Search Results (1,747)

Leaves of vascular plants exhibit a tip-to-base xylem conduit widening pattern, which minimizes hydraulic resistance as leaves expand. The extent to which different leaf vein orders conform to this pattern remains largely unexplored, particularly in species with dichotomously branching veins. In this study, we focused on Ginkgo biloba, a model species with dichotomous venation, to explore tracheid variation across leaf vein orders and its linkage with hydraulic path length. Across 108 leaves from three sites, tracheid hydraulic diameter (D) scaled significantly with hydraulic path length (L) at an overall scaling slope of 0.33 (95% CI: 0.31–0.34, r² = 0.53, p < 0.001). Scaling exponents differed significantly among sites and vein orders, and D decreased progressively with increasing vein order. This study provides a potentially valuable approach for investigating the effects of hydraulic path length on xylem conduit diameter and offers a framework for subsequent research on plant venation and hydraulic transport networks. Full article

The large-scale integration and grid connection of renewable energy sources and charging stations introduce a multitude of nonlinear and impact loads, resulting in more severe distortion and higher complexity of disturbance signals in power systems. As a consequence, power quality disturbances (PQDs) in active distribution networks, including overvoltage and harmonics, display greater randomness and diversity, which increases the challenge of PQD identification. To tackle this problem, this study presents a dual-channel early-fusion approach for PQD recognition based on Spatial Post-MultiScale Fusion Entropy (SMFE). SMFE is used as an entropy-based feature-construction pipeline in which a time–frequency representation is formed prior to spatial post-multiscale aggregation to produce a compact complexity map complementary to waveform morphology. Subsequently, a dual-channel model is constructed by integrating waveform-morphology input with SMFE-derived complexity features for joint learning. By leveraging the ConvNeXt architecture and a Squeeze-and-Excitation (SE) mechanism, a multimodal channel-recalibration model is implemented to emphasize informative feature responses during PQD recognition. Experimental verification with simulated signals shows that the proposed approach achieves an identification accuracy of 97.83% under an SNR of 30 dB, indicating robust performance under the tested noise settings. Full article

Fractures are becoming a bigger and bigger global health problem, with an estimated 178 million new cases each year and 455 million people living with disabilities caused by fractures. Donor site morbidity, the risk of immune rejection, and limited functional integration all make current grafting techniques less effective. Biomaterials that come from nature, like collagen, gelatin, chitosan, alginate, hyaluronic acid (HA), and silk fibroin, have become promising scaffolds because they are bioactive, mimic the extracellular matrix (ECM), and can be broken down by enzymes. Crosslinking and composite reinforcement can greatly change how well they work. For example, collagen scaffolds that are highly crosslinked with glutaraldehyde keep up to 51.9% of their tensile strength after being exposed to enzymes, while non-crosslinked scaffolds only keep 12% of their strength. Chitosan–hydroxyapatite matrices, on the other hand, can reach compressive strengths of 2–12 MPa, which is close to the strength of cancellous bone. Additive manufacturing and 4D printing allow for precise control of structures and the ability to change their shape over time, which helps with vascularization and mechanical adaptation. Injectable and in situ-forming hydrogels show clinically important results, such as filling 85% of osteochondral defects in rabbits, improving left ventricular ejection fraction by up to 9% in large-animal cardiac models, and speeding up healing by 25–40% in chronic wounds. Even with these improvements, it is still hard to get batch consistency, a standardized way to test mechanical properties, and production that meets GMP (Good Manufacturing Practices) standards and can be scaled up. Full article

With the increasing penetration of converter-based devices, harmonic distortion has become a major challenge for power quality monitoring in large-scale power systems. This study presents a systematic review of methods for modeling harmonic sources and their applicability to real-time monitoring of power distribution systems. The review was conducted following PRISMA guidelines, considering literature published between 2000 and 2026. Searches were performed across Scopus, IEEE Xplore, Web of Science, ScienceDirect, and MDPI using predefined keywords. A total of 128 peer-reviewed journal articles were included. Potential sources of bias were qualitatively assessed, including selection, retrieval, and classification bias; however, residual bias may still arise from database selection, keyword design, and study classification. A structured comparative framework is introduced, based on a six-dimension coverage scoring scheme and maturity analysis, enabling consistent evaluation across both methodological and deployment aspects. The robustness of this framework was evaluated using leave-one-out and perturbation analyses, indicating low variability in coverage scores and stable rankings across both corpora. A taxonomy of harmonic source modeling approaches is proposed. Comparative synthesis indicates that measurement-based approaches, particularly those leveraging distribution-level PMUs, show strong potential for real-time monitoring. Key challenges include D-PMU placement, data integration, and computational scalability. Future work should focus on physics-informed AI and digital twin-based monitoring. Full article

Accurate and rapid transient voltage stability assessment is crucial for the safe and stable operation of new energy bases in desert and grassland regions. Existing deep learning methods fail to adequately capture the high-dimensional dynamic coupling features of transient voltage signals in large-scale renewable energy bases with UHVDC transmission, and suffer from poor performance under class-imbalanced sample conditions. This paper proposes a transient voltage stability assessment method utilizing continuous wavelet transform (CWT) time–frequency images and a deep residual network (ResNet-50). CWT with the Morlet wavelet basis converts voltage time-series signals into multi-scale time–frequency images to simultaneously capture temporal and frequency-domain transient features. An improved focal loss (FL) function is introduced to dynamically adjust category weights based on actual sample distribution, enhancing model robustness under extreme class imbalance. The proposed method is validated on a modified IEEE 39-bus system incorporating the Qishao UHVDC line and wind/photovoltaic integration in Northwest China, using 1490 simulation samples under diverse fault scenarios. Results demonstrate that the proposed CWT-ResNet achieves 98.88% accuracy, 94.74% precision, 100% recall, and 97.29% F1-score, outperforming SVM, 1D-CNN, and 1D-ResNet baselines. Under 5 dB noise conditions, the method maintains over 90% accuracy, demonstrating strong noise robustness. 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

The urgent need to decarbonize the global cement industry is compounded by the declining availability of conventional supplementary cementitious materials (SCMs). Limestone-calcined clay cement (LC3) emerges as a highly sustainable alternative, enabling up to 50 percent clinker replacement and an approximate 40 percent reduction in carbon dioxide emissions. Unlike existing reviews that focus on basic material properties, this paper critically bridges the gap between fundamental hydration thermodynamics and next-generation sustainable engineering applications. Through a structured bibliographic analysis of 135 contemporary sources published between 2000 and 2026, it traces the evolution of LC3 from a laboratory innovation to a highly promising solution for large-scale industrial implementation and circular economy integration. The discussion highlights the synergistic alumina carbonate reaction. This reaction forms carboaluminate phases. These phases significantly densify the microstructure and enhance long term durability. Key engineering properties are examined, contrasting rheological challenges from high water demand and carbonation susceptibility against its exceptional chloride resistance in aggressive environments. The transition to field application is thoroughly assessed, emphasizing technological advances in flash calcination, environmental footprint reduction through life cycle assessment (LCA), and production scalability. Finally, rather than restating known challenges, this review exposes the limitations of current empirical mitigation strategies. It proposes a targeted research agenda focused on molecular-level green admixture design and field calibrated durability models to support the integration of LC3 into emerging sustainable technologies such as 3D concrete printing. Full article

Marine electric-source time domain electromagnetic (TDEM) surveys typically involve the simultaneous movement of transmitters and receivers, which generates a large number of transmitter–receiver pairs. This acquisition geometry creates notable challenges for 3D inversion, mainly because of the large data volume and high computational cost. However, the electromagnetic “sensitive region” for each transmitter–receiver pair is much smaller than the full survey area. Based on this feature, we propose an efficient 3D inversion approach using the footprint technique. By clearly defining the sensitivity region, referred to as the footprint domain, for each pair, the method builds the sensitivity matrix only within localized subsurface regions that significantly affect the observed response. This approach greatly reduces both forward modeling cost and memory requirements. The forward modeling adopts an integral equation method combined with cosine transforms for fast 3D field computation, while the inversion framework uses a regularized conjugate-gradient algorithm, further accelerated by parallel computing under footprint domain constraints. Numerical simulations also examine the effects of offset, time channel, seawater thickness, and resistivity on the footprint domain, helping clarify the spatiotemporal diffusion behavior of TDEM fields in shallow marine environments. Tests on representative models show that the proposed method remains stable and accurate under complex geological conditions while significantly improving computational efficiency. In particular, the footprint domain technique improves inversion speed by about 55% compared with full domain inversion. These results indicate that the proposed approach provides a reliable and scalable option for large-scale 3D inversion of marine TDEM data. Full article

Fractures as critical discontinuous structural planes in rock masses, directly govern their stability and serve as the core controlling factor in rock mechanics engineering. Existing deep learning models for fracture extraction face persistent challenges, including imbalanced integration of deep and shallow features, limited suppression of background noise, inadequate multi-scale feature representation, and large parameter sizes—making it difficult to strike a balance between detection accuracy and deployment efficiency. Focusing on the Wanshanshan quarry in Yunnan, this study first constructs a high-precision digital model using close-range photogrammetry and 3D real-scene reconstruction. A lightweight yet high-accuracy intelligent detection method, termed MSEM-Deeplabv3+, is then proposed for rock mass fracture extraction. The model adopts lightweight MobileNetV2 as the backbone network, incorporating inverted residual modules and depthwise separable convolutions, resulting in a parameter size of only 6.02 MB and FLOPs of 30.170 G—substantially reducing computational overhead. Furthermore, the proposed MAGF (Multi-Scale Attention Gated Fusion) and SCSA (Spatial-Channel Synergistic Attention) modules are integrated to enhance the representation of fracture details and semantic consistency while effectively suppressing multi-source and multi-scale background interference. Experimental results demonstrate that the proposed model achieves an mPA of 89.69%, mIoU of 83.71%, F1-Score of 90.41%, and Kappa coefficient of 80.81%, outperforming the classic Deeplabv3+ model by 5.81%, 6.18%, 4.53%, and 9.2%, respectively. It also significantly surpasses benchmark models such as U-Net and HRNet. The method accurately captures fine and continuous fracture details, preserves the spatial distribution of long-range continuous fractures, and maintains robust performance on the CFD cross-scene dataset, showcasing strong adaptability and generalization capability. This approach effectively mitigates the risks associated with manual high-altitude inspections and provides a lightweight, high-precision, non-contact intelligent solution for fracture detection in high-steep rock slopes. Full article

In road planning and landscape design, data collection emphasizes existing site conditions, particularly in projects involving modifications rather than new construction, as such data directly inform subsequent planning decisions. Beyond conventional surveying techniques, large-scale street-region digital elevation models can be generated using aerial imagery acquired from unmanned aerial vehicles. The point clouds derived from these aerial photographs provide a basis for constructing spatial models applicable to street landscape and road planning. In this study, aerial data were processed using Pix4D software 4.8.4 to generate the initial spatial model, which was subsequently integrated into a building information modeling-based design framework in Autodesk Revit 2022. This approach enabled rapid and precise design outputs, while the resulting BIM model was further applied to mapping applications to establish a foundational database for regional public works. Full article

Hyperspectral imagery (HSI) offers rich spectral signatures and fine-grained spatial structures for remote sensing, but practical HSI classification is often constrained by scarce labels and complex geometric disturbances, including translation, rotation, scaling, and shear. Existing deep models are typically developed under Euclidean assumptions and rely on data-hungry training pipelines, which makes them brittle in the few-shot regime. To address this challenge, we propose EMNet, a Lie-group-based Equivariant Manifold Network for few-shot HSI classification that explicitly encodes geometric invariance and improves discriminative accuracy. EMNet couples an SE(2)-based Equivariance-Guided Module (EGM) to enforce equivariance to translations and rotations with an affine Lie-group-based Characteristic Filtering Convolution (CFC) that models scaling and shearing on the feature manifold while adaptively suppressing redundant responses. Extensive experiments on WHU-Hi-HongHu, Houston2013, and Indian Pines demonstrate state-of-the-art performance with competitive complexity, achieving OAs of 95.77% (50 samples/class), 97.37% (50 samples/class), and 96.09% (5% labeled samples), respectively, and yielding up to +3.34% OA, +6.01% AA, and +4.14% Kappa over the strong DGPF-RENet baseline. Under a stricter 25-samples-per-class protocol with 10 repeated random hold-out splits, EMNet consistently improves the mean accuracy while exhibiting lower variance, indicating better stability to sampling uncertainty. On the city-scale Xiongan New Area dataset with extreme long-tail imbalance (1580 × 3750 pixels, 256 bands, and 5.925 M labeled pixels), EMNet further boosts OA from 85.89% to 93.77% under the 1% labeled-sample protocol, highlighting robust generalization for large-area mapping. Beyond point estimates, we report mean ± SD/SE across repeated splits and provide rigorous statistical validation by computing Yule’s Q statistic for class-wise behavior similarity, performing the Friedman test with Nemenyi post hoc comparisons for multi-method ranking significance, and presenting 95% confidence intervals together with Cohen’s d effect sizes to quantify practical improvement. Full article

Referring Remote Sensing Image Segmentation (RRSIS) is a representative multimodal understanding task for remote sensing, which segments designated targets from remote images according to free-form natural language descriptions. However, complex remote sensing characteristics, such as cluttered backgrounds, large-scale variations, small scattered targets and repetitive textures, lead to unstable visual grounding and further spatial grounding drift, resulting in inaccurate segmentation results. Existing approaches typically perform implicit visual–linguistic fusion across encoding and decoding stages, entangling spatial grounding with mask refinement. This tightly coupled formulation lacks explicit structural constraints and is prone to cross-modal ambiguity, especially in complex remote sensing layouts. To address these limitations, we propose a Structurally consistent and Grounding-aware Stagewise Reasoning Framework (SGSRF) that follows a grounding-first, segmentation-second paradigm. The framework decomposes inference into three cascaded stages with progressively imposed structural constraints. First, Cross-modal Consistency Refinement (CCR) lays the foundation for stable spatial grounding by enhancing visual–textual structural alignment via CLIP-based features and Structural Consistency Regularization (SCR), producing well-aligned multimodal representations and reliable grounding cues. Second, Grounding-aware Prompt (GPG) Generation bridges grounding and segmentation by converting aligned representations into complementary sparse and dense prompts, which serve as explicit grounding guidance for the segmentation model. Third, Grounding Modulated Segmentation (GMS) leverages the Segment Anything Model (SAM) to generate fine-grained mask prediction under the joint guidance of prompts and grounding cues, improving spatial grounding stability and robustness to background interference and scale variation. Extensive experiments on three remote sensing benchmarks, namely RefSegRS, RRSIS-D, and RISBench, demonstrate that SGSRF achieves state-of-the-art performance. The proposed stagewise paradigm integrates structural alignment, explicit grounding, and prompt-driven segmentation into a unified framework, providing a practical and robust solution for RRSIS in real-world Earth observation applications. Full article

Rising urban temperatures have become a critical constraint to urban ecosystem resilience and livability due to rapid urbanization. This study proposes a novel intra-city zoning scheme, named component morphological blocks (CMBs), which classifies built-up areas into six types characterized by multidimensional urban canopy morphologies. The XGBoost-SHAP model, optimized via Bayesian tuning, was employed to examine the relative contributions of 16 potential driving variables to block-scale land surface temperature (LST). The results show that: (1) LST gradually increases with increasing building density in the warm seasons. The average building height (BH) exhibits a positive correlation with shaded area, thereby reducing LST on the block scale; (2) hotspots are mainly concentrated in function-oriented blocks with hotspot distribution indices of 1.85, 1.96, 1.24, and 1.14, respectively. Coldspots are largely observed in blue–green space in the warm seasons; (3) BH dominates the LST across seasons, while the building-related factors make a prominent impact on LST in warm seasons. The contribution of vegetation canopy density is followed by BH during autumn and winter (12.2%, 10.9%); (4) a distinct transition occurs between summer normalized difference built-up index (NDBI) and fractional vegetation cover around an NDBI of 0.1. In winter, the interaction between 2D and 3D vegetation factors indicates a shift in their relative contributions from negative to positive as they increase. This study demonstrates that CMBs serve as an effective choice for characterizing LST patterns at the block scale, providing insights for sustainable urban development aimed at mitigating the urban heat island effect. Full article

The rapid deployment of Large Language Models (LLMs) in multilingual, production-scale systems has made inference-time energy consumption a critical yet systematically under-evaluated dimension of model quality. While accuracy-centric benchmarks dominate current evaluation practice, they fail to capture the energy cost of reasoning, particularly across languages and task complexities where consumption profiles diverge substantially. In this work, we present a comprehensive energy–performance evaluation of five instruction-tuned LLMs, spanning Transformer, Grouped-Query Attention, and State Space Model architectures, across thirteen typologically diverse languages and multiple task difficulty levels under controlled GPU-level energy measurement on NVIDIA H200 hardware. Our analysis encompasses 65 model–language configurations totaling over 5100 individual inference runs, supported by rigorous non-parametric statistical testing (Friedman tests, pairwise Wilcoxon signed-rank with Holm correction, and paired Cohen’s d effect sizes). We report four principal findings. First, energy consumption varies up to threefold across models under identical workloads (${\chi }^2=49.42$, $p=4.78\times {10}^{-10}$, Friedman test), stratifying into three distinct energy regimes driven by architecture and generation dynamics rather than parameter count. Second, energy expenditure and reasoning performance are only weakly coupled, as confirmed by Spearman rank correlation analysis ($r_s=0.109$, $p=0.386$). Third, task category and difficulty level introduce substantial and model-dependent variation in both energy demand and performance, with cross-lingual performance variance amplifying at higher difficulty levels. Fourth, language choice acts as a measurable deployment parameter as follows: Romance languages on average achieve lower energy consumption than English across multiple models, while model efficiency rankings shift across languages, yielding language-dependent Pareto-optimal frontiers. We formalize these trade-offs through multi-objective Pareto analysis and introduce a composite AI Energy Score metric that captures reasoning quality per unit of energy. Of the 65 evaluated configurations, only four are Pareto-optimal, three Mistral-7B configurations at the low-energy extreme and one Phi-4-mini-instruct configuration at the high-performance end, while three of the five models are entirely dominated across all language configurations. These findings provide actionable guidelines for energy-aware model selection in multilingual deployments and support the integration of AI Energy Scores as a standard complementary criterion in LLM evaluation frameworks. Full article

Task scheduling for vehicle–crane coordination is crucial for the operational efficiency of electrified automated container terminals (ACTs). However, under fully shared dispatching, existing studies rarely capture how charging-induced capacity fluctuations disrupt bidirectional service–arrival matching and propagate service-window shifts. To address this gap, this study proposes a comprehensive spatio-temporal optimization approach. Firstly, a bi-objective model is established to minimize service–arrival mismatch and vehicle energy consumption under state-of-charge (SOC) and charger-capacity constraints, explicitly quantifying vehicle–crane alignment at both handling interfaces. Secondly, an enhanced multi-objective algorithm (ST-NSGA-II) is developed, integrating a feasibility-preserving recursive decoding mechanism and a spatio-temporal variable neighborhood search (VNS) procedure. Finally, numerical experiments demonstrate that ST-NSGA-II significantly reduces mismatch and energy consumption compared to standard NSGA-II in large-scale scenarios. It also outperforms MOEA/D in Pareto-set quality, yielding a higher hypervolume (1.301 vs. 0.960) and a lower Spacing value (0.102 vs. 0.185). The results demonstrate that the proposed spatio-temporal optimization approach can effectively reduce handover mismatch compared to conventional scheduling modes, thereby achieving seamless task scheduling for vehicle–crane coordination. Full article