Search Results (6,371)

The spatial distribution of different hard magnetic phases is a key factor affecting the coercivity of dual-main-phase rare-earth permanent magnets. However, the underlying relationship between main-phase distribution and magnetization reversal behavior in Nd₂Fe₁₄B/Dy₂Fe₁₄B magnets remains unclear. In this work, micromagnetic simulations based on MuMax3 and OOMMF are performed to systematically investigate the effects of main-phase spatial arrangement on the magnetic properties and magnetization reversal mechanisms of Nd₂Fe₁₄B/Dy₂Fe₁₄B exchange-coupled magnets. First, single-phase Nd₂Fe₁₄B and Dy₂Fe₁₄B models are constructed to clarify the intrinsic magnetic characteristics of the two phases. The calculated demagnetization curves show that, although the magnetocrystalline anisotropy field H_A of Nd₂Fe₁₄B is lower than that of Dy₂Fe₁₄B, its higher saturation magnetization M_S results in a slightly larger anisotropy constant K, according to K = ${\displaystyle \frac{1}{2}}$µ₀·H_A·M_S. Nevertheless, Dy₂Fe₁₄B exhibits a stronger resistance to magnetization reversal, as reflected by its higher nucleation field H_N and coercivity H_C. This indicates that the resistance to magnetization reversal is more directly associated with H_A than with K alone. Subsequently, three types of exchange-coupled dual-main-phase Nd₂Fe₁₄B/Dy₂Fe₁₄B magnet models, including cubic, cylindrical, and sandwich structures, are constructed with identical size fractions of the two phases to investigate the influence of phase spatial distribution on magnetization reversal behavior. The calculated results demonstrate that placing the Dy₂Fe₁₄B phase in the outer region leads to higher H_Nand H_C than the reverse phase arrangement, owing to its higher H_A, which strengthens the resistance against magnetization reversal. Further analysis of the in-plane magnetic-moments and angular distributions reveals that magnetic-moment deviation is initially activated in the Nd₂Fe₁₄B region with lower H_A, followed by gradual propagation through exchange coupling at the phase interface. This effect of phase spatial distribution is not limited to Nd₂Fe₁₄B/Dy₂Fe₁₄B exchange-coupled magnets. In Nd₂Fe₁₄B/La₂Fe₁₄B and Nd₂Fe₁₄B/SmCo exchange-coupled magnets, placing the phase with the higher H_A in the outer region likewise results in higher H_Nand H_C than the reverse phase arrangement. In addition, for all the dual-main-phase exchange-coupled magnets considered above, the coercive field decreases with increasing magnet size. These findings provide theoretical insights into the regulation of coercivity through spatial phase distribution in dual-main-phase rare-earth permanent magnets. Full article

Person–Job Fit (PJF) serves as a core task of intelligent recruitment recommendation. However, existing graph-based PJF models rely on a fixed, single-path aggregation scheme, thereby failing to simultaneously capture global interaction statistics and local competency-matching signals from candidate–job bipartite graphs. To address this limitation, this work proposes Bi-path Graph Convolutional Neural Network with Gate Fusion (BiGCNG), a dual-path graph convolutional network with global learnable gate fusion, composed of three coordinated modules. First, the Shared Text Embedding Pre-processing Module (STEPM) generates unified node embeddings by fusing structured attributes and BERT contextual text features. Second, the Bi-path Graph Convolution Module (BiGCM) extracts multi-granularity graph representations via separate sum and max aggregation paths. Third, the lightweight Gate Fusion Module (GFM) balances two feature streams via a learnable global scalar gate. The model is optimized with regularized Bayesian Personalized Ranking (BPR) loss on highly sparse recruitment data (99.97% sparsity). BiGCNG is evaluated on the Zhilian dataset, a real-world Chinese recruitment dataset, and outperforms five mainstream baselines notably, increasing MRR@5 by 7.67% and NDCG@5 by 5.48% on the Candidate subset, 2.30% and 0.61% on the Job subset against the best baseline, respectively. Several visualizations and hyperparameter analysis jointly validate the effectiveness and robustness of dual-path propagation and gate fusion. This work provides an effective multi-granularity graph learning paradigm for intelligent talent recruitment matching. Full article

Seismic site response analysis plays a fundamental role in predicting earthquake ground motions at the ground surface by accounting for soil effects on wave propagation. Although advanced nonlinear methods can accurately simulate complex soil behaviour, their use in routine engineering practice is challenging. Consequently, the Equivalent Linear (EQL) method remains widely adopted despite relying on simplifying assumptions, particularly the definition of the effective shear strain (ESS), which governs the selection of strain-compatible soil properties. An inappropriate ESS may lead to over- or underestimation of soil nonlinearity and, consequently, of the seismic response. This issue is especially relevant for mine tailings, whose cyclic behaviour remains poorly understood despite the significant risks associated with earthquake-induced failures of tailings storage facilities. This study investigates the influence of the ESS assumption using an in-house EQL code through a detailed case study and a parametric analysis comprising 2106 simulations, in which seventeen surface ground-motion intensity measures are evaluated as a function of the ESS coefficient, $R_{\gamma }$. The results confirm general trends reported in the literature but also identify cases in which neither the magnitude nor the direction of the effects of varying $R_{\gamma }$ can be inferred from $R_{\gamma }$ alone, as they depend on the interaction between the deposit response and the frequency content of the input motion. Consequently, while the conventional value of $R_{\gamma }=0.65$ is generally adequate for routine analyses, parametric studies are recommended for critical structures such as tailings storage facilities. Full article

In tasks such as autonomous driving, low-altitude remote sensing, intelligent surveillance, and low-altitude security, RGB-T object detection must maintain stable performance under complex conditions, including illumination variations, thermal-source interference, background clutter, and dense distributions of small objects. Existing multimodal detection methods typically rely on complex attention structures or heavyweight fusion modules, making it difficult to balance detection accuracy, model lightweightness, and deployment efficiency; moreover, modality noise and redundant background information are prone to joint propagation during shallow fusion, weakening the responses of small and weak objects. To address these issues, this paper proposes ZOS-Net, a lightweight RGB-T object detection network that integrates cross-modal relation enhancement and selective target awareness. Specifically, ZOS-Net introduces a Cross-Modal Fine-Grained Gated Fusion module, termed ZCGF, at the shallow P3 stage to enhance reliable complementary information from visible textures and infrared thermal responses; an Object-Aware Relation Enhancement module, termed OAGR, is introduced at the semantic bridging stage from P4 to P3 to generate object-relation priors; and a Selective Relation-Guided Target Perception Enhancement Module, termed SRTPEM, is further employed to perform foreground enhancement and weak background calibration on the final P3 features. Experimental results show that ZOS-Net achieves a favorable balance between detection performance and complexity on the M³FD, FLIR-aligned, and VEDAI datasets. On M³FD, ZOS-Net achieves 88.3% mAP50 and 59.5% mAP50:95 with only 4.74 M parameters and 7.9 G FLOPs; on FLIR-aligned, it achieves 83.5% mAP50 and 46.8% mAP50:95; and on VEDAI, it achieves 74.5% mAP50. These results indicate that the proposed method improves the detection performance of small and weak objects in complex backgrounds and provides a lightweight solution for RGB-T object detection on resource-constrained platforms. Full article

Ultrasonic pulse-echo imaging is one of the most widely used non-destructive evaluation (NDE) modalities for monitoring the structural integrity of reinforced-concrete civil infrastructure such as bridge decks, tunnel linings, and dam walls. In this acquisition geometry, a single low-frequency transducer is mechanically raster-scanned over the accessible top surface of the specimen and records one A-scan per scan position, simultaneously serving as transmitter and receiver. However, commonly used reconstruction algorithms such as the Synthetic Aperture Focusing Technique (SAFT) and Reverse Time Migration (RTM) tend to produce reconstructions of limited quality on this class of data because they do not adequately model the round-trip propagation kernel that is specific to the monostatic geometry, they do not separate the strong near-surface direct-arrival reflection from the bulk image, and they do not account for the persistent aggregate-induced clutter that contaminates every A-scan in concrete media. In this paper, we propose a clutter-aware reconstruction method for monostatic ultrasound acquisition (CARMA), whose main innovation is the joint integration of a monostatic-specific round-trip propagation model, a dedicated near-surface direct-arrival subspace, and a data-adaptive low-rank clutter subspace within a unified model-based reconstruction framework. Unlike existing reconstruction approaches, CARMA explicitly accounts for the co-located transmit–receive geometry through a squared-cosine round-trip directivity model while simultaneously separating scan-dependent direct-arrival contributions and aggregate-induced clutter from the desired reflectivity image. To verify the method under fully controlled and repeatable conditions, we generate intensive, physically realistic full-wave simulations with the k-Wave pseudo-spectral acoustic solver that reproduce a representative civil-infrastructure inspection scenario: three reinforced-concrete specimens with a stepped back wall of varying thickness, ten embedded ground-truth defects spanning steel tendon ducts and low-impedance polystyrene inclusions, a monostatic raster-scanned pulse-echo acquisition, and randomly distributed aggregate scatterers that reproduce the clutter of real concrete. Results on these intensive k-Wave simulations indicate that CARMA reconstruction yields approximately $2\times$ lower localization error than RTM and approximately $4\times$ lower localization error than SAFT, while recovering the deepest embedded defect with substantially better localization and contrast than the comparison methods. Full article

Background/Objectives: Sweet orange (Citrus sinensis (L.) Osbeck) is an economically important fruit crop that contributes substantially to food security and smallholder income in Mozambique. Despite this, the genetic diversity of the locally grown germplasm has not been characterized at the molecular level, limiting its improvement and conservation programs. This study assessed the genetic diversity and population structure of germplasm from 94 sweet orange trees sampled across four districts of Inhambane Province using DArTSeq single-nucleotide polymorphism (SNP) markers. Methods: After filtering 8111 SNPs for call rate (≥0.80) and minor allele frequency (≥0.01), 1263 markers were retained, of which 1144 were anchored to the nine chromosomes of the reference genome. Results: Sparse non-negative matrix factorization identified K = 1, indicating a single undifferentiated gene pool, supported by a smooth PCA scree with one weak axis. DAPC assigned individuals to their district only 38.3% of the time (random expectation = 25%; maximum a-score = 0.10), and the first two PCoA axes explained 10.33% of variation with complete district overlap, indicating no detectable geographic structure. Diversity was low, with observed heterozygosity (Ho = 0.247) exceeding expected heterozygosity (He = 0.138) and a negative inbreeding coefficient (Fis = −0.222). The pattern indicated a heterozygote excess consistent with the fixation of the heterozygous interspecific-hybrid genome under clonal propagation. A hierarchical analysis of molecular variance showed that differentiation among districts was negligible (0.04%), whereas 4.16% of variation was partitioned among orchards (farms) within districts, indicating that the little of the existing structure resides at the orchard level, confounded with propagation method and cultivar, rather than among districts. Most variation was partitioned within individuals (76.0%), and pairwise FST values (0.0005–0.0035) were uniformly low. Conclusions: These results indicate that the sweet orange orchards stem from a single, highly heterozygous gene pool redistributed through the exchange of seed and vegetative planting material. This underscores the need to introduce diverse external germplasm to broaden the genetic base for sustainable improvement in Mozambique. Full article

A new architecture and design method are proposed for field-programmable gate array (FPGA)-based Moore finite state machines (FSMs). The proposed architecture includes a core based on functional decomposition, a core based on twofold state assignment (TSA), and a block for replacing FSM inputs (RI) in the TSA-based core. TSA belongs to the class of structural decomposition methods. The conditions under which the proposed method leads to FSM circuits with better spatial characteristics than their counterparts without RI are identified. This improvement is accompanied by a degradation of temporal characteristics. The method targets Moore FSM circuits implemented with look-up table (LUT) elements. The TSA-based core is based on classes of compatible states. To reduce the number of these classes, we use the replacement of FSM inputs by additional variables. The efficiency of the proposed method is evaluated using experiments based on a known library of benchmark FSMs. The experiments show that the trade-off between LUT count and propagation time depends on the FSM group. For the SD group (14 benchmarks), the LUT count is reduced by 13.28%, while the propagation time increases by 41.08%. For the FD group (6 benchmarks), the LUT count is reduced by 8.09%, while the propagation time increases by 2.88%. Over all 53 benchmark FSMs, the LUT count is reduced by 2.20%, while the propagation time increases by 18.77%. Full article

In this study, we present the development of selective-area porous GaN (SPG) microarrays integrated with embedded quantum dots (QDs) for next-generation display applications. We report a dielectric hard mask-based architecture that withstands high etching voltages without suffering structural damage, overcoming the limitations of conventional polymer masks. This process provides precise control over both vertical and lateral pore propagation, achieving an effective pixel size of 15 μm and realizing an ultrahigh-resolution (>1270 ppi) display structure. Furthermore, optical performance measurements confirmed that the obtained array exhibited an excellent color gamut, reaching 119.2% of the NTSC and 95.6% of the Rec. 2020 standards. Therefore, the present dielectric hard mask-based patterning technology provides an effective solution for achieving both process stability and high-resolution pixel structures, enabling the fabrication of high-performance micro-LED displays with full-color capabilities. Full article

Parity-time ($\mathcal{PT}$) symmetry offers a well-established route for enhancing light-matter interaction by means of formation of exceptional points. The model of plane waves is commonly used for finding and investigating exceptional points in open multilayer systems. Here we study behaviors of TE-polarized Bessel light beams in $\mathcal{PT}$-symmetric multilayer structures. It is shown that, due to the translational invariance of the planar interfaces, the exceptional and diabolic points of the Bessel beam are identical to those of a conventional plane wave with the same angle of incidence. Basing on this equivalence, we adopt the method of scattering matrices to TE-polarized non-paraxial Bessel beams and determine positions of exceptional points depending on the transverse wave number and non-Hermiticity parameter for both propagating and evanescent beams. We reveal the evolution of exceptional and diabolic lines when the number of layers changes and find their link to the transmission and reflection spectra. This research may pave the way for exploiting light beams in non-Hermitian photonics. Full article

The Peregrine breather (PB), a classical localized solution of the nonlinear Schrödinger equation (NLSE), is widely used to describe the evolution of deep-water rogue waves. However, the influence of variable bathymetry on PB focusing remains insufficiently understood. A two-dimensional RANS–VOF numerical wave tank is therefore established using computational fluid dynamics (CFD) to investigate deterministic PB propagation over variable bathymetry. The model is validated through mesh- and time-step-sensitivity analyses and comparison with the analytical PB solution. Relative water depth, bathymetric interaction length, and bathymetric position are systematically examined. The results reveal for the first time a bathymetry-induced delayed-focusing phenomenon: the PB undergoes local defocusing over elevated topography and refocuses farther downstream after re-entering deeper water. The delay increases as water depth decreases. For k₀h_shelf > 1.363, increasing the interaction length mainly enhances the focusing delay, while self-focusing recovers in deeper water. In contrast, for k₀h_shelf < 1.363, an interaction length of approximately two carrier wavelengths disrupts the coherent PB structure and splits it into two wave packets. The onset position of bathymetric forcing has only a minor effect on the final delay. These results clarify how variable bathymetry modulates PB focusing and structural stability and provide a theoretical reference for nearshore extreme-wave risk assessment. Full article

Ground-based Global Navigation Satellite System (GNSS) stations provide continuous atmospheric remote sensing through electromagnetic propagation delays. Precise point positioning (PPP) yields zenith tropospheric delay (ZTD), and ZTD gradients provide a proxy for off-farm tropospheric structure that is unavailable to supervisory control and data acquisition (SCADA)-only forecasts. We propose a model combining a long short-term memory (LSTM) backbone, GNSS conditioning, and a graph neural network (GNN), denoted LSTM+GNN+GNSS, for 4 h wind-power forecasting. Historical PPP-derived ZTD and quality indicators condition a shared temporal representation; a ZTD-gradient direction proxy, turbine geometry, and observation confidence guide a gated graph-residual correction at 15–90 min. On 666 common Yandun test origins, we compare LSTM, LSTM+GNN, and LSTM+GNN+GNSS. The complete system achieves a normalized mean absolute error (nMAE) of 4.53% (4.527 ± 0.132% across three power-model seeds), reducing nMAE by 7.57% relative to LSTM+GNN and 8.23% relative to LSTM. Paired moving-block 95% confidence intervals support both comparisons, while Bonferroni-adjusted lead-wise tests agree from 30 to 225 min. These results demonstrate the incremental predictive value of the complete GNSS-conditioning pathway under the chronological holdout protocol. Full article

Carbon-fiber structural batteries represent a class of multifunctional energy-storage systems that integrate electrochemical energy storage with mechanical load-bearing capability. Unlike conventional batteries, which are mainly evaluated based on cell-level energy density, structural batteries provide new opportunities for system-level weight reduction by reducing inactive structural mass, improving space utilization, and enabling distributed energy storage within integrated structures. In recent years, substantial progress has been achieved in carbon-fiber electrodes, structural electrolytes, laminated devices, electrolyte topology engineering, and fully carbon-fiber structural batteries. Nevertheless, most reported advances have been demonstrated under relatively ideal static testing conditions, while maintaining multifunctional performance under manufacturing and long-term service conditions remains a critical challenge. This review systematically examines the development of carbon-fiber structural batteries from a reliability perspective. First, the system-level motivations and technological evolution are introduced, and existing architectures are categorized according to their integration depth and degree of multifunctional coupling. Carbon-fiber electrodes are then discussed with emphasis on balancing capacity, ion transport, cycling stability, mechanical property retention, interfacial robustness, and manufacturing scalability. Furthermore, structural electrolytes are reviewed from the viewpoint of topology-enabled regulation of ion transport and load transfer, with particular focus on the intrinsic trade-off between ionic conductivity and mechanical modulus. In addition, manufacturing routes and device architectures are analyzed from the perspective of multifunctionality-degrading defects, including voids, dry regions, coating cracks, weak interfaces, and current-collector discontinuities. Finally, retained multifunctionality is used as a reliability-oriented evaluation criterion to examine the preservation of electrochemical, mechanical, interfacial, and safety functions, with particular emphasis on the carbon-fiber-specific failure chain linking interfacial and manufacturing heterogeneities to multifunctionality-degrading defects, coupled-field localization, and damage propagation. This review emphasizes that reliable carbon-fiber structural batteries require application-specific and coordinated optimization of materials, interfaces, electrolyte topology, coupled degradation behavior, and validation protocols. Full article

Cavitation erosion severely limits the service life of marine hydraulic components. Magnetron sputtering was utilized to fabricate TiSiN/AlCrTiVNbN high-entropy nitride nanomultilayer films, whose modulation periods ranged from 4 nm to 23 nm. The effects of modulation period on the microstructure, mechanical properties, and cavitation erosion resistance were systematically investigated. All films exhibited a single-phase face-centered cubic structure with a preferred orientation (200) plane. The film with a 6 nm layer period reached the highest hardness (38.9 GPa) and elastic modulus (214.1 GPa), which is due to the many closely fitting interfaces that effectively stopped line defect movement; in cavitation erosion tests in 3.5% salt water, this film had the lowest mass loss (0.8 mg) and its surface stayed in the best condition. Its superior cavitation erosion resistance originates from the synergistic effects of high-density coherent interfaces that obstructed crack propagation, enhanced mechanical properties that provided excellent resistance to plastic deformation, and the formation of a protective oxide layer during cavitation. As this work shows, optimizing the layer period is an effective strategy; it allows the design of strong protective films for ocean use. Full article

Halo-gravity traction (HGT) is widely used as a preoperative treatment for severe pediatric spinal deformities by gradually applying traction forces through a cranial fixation system. Although the clinical effectiveness of HGT has been extensively documented, uncertainty associated with the structural response and operational behavior of mobile HGT systems has received comparatively little attention. This study presents a preliminary coupled clinical–structural uncertainty quantification framework for engineering evaluation of a mobile halo-gravity traction wheelchair. Reduced-order structural and clinical surrogate models are combined with Latin Hypercube Sampling, bounded beta-distributed input variables, third-order Polynomial Chaos Expansion, and Sobol sensitivity analysis. Sixteen uncertain patient, operational, geometric, fabrication, and halo-interface parameters are propagated through models of combined beam–column loading, elastic buckling, welded-joint loading, scenario-based wheelchair tipping stability, halo-pin load concentration, traction delivery, and representative clinical response. Thirty-two alternative beta-distribution shape cases are examined to assess sensitivity to the assumed marginal distribution shapes. Within the investigated bounds and reduced-order modeling assumptions, the computational screening model did not predict loss of the prescribed structural margins. The results identify the parameters governing the different response modes and demonstrate how uncertainty in mobility-related acceleration, support geometry, traction loading, fabrication efficiency, and halo-pin load transfer can be evaluated within a unified framework. Because the model has not yet been validated through dedicated prototype testing or higher-fidelity full-system simulation, the results should be interpreted as uncertainty-informed engineering screening rather than as experimental verification or clinical certification of device safety. Full article

Hyperspectral images contain abundant spectral information and provide fine-grained spatial representations. However, their high dimensionality, severe spectral redundancy, subtle inter-class differences, mixed boundary regions, and limited labeled samples pose significant challenges to accurate classification. Convolutional neural networks (CNNs) have limited capability in modeling non-Euclidean structural relationships, whereas graph convolutional networks (GCNs) are susceptible to the quality of superpixel segmentation and noise propagation over graph structures. To address these issues in hyperspectral image classification, this paper proposes a Reliability-Aware Cross-Branch Contrastive Graph Convolutional Network (RACB-CGCN). The proposed method employs a dual-branch CNN–GCN architecture to extract pixel-level local spectral–spatial features and superpixel-level structural features, respectively. A superpixel reliability estimation and propagation control mechanism is introduced to assess node reliability based on the discrepancy between pixel-level features and superpixel-reconstructed features. This mechanism effectively suppresses the propagation of noisy information caused by impure superpixels and mixed boundary regions. Meanwhile, a cross-branch supervised contrastive learning strategy is developed to enhance semantic consistency between the CNN and GCN branches, thereby improving intra-class compactness and inter-class separability. In addition, a class-adaptive fusion module is designed to dynamically adjust the contributions of the two branches according to the feature characteristics of different land-cover classes. Experimental results demonstrate that the proposed method effectively exploits the complementary information between pixel-level fine-grained features and superpixel-level structural features, leading to improved classification accuracy and robustness in hyperspectral image classification. Full article