Search Results (35,737)

Traditional leaf area index (LAI) measurement methods are destructive, time-consuming, and labor-intensive. In this study, 282 four-year-old central-leader apple trees were used as research subjects. Canopy reflectance spectra in the range of 4000−10,000 cm⁻¹ were collected, and the corresponding true LAI values were measured destructively by harvesting all leaves from a representative branch of each tree using a leaf area meter. The dataset was randomly divided into training (70%) and testing (30%) sets. Eight spectral pretreatment methods were compared. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to extract characteristic wavelengths. Subsequently, both a BP neural network and a Support Vector Machine (SVM) model for LAI prediction were constructed. The optimal model was selected based on evaluation metrics including the coefficient of determination (R²), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE). The combined preprocessing of MSC and SD yielded the optimal results, screening out 26 characteristic wavelengths. The SVM linear kernel model (c = 5, g = 0.3) constructed based on MSC + SD preprocessing performed best, achieving a validation set R² of 0.90, MAE of 0.2117, MBE of −0.1214, and MAPE of 16.09%. The performance on the training set and validation set was comparable, with no overfitting observed. The MSC + SD preprocessing combined with CARS feature screening and SVM linear kernel modeling enables rapid, non-destructive estimation of apple canopy LAI, providing an effective technical tool for precision orchard management. Full article

Replicating the authentic masticatory properties of conventional animal meat remains a primary technical bottleneck for sustainable plant-based analogues. To address critical textural deficiencies like structural fragmentation, this study systematically optimized plant-based patty formulations. The independent and interactive effects of textured fibrous soy protein (TFSP), water, and konjac glucomannan (KGM) were quantified using single-factor experiments and Response Surface Methodology (RSM). Single-factor experiments revealed that springiness peaked at 60 g TFSP, 15 g water, and 10 g KGM, respectively, with excessive additions of each component resulting in structural network disruption. Designating springiness as the core metric, a reliable quadratic regression model identified the optimal matrix: 63.36 g TFSP, 14.39 g water, and 8.57 g KGM. Empirical validation achieved a maximum springiness of 1.56 mm and hardness of 5.51 N, with a negligible relative error (1.27%) from theoretical predictions. Mechanistically, KGM functioned as an active polymeric filler, interacting synergistically with hydrated protein fibers via hydrogen bonding and hydrophobic associations to reinforce the structural network. Comparative Texture Profile Analysis demonstrated that the optimized PBP exhibited a tender masticatory profile with hardness and springiness approximating conventional beef patties, while presenting lower chewiness and higher adhesiveness attributable to the water-binding capacity of KGM. Ultimately, this research provides mathematically validated engineering parameters and theoretical insights into protein–polysaccharide phase behaviors to facilitate the industrial manufacturing of premium plant-based meats. Full article

Earthquake-induced landslide reactivation poses a sustained hazard for years following major seismic events, yet operational prediction remains constrained by heterogeneous multi-modal data, sparse supervision, and the absence of uncertainty-aware frameworks. This paper presents PIMSEL, a physically guided multi-modal semi-supervised framework for post-seismic landslide reactivation risk assessment. PIMSEL integrates satellite-derived morphological features, precipitation time series, and seismic hazard attributes through four components: entropy-regularized optimal transport for cross-modal semantic alignment without paired supervision; causally constrained hierarchical fusion enforcing domain-consistent modal weighting; scenario-based prototype mutation for semi-supervised learning from sparse expert annotations; and prototype-anchored variational graph clustering that simultaneously stratifies landslides into HIGH, MEDIUM, and LOW risk tiers and produces decomposed aleatoric and epistemic uncertainty estimates for operational triage. The HIGH risk tier operationally corresponds to predicted reactivation, validated against 598 documented reactivation events across 7482 co-seismic landslides from three Sichuan Province earthquake sequences: the 2013 Lushan (Mw 7.0), 2017 Jiuzhaigou (Mw 7.0), and 2022 Luding (Mw 6.8) events. PIMSEL achieves 82.5% reactivation recall and 66.4% precision, outperforming twelve baselines across clustering quality, classification, and uncertainty calibration metrics. Ablation studies confirm that optimal transport alignment contributes the largest individual performance gain. Current limitations include quarterly assessment frequency and dependence on optical imagery under cloud cover, which future integration of real-time meteorological triggers and SAR data should address. Full article

The inherent non-uniformity of Infrared Focal Plane Arrays (IRFPA) inevitably results in stripe noise, which severely degrades image quality and hinders downstream applications. Existing deep learning methods often struggle to strike a balance between effective denoising and the preservation of fine thermal textures. To address this issue, we propose a Bottleneck-free Channel Dual-path Aggregation Network (BCDA-Net) based on a “Perception-Reconstruction” design principle. In the perception stage, the network jointly employs the Dual-Path Channel Down-sampling (DCD) module and the Context-Guided Stripe Attention Block (CGSAB). The DCD module utilizes a channel split strategy to simultaneously extract semantic features and preserve high-frequency textures, while the CGSAB performs global context modeling on these features to precisely perceive and locate global stripe noise patterns. In the reconstruction stage, we integrate the Cascaded Dense Feature Aggregation (CDFA) module with a Bottleneck-Free Aggregation Strategy (BFAS). The CDFA utilizes the perceived information to densely aggregate features and progressively reconstruct clean image details, whereas the BFAS structurally blocks the propagation of low-resolution noise during decoding, effectively mitigating aliasing artifacts induced by deep feature upsampling. Together, these components form a complete closed loop from accurate noise perception to high-fidelity reconstruction. Extensive experiments on public and real-world datasets demonstrate that BCDA-Net maximally preserves image details while removing non-uniform stripe noise. Both objective metrics and subjective visual quality outperform existing state-of-the-art methods. Full article

The detection and monitoring of asbestos–cement roofing remain a critical public health and environmental challenge, especially in urban and suburban areas where asbestos-containing materials are still widespread due to their extensive use in the 20th century. Although hyperspectral and high-resolution multispectral remote sensing have proven effective for mapping asbestos–cement roofs, many existing approaches rely on proprietary software, limiting transparency, reproducibility, and large-scale adoption. This study presents a fully reproducible, cost-free Python-based workflow for the detection and temporal monitoring of asbestos–cement roofing using high-resolution multispectral WorldView-3 imagery. The workflow integrates atmospheric correction (using the Py6S radiative transfer model), spatial preprocessing, supervised pixel-based classification, postprocessing, and building-level aggregation within an open framework. A Maximum Likelihood Classifier is applied to VNIR and SWIR data using empirically defined roof typologies to enhance class separability. Pixel-level results are aggregated to the building scale through adaptive thresholding enabling the translation of spectral classifications into meaningful building-level information. Tested over the city of Mantua (Italy), the approach achieved reliable classification performance and enabled multi-temporal comparison to identify changes potentially due to roof remediation. Evaluation metrics (precision, recall, and F1-score) highlight the importance of carefully choosing the building-level threshold. By relying exclusively on open-source tools, the workflow enhances transparency, reproducibility, and scalability for long-term monitoring. Full article

For power-grid applications such as transmission corridor inspection, substation asset inspection, and post-disaster emergency repair, reliable UAV self-localization under GNSS-degraded or GNSS-denied conditions is critical to ensuring operational safety and accurate defect geotagging. Due to substantial discrepancies in viewpoint, scale, and geometric structure between oblique UAV images and nadir satellite images, conventional RGB-based cross-view retrieval methods often suffer from unstable alignment and insufficient geometric modeling, particularly in scenarios with repetitive textures and partial overlap. To address these challenges, we propose a cross-view visual geo-localization model that integrates RGBD multimodal inputs with multi-scale attention enhancement. Specifically, MiDaS is used to estimate relative depth from UAV imagery, which is concatenated with RGB to form a four-channel input, while satellite images are padded with an additional zero channel to maintain dimensional consistency. A shared-weight ViTAdapter is adopted to learn joint semantic–geometric representations, and a lightweight Efficient Multi-scale Attention (EMA) module is adopted on spatial feature maps to strengthen multi-scale spatial consistency. In addition, an IoU-weighted InfoNCE loss is employed to accommodate partial matching during training, thereby improving the robustness of feature alignment. Experiments on the GTA-UAV dataset under the cross-area protocol show stable performance across both retrieval and localization metrics. Specifically, Recall@1, Recall@5, and Recall@10 reach 18.12%, 38.83%, and 49.47%, respectively; AP is 28.01 and SDM@3 is 0.53; meanwhile, the top-1 geodesic distance error Dis@1 is 1052.73 m. These results indicate that explicit geometric priors combined with multi-scale spatial enhancement can effectively improve cross-view feature alignment, leading to enhanced robustness and accuracy for localization in challenging power inspection scenarios. Full article

Peripheral nerve injuries involving critical-sized gaps remain a major clinical challenge. Although autologous nerve grafting is considered the gold standard for peripheral nerve repair, its clinical application is limited by the availability of donor nerve tissue and the risk of donor-site morbidity, including sensory deficits and functional impairment. Therefore, nerve guidance conduits (NGCs) have emerged as a promising alternative when combined with bioactive modulation strategies. In this study, we evaluated bisvinyl sulfonemethyl (BVSM)-crosslinked gelatin conduits integrated with electrical stimulation (ES) at different frequencies (0, 2, 20, and 200 Hz) in a rat sciatic nerve defect model over a 4-week recovery period (n = 10 per group). Structural regeneration was assessed by morphometric analysis, electrophysiology, macrophage infiltration, CGRP immunoreactivity, retrograde Fluorogold tracing, quantitative PCR of growth factors and inflammatory cytokines, and behavioral testing. Among all stimulation paradigms, low-frequency ES at 2 Hz produced the most pronounced regenerative effects. The 2 Hz group demonstrated significantly greater axon number, axonal density, and regenerated nerve area compared with control and high-frequency groups (p < 0.05). Electrophysiological assessments revealed improved nerve conduction velocity, higher MAP amplitudes, and shorter latencies. Enhanced macrophage recruitment and elevated CGRP expression were observed, suggesting coordinated neuroimmune and neurochemical activation. Gene expression analysis indicated upregulation of neurotrophic factors and balanced inflammatory cytokine responses under low-frequency stimulation. In contrast, high-frequency stimulation (200 Hz) failed to enhance overall regeneration and showed reduced axonal metrics, suggesting possible overstimulation-associated suppression. Collectively, these findings demonstrate that BVSM-crosslinked conduits provide a stable and biocompatible regenerative scaffold, and that appropriately tuned low-frequency electrical stimulation (2 Hz) optimally enhances structural, molecular, and functional recovery. The integration of material engineering with bioelectrical modulation represents a promising strategy for next-generation bioelectronic interfaces in peripheral nerve repair. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature–like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature–like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩~r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

The escalating prominence of environmental, social, and governance (ESG) criteria within contemporary corporate practice has generated a proliferation of measurement and reporting frameworks, creating substantial challenges regarding comparability and standardisation. This study aimed to critically analyse and synthesise the scholarly literature on ESG measurement and reporting frameworks from an accounting perspective. A systematic review adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines was conducted, searching Scopus, ScienceDirect, and Web of Science databases for the period 2020–2025, yielding a final corpus of 50 peer-reviewed articles. Findings reveal that Stakeholder Theory and Institutional Theory constitute the predominant conceptual underpinnings, with three framework categories identified: measurement, reporting, and integrated approaches. The analysis evidenced persistent methodological heterogeneity among ESG metrics and considerable variation in achieved comparability levels. Notably, the governance dimension remains underdeveloped relative to environmental and social dimensions, and small and medium-sized enterprises continue to be underrepresented despite their economic significance. This review contributes by providing a classification of ESG frameworks and their theoretical foundations, whilst identifying gaps that delineate avenues for future inquiry. Full article

The increasing complexity of next-generation wireless systems challenges the scalability and generalization capabilities of traditional model-driven physical layer (PHY) design, which relies on analytically derived channel models and optimization frameworks. This paper presents a comprehensive survey and critical review of deep learning (DL) architectures enabling the transition toward AI-native PHY design. A unified optimization perspective is developed in which all PHY tasks—including channel estimation, channel state information (CSI) feedback, massive MIMO processing, signal detection, channel coding, beamforming, resource allocation, and semantic-aware transmission—are formulated under a common empirical risk minimization (ERM) framework. Neural architectures such as autoencoders, convolutional and recurrent networks, transformers, and reinforcement learning models are examined through their underlying optimization formulations, loss functions, training methodologies, and representation learning mechanisms. The review compares model-driven and AI-native approaches in terms of performance metrics, computational complexity, robustness, generalization capability, and practical deployment constraints, including hardware limitations, energy efficiency, and real-time feasibility. The analysis highlights the conditions under which AI-native architectures provide adaptability and performance improvements while identifying trade-offs in complexity, latency, and interpretability. The study concludes by outlining prioritized research directions toward fully adaptive and self-optimizing wireless communication systems. Full article

Iodine is an essential trace element for thyroid hormone synthesis, metabolic homeostasis, and fetal neurodevelopment. During pregnancy, maternal iodine requirements increase substantially, yet global recommendations are primarily based on population-level biomarkers rather than individualized physiological data. In this review, we examine current international guidelines for iodine adequacy in pregnancy, evaluate the limitations of population-based metrics—such as urinary iodine concentration (UIC) and serum thyroglobulin (Tg)—and highlight emerging evidence on physiological adaptations, functional biomarkers, and individualized risk factors. We incorporated data from population surveillance studies, mechanistic investigations of thyroid adaptation, and clinical outcome research identified through a literature search of PubMed/MEDLINE and Scopus (2016–2025). Evidence indicates that the widely adopted WHO range for iodine intake in pregnant women may overestimate the actual needs of gestation. There is a U-shaped relationship between iodine intake and thyroid outcomes, meaning both low and high iodine exposure adversely affect maternal thyroid function and fetal neurodevelopment, highlighting the narrow optimal intake window. Individualized considerations—including autoimmune thyroid disease, supplementation practices, environmental exposures, and coexisting micronutrient deficiencies—further modulate iodine requirements. Functional indices, such as the Thyroid Feedback Quantile-based Index (TFQI), may offer complementary tools for assessing iodine adequacy beyond traditional biomarkers. Full article

Background: Magnesium sulfate (MgSO₄) added to local anesthetics has been investigated as an adjuvant in regional anesthesia, but its role in ultrasound-guided erector spinae plane block (ESPB) remains uncertain. Methods: We conducted a PRISMA 2020-compliant systematic review of randomized controlled trials evaluating MgSO₄ added to the local anesthetic solution in ESPB. In the predefined core comparison (MgSO₄ added to local anesthetic vs. local anesthetic alone in adult postoperative surgery), four trials (225 participants enrolled; 160 contributing to the comparison) informed the qualitative synthesis. Results: Eight randomized controlled trials were included. In the predefined core comparison, 24 h pain intensity was reported heterogeneously and was frequently not extractable as continuous data, precluding pooling. Opioid consumption or rescue analgesia more often favored MgSO₄; however, outcome metrics, analgesic drugs, and assessment windows were not harmonized, and these effects were not consistently accompanied by reductions in pain intensity at 24 h, limiting their interpretation as true analgesic benefit. Safety reporting was frequently incomplete and often lacked structured adverse event tabulation. Risk of bias varied across domains, and GRADE certainty for all core outcomes was very low. Conclusions: Current randomized evidence does not support routine use of MgSO₄ as an adjuvant in ESPB. Future trials using standardized ESPB techniques, harmonized magnesium dosing strategies, and core outcome sets are required to determine whether magnesium provides clinically meaningful incremental analgesic benefit. Full article

Background/Objectives: Privacy preservation in big data environments is an NP-hard optimization task that requires the satisfaction of k-anonymity and l-diversity constraints to ensure data utility. Methods: This study proposes a novel hybrid optimization approach, adaptive hybrid FOX–RUN Intensive Mutation (PA-FRIM), to address the privacy–utility trade-off in anonymization process. The proposed approach integrates FOX-based global exploration with RUN-based local search using a hybrid adaptive control strategy and intensive mutation search to improve solution diversity in highly constrained solution spaces. Results: The experimental study on the Adult and Bank Marketing datasets shows that PA-FRIM exhibits stable convergence behavior compared to competing methods. The results indicate that full privacy is achieved on the Adult dataset with a violation value of 0.00, and information loss is minimized with an NIL measure of 0.5686. From the analytical utility perspective, PA-FRIM ensures data usability, even in the constrained region, achieving classification accuracies of 89.61% on the Bank Marketing dataset and 84.90% on the Adult dataset. Conclusions: By using a multi-metric evaluation strategy, PA-FRIM provides a robust optimization framework that eliminates privacy violations while maintaining high analytical performance. Full article

The Timed Up and Go (TUG) test is a clinical gold standard for assessing elderly mobility, yet its automated deployment in home-monitoring and resource-limited areas is hindered by high hardware costs and expert calibration requirements. This study introduces a Physics–Data Hybrid framework specifically designed for uncalibrated consumer-grade CMOS cameras, enabling a “plug-and-play” solution for early Parkinson’s disease (PD) risk screening. The proposed pipeline integrates learning-based pose perception with a self-evolving physics model to recover absolute metric-scale motion without manual checkerboard calibration. A noise-adaptive fusion strategy is implemented to reconcile 2D pixel dynamics with 3D kinematic consistency, overcoming the inherent scale ambiguity of monocular vision. Crucially, this framework enables the extraction of high-dimensional spatiotemporal parameters—such as stride length coefficient of variation and mean gait velocity—which provide a finer diagnostic resolution for capturing subtle motor fluctuations than conventional timing-only systems. Results from our pilot study with a cohort of 10 subjects demonstrate that these extracted metric features serve as decisive markers for risk staging simulated by dual-task-induced cognitive-motor-interference, achieving 98% screening accuracy and an overall classification accuracy of 87.32%. This framework provides a robust, low-cost tool for ubiquitous telehealth, potentially supporting early PD risk assessment in underserved populations. Full article

Surface and interface engineering has become a decisive factor in determining the performance and reliability of integrated photonic sensors. As photonic device architectures advance and geometric optimization strategies approach their fundamental performance limits, the nanoscale interface region where confined optical modes interact with the surrounding environment progressively becomes the dominant factor governing sensitivity, noise characteristics, and long-term operational stability. This review critically examines recent advances in these strategies applied to integrated photonic sensing platforms, including waveguide, interferometric, and resonant architectures. Emphasis is placed on how functional layers, nanomaterials, and hybrid interfaces modify light–matter interactions, while simultaneously introducing optical loss, spectral distortion, and stability constraints. Beyond summarizing reported sensitivity enhancements, this review analyzes performance benchmarking methodologies and highlights the limitations of conventional metrics such as bulk sensitivity and nominal limit of detection. Normalized figures of merit are discussed as essential tools for isolating genuine interface contributions across diverse platforms. Experimentally documented trade-offs between enhanced surface interaction, optical degradation, and temporal drift are examined in detail, alongside challenges related to reproducibility, wafer-scale variability, and long-term interface stability. By synthesizing insights from photonics, surface chemistry, and materials science, this review outlines key open questions and identifies design principles necessary for translating surface-engineered photonic sensors from laboratory demonstrations to robust and scalable sensing technologies. Full article