Search Results (5,994)

To address the challenges associated with laser powder bed fusion (LPBF) of overhanging structures—namely warping deformation, powder adhesion, and inadequate forming accuracy—this study investigates the optimization of the support–part contact interface using Inconel 625 alloy. The objective is to achieve high-quality part formation with minimal support structures. A Taguchi experimental design was employed to systematically evaluate the effects of key block support parameters—tooth height, tooth top length, tooth base length, and tooth base spacing—on the forming performance of overhanging structures, with forming accuracy and support removability as the optimization targets. The results reveal that tooth top length significantly influences both the forming accuracy of overhanging specimens and the ease of support removal. Specifically, an increase in tooth top length leads to a rapid reduction in specimen deformation, but simultaneously increases the difficulty of support removal. When the tooth top length was set to 0.1 mm, all overhanging specimens failed to form successfully. Tooth base length also plays a critical role in support removability, with removal difficulty initially decreasing and then stabilizing as the tooth base length increases. Based on the trade-off between forming quality and support removability, the optimal parameter combination was identified as: tooth height of 0.4 mm, tooth top length of 0.7 mm, tooth base length of 1.0 mm, and tooth base spacing of 0.3 mm. A validation experiment conducted using this optimized configuration demonstrated good forming accuracy in the support contact area, with a deformation value of −0.208 mm, confirming the effectiveness and reliability of the proposed parameters. This study not only provides a theoretical foundation for the optimal design of block supports in LPBF but also offers experimental data and practical guidance for selecting support parameters in the fabrication of overhanging structures. Full article

Accurate segmentation of structural cracks is a core prerequisite for quantifying crack parameters, assessing damage severity, and providing early warning of structural safety. However, different types of structures exhibit significant individual variations in features such as color, texture, and brightness. Consequently, commonly used image segmentation algorithms struggle to establish a universal mathematical model, making it challenging to robustly identify and precisely segment crack targets amidst multi-feature disparities. To address the issue, this paper proposes a crack-segmentation algorithm based on bimodal image fusion and brightness piecewise linear enhancement (CSA-BB), and further enables parameter extraction and crack monitoring. The algorithm utilizes the complementary properties of visible-light and pseudo-color images for bimodal image fusion, thereby enhancing the detailed features of cracks. Furthermore, a brightness piecewise linear function has been devised that automatically selects appropriate parameters for image enhancement of structural cracks across varying background brightness. Subsequently, the crack region is effectively segmented using the bottom-hat transform and the OTSU algorithm. Ultimately, the crack’s safety level is determined from the acquired crack parameters, thereby enabling effective monitoring and assessment of the crack development process. In this paper, the proposed method achieves the best segmentation performance with a Dice coefficient of 0.4511 and a Jaccard index of 0.2981. Compared to the second-best algorithm, it yields significant improvements of 26.9% and 34.5%, respectively, demonstrating higher consistency with the ground truth. Moreover, superior computational efficiency and robustness are achieved, fulfilling the operational demands of real-world engineering environments. Full article

Honey authenticity control remains analytically challenging due to the complexity of its matrix and the increasing sophistication of adulteration practices. While chromatographic, spectrometric, and isotopic methods provide high confirmatory accuracy, their routine application is constrained by cost, time, and infrastructure requirements. In this context, fluorescence spectroscopy has emerged as a rapid, non-destructive, and cost-efficient screening approach capable of capturing subtle matrix-level compositional variations. This review critically evaluates the application of steady-state and excitation–emission matrix (EEM) fluorescence in honey quality and authenticity assessment. Fluorescence is positioned within tiered analytical frameworks as a first-line or intermediate screening tool preceding confirmatory chromatographic or NMR-based analyses. Emphasis is placed on intrinsic fluorophore domains, excitation–emission measurement strategies, and chemometric interpretation, including multiway analysis and supervised classification models. Recent developments in portable LED-based systems, laser-induced fluorescence, nanoparticle-based probes, and data-fusion strategies are discussed alongside key limitations related to matrix effects, spectral overlap, reproducibility, and model transferability. The review provides a structured framework for the strategic integration of fluorescence spectroscopy into contemporary honey authentication workflows. Full article

Additive manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized polymer-based fabrication through design freedom and material efficiency. This work presents a comprehensive statical optimization of FDM parameters affecting the flexural properties of acrylonitrile/butadiene/styrene (ABS) specimens. The effects of layer thickness (0.15–0.25 mm), infill density (30–70%), printing speed (35–95 mm/s), and build orientation (Flat, On-edge, Vertical) were investigated following ASTM D790 standards. A Taguchi L9 orthogonal array coupled with ANOVA analysis was employed to quantity parameter significance. According to the ANOVA analysis, infill density was identified as the most influential parameter, accounting for 61.3% of the variation in flexural strength (σ_f) and 60.1% in flexural modulus (E_b). The optimal configuration (0.25 mm layer thickness, 70% infill, 65 mm/s speed, horizontal orientation) yielded a flexural strength of 84.9 MPa and modulus of 2.54 GPa. Microstructural observations confirmed that higher infill and moderate speed improved interlayer fusion and reduced void formation. The developed Taguchi–ANOVA framework offers quantitative insights for tailoring process–structure–property relationships in polymer-based additive manufacturing. Full article

Millimeter-wave (mmWave) radar point clouds offer a privacy-preserving solution for Human Activity Recognition (HAR), but their inherent sparsity and noise limit single-modal performance. While multimodal fusion mitigates this issue, existing methods often suffer from severe negative transfer during visual degradation and incur high computational costs, unsuitable for edge devices. To address these challenges, we propose Tac-Mamba, a lightweight cross-modal state space model. First, we introduce a topology-guided distillation scheme that uses a Spatial Mamba teacher to extract structural priors from visual skeletons. These priors are then explicitly distilled into a Point Transformer v3 (PTv3) radar student with a modality dropout strategy. We also developed a Trust-Aware Cross-Modal Attention (TACMA) module to prevent negative transfer. It evaluates the reliability of visual features through a SiLU-activated cross-modal bilinear interaction, smoothly degrading to a pure radar-driven fallback projection when visual inputs are corrupted. Finally, a Lightweight Temporal Mamba Block (LTMB) with a Zero-Parameter Cross-Gating (ZPCG) mechanism captures long-range kinematic dependencies with linear complexity. Experiments on the public MM-Fi dataset under strict cross-environment protocols demonstrate that Tac-Mamba achieves competitive accuracies of 95.37% (multimodal) and 87.54% (radar-only) with only 0.86M parameters and 1.89 ms inference latency. These results highlight the model’s exceptional robustness to modality missingness and its feasibility for edge deployment. Full article

Constructing resilient Ecological Security Patterns (ESPs) in polycentric urban agglomerations is computationally challenging due to persistent scale mismatches between local planning and regional strategies. To address this, we developed a novel Proactive Integration Mechanism (PIM), a computational framework that dynamically optimizes ESPs by algorithmically fusing multi-source geospatial data. The PIM integrates three innovative components: (1) a Function–Structure–Policy data fusion approach that couples Self-Organizing Map clustering of ecosystem services with Morphological Spatial Pattern Analysis and policy data to identify ecological sources; (2) a Dual-Feedback Mechanism that hybridizes circuit theory with an Improved Ant Colony Optimization algorithm for dynamic corridor delineation; and (3) complex network analysis to derive targeted interventions from topological properties. Applied to a node city of the Chengdu-Chongqing Economic Circle, the PIM identified 22 integrated ecological sources and 37 corridors. The optimized network showed enhanced resilience: a deterministic 20.5% increase in circuit redundancy (α-index) and an 8.6% improvement in overall connectivity (γ-index), achieved through minimal topological modifications. Temporal validation (2000–2020) confirmed the high stability of the identified patterns. This study provides a potentially replicable and computationally robust framework that bridges spatial ecology with optimization algorithms, offering a promising paradigm for constructing ESPs in node cities within subtropical urban agglomerations. Full article

Analyzing large-scale student feedback is critical for higher education quality assurance, yet manual analysis is inefficient and subjective. This paper proposes an integrated framework that unifies supervised classification, unsupervised clustering, and retrieval-augmented generation (RAG) to produce evidence-grounded and actionable insights. Ensemble-based supervised models perform thematic classification, while multi-encoder embedding fusion enables unsupervised discovery of coherent feedback clusters. A multi-stage RAG module integrates category predictions and cluster structure to retrieve representative evidence and generate transparent summaries with citation traceability. The framework is evaluated on student feedback collected from a Central Asian university and two public benchmarks, EduRABSA and Coursera course reviews, covering seven thematic categories. The supervised ensemble achieves 83.0% accuracy and 0.829 Macro-F1 on the primary dataset, while unsupervised clustering attains a silhouette score of 0.271 under the best fusion strategy. Independent evaluation on external benchmarks yields ensemble accuracy of 81.1% on EduRABSA and 49.8% on Coursera, confirming the framework’s adaptability across diverse educational contexts. By leveraging supervised labels and unsupervised structure, the proposed framework enables evidence-grounded, category-aware LLM-based summaries that faithfully reflect the diversity and distribution of student feedback and support actionable educational decision-making. Full article

Historic buildings often exhibit high energy use intensity (EUI), while conservation constraints limit envelope retrofits, making it difficult to identify robust and actionable operational predictors. Using four in-use historic buildings in Shenyang, China, this study presents a pilot methodological demonstration with a controlled-comparability workflow consisting of two linked layers: (i) a Driver layer of intervenable operational variables and (ii) a Pathway layer of calibrated EnergyPlus heat-balance terms for physics-informed interpretation. Three importance approaches (Spearman, wrapper RFE with XGBoost, and Random Forest) are compared; rankings are fused via reciprocal rank fusion, and stability is tested using cross-period rolling validation across Top-K feature sets. After similarity screening, EUI variation is better explained by operational predictors and the corresponding simulated loss channels than by macro-scale structural heterogeneity. Infiltration-related indicators and envelope/infiltration loss components remain consistently prominent, while Spearman importance is less stable in the Pathway layer under seasonal switching and nonlinear coupling. A Top-10 subset provides a favorable accuracy–stability trade-off. The proposed Driver–Pathway mapping supports conservation-compatible prioritization hypotheses within a simulation-consistent interpretive framework; findings are associational and context dependent and should be validated through field measurements and experimental or quasi-experimental studies before prescriptive claims are made. Full article

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is crucial for brain tumor diagnosis, clinical treatment decisions, and advancing research. CNNs and Transformers have dominated this area, but CNNs struggle with long-range modeling, whereas Transformers are limited by the high computational costs of self-attention. Recently, Mamba has garnered significant attention due to its remarkable performance in long sequence modeling. However, the original Mamba architecture, designed primarily for 1D sequence modeling, fails to effectively capture the spatial and structural relationships essential for brain tumor segmentation. In this paper, we propose FocusMamba, a Mamba-based model inspired by human visual observation patterns, which jointly enhances local detail modeling and global contextual understanding. FocusMamba consists of three components: (i) a novel hierarchical and tri-directional Mamba unit that elevates attention from the global to the window level, reinforcing local semantic feature extraction, while simultaneously achieving window-level interactions to maintain broader global awareness, (ii) a large kernel convolution unit that captures long-range dependencies within whole-volume features, overcoming the limitations of Mamba’s single-scale context modeling, and (iii) a fusion unit that enhances the overall feature representation by fusing information from different levels. Extensive experiments on the BraTS 2023 and BraTS 2020 datasets demonstrate that FocusMamba achieves superior segmentation performance compared with several advanced methods. Full article

Additive manufacturing by Selective Laser Melting (SLM) or, precisely, Laser Powder Bed Fusion (L-PBF), offers new opportunities for producing electrically functional metal components with tailored geometric designs and material properties. In this study, the electrical contact resistance and related properties of 3D-printed samples made from Al5086 aluminum alloy are tested. The benefits of Al5086 include flexibility without cracking, welding ability and exceptional resistance to corrosion in saltwater and industrial environments. This makes it an excellent candidate for power electric applications due to its good electrical conductivity and corrosion resistance. In this study, an analysis is performed to assess the impact of internal volumetric properties and surface parameters on general contact resistance performance. This analysis combines advanced testing procedures and parameter identification of the electric contact resistance model. This study investigates how these parameters affect contact resistance, which is a critical factor in the reliability of electrical devices. Electrical contact resistance was measured using a dedicated test setup that applied consistent pressure and maintained directional alignment. The results show that the printing direction of the samples slightly affects resistance values due to the continuity of current paths along the build direction, likely due to homogenous inter-layer boundaries and mechanical stress distribution. These findings suggest that both print orientation and internal structure must be considered when designing 3D-printed contact elements for electrical applications. Overall, this study demonstrates the feasibility of using L-PBF-fabricated aluminum components in electric applications where both electrical and structural performances are essential. Full article

To address the reduced accuracy of stereo matching networks near object boundaries and disparity discontinuities, a double cost–volume stereo matching network with entropy-difference-guided fusion is proposed. The proposed network was built based on RAFT-Stereo. It employs a pretrained backbone to extract multi-scale features and uses deformable attention for cross-scale feature fusion. A shallow image-guided branch was used to generate pixel-wise constraint information to limit the magnitude of sampling offsets and alleviate cross-structure sampling. Based on the extracted features, a group-wise correlation cost–volume and a normalized correlation cost–volume were constructed. Both cost–volumes were regularized by 3D Hourglass networks, and a structure-consistent intra-scale aggregation module was introduced during the regularization of the group-wise correlation cost–volume. The two aggregated results were then fused by the entropy-difference-guided fusion module to obtain the final cost–volume. The experimental results show the effectiveness of the proposed network in the Scene Flow, KITTI, and ETH3D datasets, achieving an endpoint error of 0.45 px and a >3 px error rate of 2.41% on the Scene Flow dataset. Full article

Artificial intelligence (AI) has become a transformative tool in the field of molecular biosensing, enabling data-driven optimization in sensor design, signal processing, and real-time monitoring. AI promotes the discovery of biomarkers, the design of high-affinity receptors, and the rational engineering of sensing materials, thereby enhancing sensitivity, specificity, and detection accuracy. In the development of biosensors, AI-assisted strategies have accelerated the identification of novel molecular targets, guided the design of proteins and aptamers with enhanced binding performance, and optimized plasmonic and nanophotonic structures through forward prediction and inverse design frameworks. The integration of artificial intelligence has significantly enhanced the performance of various biosensing platforms, including optical, electrochemical, and microfluidic biosensors. It also enabled automatic feature extraction, noise reduction, dimensionality reduction, and multimodal data fusion, overcoming the challenges posed by complex signals, environmental interference, and device variations. These capabilities are particularly crucial for wearable molecular biosensors, as low signal strength, motion artifacts, and fluctuations in physiological conditions impose strict requirements on robustness and real-time reliability. This review systematically summarizes the latest advancements in AI-assisted molecular biosensors, highlighting representative sensing strategies and algorithms for wearable and real-time monitoring, and discusses the current challenges and future development opportunities of intelligent biosensing technologies. Full article

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has driven the global COVID-19 pandemic, imposing a tremendous burden on public health. As the virus continually evolves through rapid mutations, the pandemic has transitioned into a prolonged endemic phase. Despite the development of novel drugs and vaccines, clinical outcomes remain suboptimal for vulnerable populations, including the elderly and those with comorbidities or compromised immunity. Tea polyphenols, a class of structurally diverse and bioactive nutraceuticals, may modulate viral entry, replication, and host inflammatory pathways implicated in disease progression through pleiotropic effects on viral attachment, membrane fusion, intracellular replication, and proteolytic processing. Here, we provide an updated chemo-biological perspective on the antiviral and immunomodulatory mechanisms of tea polyphenols against SARS-CoV-2. Current evidence highlights their potential to serve as promising candidates for further mechanistic and translational investigation as adjunctive strategies and nutraceuticals for COVID-19 management. Importantly, no large-scale randomized controlled trials have yet demonstrated clinical benefit of tea polyphenols in COVID-19. Full article

With the widespread adoption of various types of Unmanned Aerial Vehicles (UAVs), their non-compliant operations pose a severe challenge to public safety, necessitating the urgent identification and detection of UAV targets. However, in complex backgrounds, UAV targets exhibit small-scale dimensions and low contrast, coupled with extremely low signal-to-noise ratios. This forces conventional target detection methods to confront issues such as feature convergence, missed detections, and false alarms. To address these challenges, we propose a Multi-View Feature Fusion Network (MVFF) that achieves precise identification of small, low-contrast UAV targets by leveraging complementary multi-view information. First, we design a collaborative view alignment fusion module. This module employs a cross-map feature fusion attention mechanism to establish pixel-level mapping relationships and perform deep fusion, effectively resolving geometric distortion and semantic overlap caused by imaging angle differences. Furthermore, we introduce a view feature smoothing module that employs displacement operators to construct a lightweight long-range modeling mechanism. This overcomes the limitations of traditional convolutional local receptive fields, effectively eliminating ghosting artifacts and response discontinuities arising from multi-view fusion. Additionally, we developed a small object binary cross-entropy loss function. By incorporating scale-adaptive gain factors and confidence-aware weights, this function enhances the learning capability of edge features in small objects, significantly reducing prediction uncertainty caused by background noise. Comparative experiments conducted on a multi-perspective UAV dataset demonstrate that our approach consistently outperforms existing state-of-the-art methods across multiple performance metrics. Specifically, it achieves a Structure-measure of 91.50% and an F-measure of 85.14%, validating the effectiveness and superiority of the proposed method. Full article

Salient Object Detection (SOD) in Optical Remote Sensing Images (ORSIs) aims to localize and segment visually prominent objects amidst complex backgrounds and extreme scale variations. However, we observe that current frequency-aware methods typically rely on a naive feature aggregation paradigm, merging frequency and spatial features via simple concatenation, addition, or direct combination. This shallow interaction overlooks the inherent semantic misalignment between the two domains, resulting in feature redundancy and poor boundary delineation. To address this limitation, we propose the Cooperative Hybrid Domain Network (CHDNet), a framework designed to facilitate synergistic cooperation between heterogeneous domains. Specifically, we propose the Cross-Domain Multi-Head Self-Attention (CD-MHSA) mechanism as a semantic bridge following the encoder. It employs a dimension expansion strategy to construct a Unified Interaction Manifold and utilizes a Frequency Anchor Interaction mechanism to achieve precise modulation of spatial textures using global spectral cues. Furthermore, to address the dual challenges of lacking explicit interpretation mechanisms for semantic co-occurrence and the susceptibility of topological structures to fracture in complex scenes during the decoding phase, we design a Multi-Branch Cooperative Decoder (MBCD) comprising three parallel paths: edge semantics, global relations, and reverse correction. This module dynamically integrates these heterogeneous clues through a Cooperative Fusion Strategy, combining explicit global dependency modeling with dual-domain reverse mining. Extensive experiments on multiple benchmark datasets demonstrate that the proposed CHDNet achieves performance superior to state-of-the-art (SOTA) methods. Full article