ability to escape from local optima and reduce the number of path turning points. In the remote-sensing-image-based sea-ice simulation scenario, compared with the conventional GRO, PSO, and GWO algorithms, the maximum observed improvements of EGRO in best fitness, convergence iterations, and the number of path turning points are approximately 31.65%, 51.24%, and 35.00%, respectively. The simulation results indicate that EGRO can provide a feasible swarm-intelligence-based optimization framework for USV path planning. The proposed algorithm can generate feasible paths with relatively shorter lengths and fewer turning points. These characteristics may provide a favorable geometric reference for subsequent trajectory generation and navigation control, thereby highlighting the potential value of EGRO in engineering applications of USV path planning. Full article

Background/Objectives: Myocardial iron overload is a major contributor to adverse cardiac outcomes in pediatric patients with transfusion-dependent β-thalassemia (TDT). Cardiovascular magnetic resonance (CMR), including T2* and T1 mapping, allows quantification of myocardial iron and early detection of cardiac dysfunction. Artificial intelligence (AI)-assisted CMR feature tracking (CMR-FT) provides a sensitive and reproducible approach for assessing myocardial deformation, even in patients with preserved left ventricular ejection fraction (LVEF). This study aimed to evaluate the utility of AI-based CMR-FT and its relationship with multiparametric CMR biomarkers, including myocardial strain (GCS, GLS, GRS), tissue characteristics (T2*, T1), and left ventricular (LV) geometry in pediatric TDT patients. Methods: In this retrospective study, 68 pediatric patients with β-thalassemia major and 20 age-matched healthy controls underwent CMR with T2*, T1 mapping, and FT-based strain analysis. Myocardial iron overload was defined as T2* < 20 ms. Strain parameters were compared between groups, and correlations with tissue characteristics and LV geometry were assessed using Pearson’s correlation. Results: Patients with myocardial iron overload have significantly reduced GCS compared to controls (−17.4 ± 1.6% vs. −19.3 ± 4.5%, p < 0.01). GCS correlated with T2* (r = −0.33, p = 0.007) and T1 (r = −0.45, p < 0.001). GLS was sensitive to LV geometric changes, particularly concentric remodeling, correlating with LV mass/EDV ratio (r = −0.449, p < 0.001). Conclusions: AI-based CMR-FT combined with multiparametric tissue imaging enhances early detection of subclinical myocardial dysfunction in pediatric TDT, offering diagnostic insights beyond conventional CMR metrics and supporting improved risk stratification. Full article

Finger vein recognition (FVR) has significant potential in biometrics due to its high accuracy and intrinsic liveness detection capabilities. However, the increasingly stringent privacy regulations have presented severe data security challenges for traditional centralized training. While federated learning (FL) mitigates these privacy concerns through a decentralized training paradigm, conventional FL algorithms that seek a single global model experience significant performance degradation on non-independent and identically distributed (Non-IID) data in real-world cross-institutional deployments. This degradation stems primarily from a dual-heterogeneity issue that involves domain shift caused by hardware discrepancies across acquisition devices, and label skew resulting from nonoverlapping user identities. To address this dual-heterogeneity challenge, we propose a personalized federated learning framework driven by hierarchical parameter decoupling and subspace metric. First, we designed a hierarchical parameter decoupling architecture. Macroscopically, the architecture retains the classifier locally to isolate label heterogeneity; microscopically, it introduces an additive parameter decomposition that decouples the feature extractor on a global full-rank basis (to capture domain-invariant semantics, namely, the shared physiological vein topologies) and a local low-rank adapter (that accommodates device-specific characteristics, such as hardware-induced noise and illumination discrepancies). Furthermore, we propose a subspace similarity matching strategy based on principal angles on the Grassmann manifold. By exploiting the geometric properties of low-rank projection matrices, this strategy accurately quantifies the underlying distribution discrepancies among clients to guide personalized weighted aggregation. Extensive experiments on six public finger vein datasets demonstrate that the proposed framework significantly improves the overall recognition performance and mitigates performance degradation caused by data heterogeneity. Full article

Few-shot remote sensing scene classification aims to accurately recognize unseen scene categories using only a scarce number of labeled samples, which has emerged as a research hotspot in the field of remote sensing image interpretation. However, remote sensing images intrinsically suffer from large intra-class variations, high inter-class similarities, and complex background interferences. Traditional few-shot learning methods typically perform feature metric learning in Euclidean space, making it difficult to capture the non-Euclidean geometric distribution characteristics of remote sensing features, and they often neglect the spatial structural information embedded in feature maps. To address these issues, this paper proposes a novel few-shot remote sensing scene classification method, termed ZSFS-RGBN, which integrates a Zigzag Scanning Feature Sequence with a Riemannian Geometric Barycenter Network. Specifically, ResNet12 is first employed as the backbone to extract deep convolutional feature maps from both the support and query sets. Second, a Zigzag scanning strategy is introduced to reorganize the two-dimensional feature maps into one-dimensional feature sequences, thereby effectively preserving the spatial locality and structural continuity of the features. Third, an autoregressive moving average (ARMA) model is constructed to characterize the spatial dependencies of the feature sequences, and its state parameters are mapped onto a symmetric positive definite (SPD) matrix manifold, enabling the deep semantic representations of remote sensing scenes in a non-Euclidean geometric space. Finally, a Riemannian geometric barycenter network is designed to learn the Riemannian barycenter of each category on the SPD manifold, where a joint loss function is introduced to simultaneously optimize intra-class compactness and inter-class separability. Comprehensive experiments are conducted on three public remote sensing scene datasets: NWPU-RESISC45, UC Merced Land-Use, and WHU-RS19. Experimental results demonstrate that the proposed method consistently outperforms several representative state-of-the-art approaches under both 5-way 1-shot and 5-way 5-shot settings. Furthermore, ablation studies verify the effectiveness of each component within the proposed framework. Full article

Automatic respiratory-sound classification becomes particularly challenging when crackles and wheezes coexist within the same respiratory cycle, because overlap events contain mixed acoustic characteristics and are severely underrepresented in standard benchmarks. In the ICBHI 2017 Respiratory Sound Database, the rare overlap label, Both, is frequently absorbed into Crackle, Wheeze, or Normal classes by conventional flat classifiers, resulting in poor interpretability and weak clinical reliability. To address this issue, this paper proposes OCAD (Overlap Composition and Class-Atom Decoding), an explainable overlap-aware respiratory-sound classification framework. Unlike conventional end-to-end classifiers that directly map features to labels, OCAD explicitly decomposes respiratory-sounds into interpretable crackle and wheeze factors, composes an overlap representation from these abnormal-event components, and performs classification through structured class atoms whose geometric relationships reflect the progression from Normal to single-event and overlap states. By preserving a fixed acoustic backbone across all compared systems, the study isolates the contribution of the proposed explainable representation and decoding mechanism. Experimental results on a fixed patient-level split of the ICBHI 2017 dataset show that OCAD achieves an ICBHI Score of 0.6920, Macro Score of 0.6550, Macro F1 of 0.6680, crackle sensitivity of 0.6300, wheeze sensitivity of 0.6550, and Both sensitivity of 0.2620. Compared with the strongest structured class-atom baseline under the same protocol, OCAD improves the ICBHI Score by 0.1334, Macro F1 by 0.2575, and Both sensitivity by 0.2054 absolute points. Additional robustness analyses across repeated seeds, patient-level cross-validation, and external respiratory-sound datasets further support the effectiveness of the proposed framework. The proposed overlap-aware representation and decoding strategy provides a more interpretable and reliable approach for respiratory sound classification in the presence of co-occurring abnormal events. Full article

In this paper, we propose a novel generative adversarial network (GAN)-based joint localization and mapping (JLAM) method using angle difference of arrival (ADOA) measurements for millimeter-wave (mmWave) communication systems. The proposed method adopts a deep auto-encoder neural network as the discriminator of the GAN and models the generator as an explicit geometric ADOA function of the access point (AP) positions and the mobile terminal (MT) position, rather than as a conventional black-box neural network. By exploiting the two-dimensional distribution characteristics of high-dimensional ADOA vectors collected at a large number of random and unknown MT positions, the proposed method learns the ADOA data distribution and transforms it into the AP geometric topology. Then, the MT positions and the indoor map are estimated based on the recovered physical and virtual AP topology. The simulation results show that, under the representative setting with $N=2000$ measured ADOA vectors and $\sigma =2°$ AOA measurement noise, the proposed method achieves an average localization error of about 0.25 m, compared with about 0.60 m for the JADE algorithm, corresponding to an error reduction of approximately $58\%$. The proposed method also provides more accurate room boundary estimation than JADE, confirming its effectiveness for mmWave JLAM. Full article