Search Results (229)

3D reconstruction and classification of aircraft are two active research areas in optical remote sensing image processing which are of great significance for applications such as airport monitoring and intelligence analysis. The traditional approaches usually focus only on one of these two tasks, and all these methods suffer from inherent limitations. In the field of 3D reconstruction, most current methods require multiple-view images as input, which is rarely feasible in remote sensing. However, single-view 3D reconstruction is an inherently ill-posed problem. Existing methods, including voxel generation and mesh template deformation, still suffer from limited accuracy and poor shape fidelity. In the field of image classification, the existing methods are mainly based on deep learning. These methods require a large amount of labeled data, and they may also be misled by the color and texture features of the target in the dataset. In this paper, we propose a unified framework for simultaneous 3D reconstruction and classification, specifically tailored for aircraft targets in optical remote sensing imagery. The key innovations are threefold: First, we introduce the Signed Distance Field (SDF) implicit representation to build a prior-guided 3D reconstruction framework pre-trained on 3D model datasets. Second, to achieve the reconstruction process with a single image as input, we design a new joint optimization pipeline. We propose a novel dual-kernel differentiable rendering method, which is fused behind the SDF generation network for iterative optimization of the implicit code and pose parameters. Third, a gated feature fusion module is developed to combine the optimal latent vector from reconstruction with the classification backbone. This integration enables the joint output of 3D meshes and category labels within a unified loop. The resulting optimal latent code plays a dual role as a generative seed for high-fidelity 3D reconstruction and as a low-dimensional feature representation for target classification. Quantitative evaluations validate the superiority of our joint framework. Compared with the strong mesh-based competitor AtlasNet, the proposed method yields a 12.2% boost in mean F-score. In object classification, leveraging the 3D implicit geometric features boosts the performance to a peak accuracy of 97.88%, outperforming advanced remote sensing backbones such as RSMamba and EAM by 2.03% and 2.54%. Additionally, ablation studies confirm the indispensability of our key designs, revealing that our dual-task feature fusion strategy brings an absolute gain of 1.18% in classification accuracy, while omitting the clustering prior stages and the dual-kernel rendering method leads to a 30.4% and 10.1% degradation in Chamfer distance. Full article

Assessment of left atrial remodeling in atrial fibrillation (AF) has traditionally relied on anteroposterior diameter, left atrial volume (LAV), and indexed left atrial volume (LAVI). Although these measures remain clinically useful, they reduce a complex, asymmetric, and anatomically constrained chamber to scalar descriptors and therefore cannot fully capture the spatial substrate that underlies AF persistence, thromboembolic risk, or arrhythmia recurrence after catheter ablation. Three-dimensional left atrial reconstruction provides a more refined framework by preserving chamber shape, regional deformation, pulmonary vein (PV) orientation, left atrial appendage (LAA) geometry, posterior wall and roof configuration, left lateral ridge anatomy, wall-thickness heterogeneity, and computational surface features. In this review, we examine how three-dimensional left atrial geometry can extend conventional remodeling assessment from measurement of atrial size toward imaging-based substrate characterization. We discuss the relative strengths and limitations of computed tomography (CT), cardiovascular magnetic resonance (CMR), three-dimensional echocardiography, and electroanatomic mapping (EAM), and summarize key geometry-derived metrics, including LAV, LAVI, left atrial sphericity, asymmetry index, atrial eccentricity index, PV anatomy, LAA morphology, posterior wall geometry, wall thickness, radiomics, and artificial intelligence (AI)-derived shape descriptors. We further synthesize evidence linking geometric remodeling with atrial cardiomyopathy, mechanical dysfunction, fibrosis, low-voltage substrate, and catheter ablation outcomes. The clinical relevance of three-dimensional left atrial geometry may be further redefined by pulsed field ablation (PFA), whose non-thermal lesion biology and tissue selectivity may modify predictors of recurrence established in radiofrequency and cryoballoon cohorts. Finally, we outline the need for standardized segmentation, harmonized metric definitions, prospective multicenter validation, and integration with AI, digital twin modeling, biomarkers, EAM data, and wearable-derived AF burden. Three-dimensional left atrial geometry is not yet a standalone determinant of ablation strategy, but it may become a central component of individualized atrial phenotyping and rhythm-control decision-making. Full article

This study examines the importance of integrating collaborative ecosystem theory into the broadband transdisciplinary approach of Engineering Asset Management (EAM). The authors seek to contribute to moving beyond traditional organization-centric EAM approaches toward systemic, multi-actor value co-creation models. This systematic review aims to investigate existing work on value co-creation across different areas and assess whether these works can be transposed to EAM. To achieve this, a structured, systematic literature review was conducted in accordance with PRISMA guidelines, using Scopus and Web of Science as information sources. The search combined the keywords “collaborative,” “ecosystem,” “value co-creation,” and “model,” resulting in 72 peer-reviewed articles. Bibliometric analysis was performed using EndNote (version 2025.2), Excel (version 16.110.2), and VOSviewer (version JAVA 1.0), complemented by thematic analysis structured around the three research questions. The results reveal a significant increase in publications after 2021, the predominance of ecosystem and value co-creation concepts, and the existence of fragmented conceptual, governance, and analytical models. The findings confirm a persistent gap that needs to be addressed to properly integrate collaborative ecosystems and enable value co-creation in EAM, providing a structured foundation for developing an integrated ecosystem-based asset management framework in this space. By conducting a structured, systematic literature review and synthesizing conceptual, governance, and analytical models, this research consolidates fragmented knowledge and proposes an integrative perspective that positions value co-creation, multi stakeholder governance, and digital enablement as central mechanisms for enhancing ecosystem performance and sustainability in asset-intensive environments. Full article

Currently, a major challenge for European economies is the volatility of electricity prices, which affects costs borne by households and firms, as well as inflation, economic competitiveness, and energy security. Although the literature has analysed various determinants of electricity prices, there is still limited evidence on the comparative short- and long-term effects of fiscal factors, the natural gas market, and the transition to renewable energy within the Member States of the European Union. This paper analyses the relationship between household electricity prices and a set of economic, climate, and fiscal determinants in EU countries over the period 2017–2025, using panel data econometric methods. The methodology includes pooled OLS models, fixed and random effects estimators, unit root tests, cross-sectional dependence (Pesaran CD) tests, cointegration analysis, and a Panel ARDL-PMG framework, complemented by robustness checks using FMOLS and DOLS-type estimators. The results indicate the existence of a stable long-run equilibrium relationship between the analysed variables, as well as significant cross-sectional dependence among countries, reflecting common shocks and interconnected dynamics in EU energy markets. Fixed effects models are used as the baseline specification, while PMG-ARDL and other dynamic estimators are employed for robustness analysis. The results are consistent across different econometric specifications. The conclusions highlight the dominant role of Household Gas Prices as the main determinant of electricity prices, while energy productivity shows a positive association with electricity price levels. Climate variables exhibit weak and unstable effects, and environmental taxes do not show statistically significant impacts within the sample period. Overall, the findings underline the importance of energy market dynamics, structural factors, and the ongoing energy transition in shaping electricity price developments in the European Union. Full article

Reservoir computing (RC) is a simple and highly efficient artificial neural network. For such a network, only the output connection weights need training, effectively reducing computational complexity. Optoelectronic time-delayed RC is typically based on an optoelectronic oscillator (OEO) with simultaneous broadband processing capabilities for both optical and electrical signals, while being readily implementable based on existing technologies. In this work, a new OEO-based RC (OEO-RC) using an electroabsorption modulated laser (EML) is designed, and the electroabsorption modulator (EAM) integrated in the EML serves as a nonlinear node. This scheme simplifies the architecture of an OEO-RC. And it is validated by using two typical tasks of the NARMA 10 time series prediction and the handwritten digit image recognition. Numerical results demonstrate that with optimized hyperparameters, this EML-based OEO-RC exhibits a comparable performance compared with some existing photonic time-delayed RCs. Full article

Waste management presents a major environmental and public health challenge, creating an urgent need for strategies that convert discarded materials into higher-value products. Waste-derived nanoparticles (WDNPs) have gained increasing attention because they integrate waste valorization with the production of functional nanomaterials for environmental, biomedical, agricultural, packaging, sensing, catalytic and energy-related applications. This review critically evaluates WDNP synthesis from five major waste streams, including agricultural residues, animal-derived waste, plastic waste, electronic waste and industrial by-products. Across these categories, precursor composition strongly influences nanoparticle size, morphology, surface chemistry, stability and functional performance, making feedstock selection and processing conditions central to reproducible production. Evidence from recent studies indicates that WDNPs have broad functional potential across environmental remediation, biomedical delivery, antimicrobial systems, sustainable packaging, agriculture, energy storage and catalysis. However, translation beyond laboratory-scale studies remains limited by feedstock variability, limited reproducibility, complex purification requirements, potential toxicity, insufficient standardization and limited pilot-scale validation. By comparing synthesis approaches, application outcomes and translational barriers across waste categories, this review provides a critical overview of the opportunities and limitations of WDNPs and identifies the key requirements for their responsible development within a circular-economy framework. Full article

Background: The presence of left atrial fibrosis is a marker of advanced remodeling and is associated with a worse outcome after pulmonary vein isolation (PVI). Conventional fluoroscopy-only cryoballoon ablation (CBA) lacks this prognostic information. The addition of electroanatomical mapping (EAM) using the inner lumen spiral catheter allows accurate voltage assessment of the left atrial posterior wall. However, the value of the finding of posterior wall low-voltage zones (pwLVZs) is unknown. Purpose: To study the value of left atrial voltage maps during CBA by comparing clinical and procedural characteristics and clinical outcome between patients with and without pwLVZs. Methods: A cohort of 250 consecutive patients who underwent index CBA for atrial fibrillation was analyzed. All patients underwent pre- and post-procedural EAM using the Achieve^TM catheter and EnSite^TM mapping system. The presence of LVZs was evaluated at the postprocedural voltage map of the posterior wall. Clinical success was defined as freedom from documented AF or atrial tachycardia (AT) >30 s after 1 year. Results: PwLVZs were found in 41/250 (16.4%) of patients. Patients with pwLVZs were older (69.3 ± 8.5 vs. 64.2 ± 10.4; p = 0.003), more frequently female (63.4% vs. 32.5%; p < 0.001) and had higher CHA₂DS₂-VASc scores (3.0 ± 1.6 vs. 2.0 ± 1.5; p < 0.001). The incidence of obesity (31.7% vs. 25.8%; p = 0.048), structural heart disease (35.5% vs. 17.4%; p = 0.021) and persistent AF (68.3% vs. 43.8%; p = 0.004) was higher in the pwLVZs group. Kaplan–Meier analysis of clinical outcome showed a higher recurrence rate in the pwLVZs group. The finding of pwLVZs was a predictor of atrial arrhythmia recurrence during follow-up (HR 2.583; 95%CI: 1.334–5.002; p = 0.005). Conclusions: In CBA facilitated by integrated EAM, pwLVZ was associated with older age, female sex, higher CHADS-VASc scores, obesity, structural heart disease and persistent AF. The finding of pwLVZs is predictive of a worse clinical outcome. Full article

Autoimmune myocarditis frequently progresses to inflammatory cardiomyopathy through dysregulated immune–stromal interactions. This study employs single-nuclei RNA-sequencing (snRNA-seq) to profile 46,233 cardiac nuclei from the experimental autoimmune myocarditis (EAM) mouse model at four timepoints: day 0 (healthy), day 14 (inflammation), day 21 (acute inflammation), and day 40 (late cardiac remodelling). Single-nuclei RNA profiling identified 18 transcriptionally distinct cell populations. Global cell–cell communication analysis revealed a dramatic peak of intercellular signalling at day 14 (5907 interactions), with fibroblast subpopulations and macrophages as dominant hubs, followed by partial resolution at day 21 (2264 interactions) and renewed remodelling at day 40 (4862 interactions). Subclustering of the macrophage compartment identified five subpopulations: Mac-TLF, Mac-MHCII, Mac-rMHCII, Mac-ResL, and Classical Monocytes. Tissue-resident macrophages (Mac-TLF, CCR2-) dominated at healthy state (~55%) but were rapidly depleted at day 14, coinciding with a dramatic influx of recruited CCR2⁺ macrophages (Mac-rMHCII), which expanded to over 70% of the compartment and maintained dominance through day 40. At inflammation (day 14), the expanded Mac-rMHCII subpopulation displayed a strongly pro-inflammatory signature (Il1b, Stat2, Parp14, Apoe), and the overall macrophage compartment was enriched for cytokine response, Fc-gamma receptor, and Notch signalling pathways, while downregulating homeostatic and mitochondrial metabolic programmes, potentially contributing to impaired efferocytosis and cardiomyocyte dysfunction. Macrophage-centred communication networks expanded markedly at day 14 (1047 interactions), with resting fibroblasts (FB-R) as the primary signalling partner, driving pro-inflammatory stromal activation marked by upregulation of Ccl2, Ccl7, and Csf2. Intra-macrophage subcluster communication also intensified at this timepoint (447 interactions). These findings delineate the temporal and functional heterogeneity of cardiac macrophages during EAM progression and identify key immune–stromal interactions driving pathological cardiac remodelling. The coexistence of pro-inflammatory and transitional reparative macrophage subsets highlights the limitations of broad immunosuppression and supports precision strategies targeting CCR2-mediated recruitment, the SPP1 signalling axis, and macrophage–fibroblast crosstalk as therapeutic avenues in myocarditis and its progression. Full article

Polar motion (PM), a key component of Earth orientation parameters (EOPs), is essential for high-precision satellite orbit determination and deep-space navigation. However, delays in data acquisition and processing limit its availability for real-time applications, necessitating the development of prediction models based on historical observations. Common approaches include least squares extrapolation (LS), autoregressive (AR) models, and their combination (LS + AR), often enhanced by effective angular momentum (EAM) from Earth’s fluid components. This study examines an EAM + LS + AR method for PM prediction, systematically evaluating how different EAM forecast horizons (1–10 days) affect 90-day prediction accuracy for both PM X and Y components. A segmented optimization strategy is proposed and validated against International Earth Rotation and Reference Systems Service (IERS) official predictions using the IERS EOP 14 C04 product. Key findings include: (a) Adjusting the EAM horizon substantially reduces prediction errors. Segmented prediction improves PM X accuracy by 20–30% (1–60 days) and 10–20% (61–90 days) relative to IERS rapid products, while PM Y short-term accuracy improves by 20–40% (1–15 days). (b) The influence of EAM horizon on long-term PM Y prediction gradually weakens, with errors converging to approximately 8 mas by day 90. (c) For 1–10-day forecasts, optimal horizons follow a systematic pattern: day m predictions achieve the highest accuracy using an (m−1)-day EAM horizon, while a 10-day horizon is optimal for long-term forecasts. (d) The proposed method shows clear advantages over IERS forecasts, with 83.5% of PM X predictions (1–90 days) and 50.78% of PM Y predictions (1–15 days), outperforming IERS daily products during the 2024 test period. Full article

Due to the complex pathophysiology and serious outcomes of autoimmune myocarditis, we sought to determine whether ethanolic lemon balm extract (LBE) could attenuate disease progression and development of dilative cardiomyopathy (DCM). EAM was induced in Dark Agouti rats by immunization with porcine myosin. Fifty animals were allocated to five groups: healthy controls, untreated EAM, and EAM treated with LBE (50, 100, or 200 mg/kg) for six weeks. Hemodynamic parameters were monitored, and echocardiography assessed cardiac structure and function. Inflammatory, oxidative, fibrotic, and apoptotic markers were analyzed. Immunological profiling revealed that LBE significantly decreased proinflammatory cytokines (IL-1, IL-6, TNF-α, IL-4, IL-17) while restoring anti-inflammatory IL-10 levels (p < 0.05). Antioxidant activity was confirmed by reduced levels of O₂⁻, H₂O₂, and TBARS, accompanied by significant increases in SOD, CAT, and GSH activity (p < 0.05), and upregulation of SOD1 and SOD2 gene expression. Additionally, LBE (200 mg/kg) markedly reversed fibrotic remodeling through suppression of TGF-β expression and collagen deposition, as shown by Sirius Red staining, and mitigated apoptosis by modulating Bax/Bcl-2 balance and reducing TUNEL-positive cells. Collectively, these findings suggest that LBE exerts strong cardioprotective effects in EAM by regulating inflammatory, oxidative, fibrotic, and apoptotic pathways, thereby preventing myocarditis progression toward DCM. Full article

Cross-chain analytics increasingly demand continuous joins across ledgers with asynchronous state evolution. Existing solutions, however, typically assume static snapshots or neglect temporal alignment, yielding semantically inconsistent results when epochs drift. This paper introduces VeriFed, a system for temporally consistent continuous cross-chain joins. We formalize the problem of snapshot-aligned continuous joins, design a Unified Adapter Layer (UAL) to align finalized snapshots across heterogeneous protocols, and develop incremental verification that composes per-chain proofs into a global summary via the Epoch Attestation Mesh (EAM) and the Delta-Linked Proof Forest (DLPF). To sustain high-throughput execution, VeriFed further adopts an incremental multi-objective optimizer that balances latency and monetary cost. Experiments on Ethereum transaction data with a simulated wide-area network (WAN) demonstrate that VeriFed achieves sub-second per-epoch latency (approx. 38 ms) and reduces verification overhead by orders of magnitude compared to state-of-the-art baselines, while effectively detecting tampering with zero false positives. These results confirm consistent efficiency and verifiability under continuous updates. Full article

Ice adhesion on exposed structures remains a major operational challenge, motivating the search for passive, material-based anti-icing strategies. Molecular dynamics offers a controlled way to investigate ice–surface interactions beyond the limits of experimental setups. In this work, we develop a simulation framework to model the impact of solid hexagonal ice droplets on metallic substrates. Ice impacts are simulated across a range of velocities (10–120 m/s), temperatures (120–250 K), and face-centred cubic surface materials (gold, copper, silver, aluminum, and nickel). Using LAMMPS, mW water force-field, EAM/Alloy metal potentials, and Lennard-Jones water–surface interactions, we quantify phase evolution through angular order parameter and quasi-liquid layer measurements, complemented by the CHILL+ algorithm in OVITO. By isolating all external factors, we show that melting increases with velocity and temperature and correlates with substrate properties: metals with high thermal diffusivity and low Young’s modulus tend to decrease post-collision ice melting. The ratio of the former to the latter, a derived index of merit Υ, significantly correlates with melting percentage and identifies silver as the most effective anti-ice material examined. Statistical analyses strongly suggest that these surface properties influence interfacial melting, supporting the use of this modelling framework for screening and designing anti-icing materials. Full article

Against the backdrop of global coral reef degradation, benthic resource structure is shifting from coral dominance to turf algae and detritus-dominated epilithic algal matrix (EAM). As a typical detritivorous reef fish, Ctenochaetus striatus (Quoy & Gaimard, 1825) plays an important ecological role in regulating the functioning of degraded coral reef ecosystems. Using stable isotope analysis (δ¹³C and δ¹⁵N), this study systematically compared the trophic niche characteristics of different size classes of C. striatus across four reef habitats in the Xisha Islands, South China Sea, representing a gradient of disturbance (Qilianyu Island > Lingyang Reef > North Reef > Langhua Reef), in order to elucidate habitat-specific ontogenetic shifts and their adaptive features. The results showed that C. striatus from Qilianyu Island and Lingyang Reef exhibited overall higher δ¹⁵N values, suggesting an overall pattern consistent with stronger nitrogen enrichment at the more disturbed reefs, whereas individuals from Langhua Reef had significantly lower δ¹³C values, indicating a stronger reliance on offshore-derived carbon pathways. Across size classes, the trophic niche area (SEAc) and intraspecific trophic heterogeneity, measured as mean nearest neighbor distance and standard deviation of nearest neighbor distance, of populations from Qilianyu Island, Lingyang Reef, and North Reef generally decreased with increasing body size, revealing a pattern of trophic convergence toward core resources. In contrast, the Langhua Reef population exhibited a distinct expansion–contraction pattern, suggesting flexible resource use across developmental stages under conditions of low human disturbance and high resource heterogeneity. Although smaller size classes generally showed high probabilities of niche overlap among reefs, overlap declined markedly in the largest size class, with most values falling below 50%, indicating that resource assimilation strategies increasingly reflected reef-specific resource backgrounds. These findings demonstrate that ontogenetic trophic niche shifts in C. striatus are not fixed, but are highly dependent on local resource context and habitat conditions. In degraded reefs with simplified resource structure, individuals tend to converge on core resource spectra to maintain survival, whereas in healthier reefs with greater habitat heterogeneity, they tend to show greater variation in major food sources and resource use. This study provides a theoretical basis for coral reef ecological restoration. Full article

Background/Objectives: Electroanatomic mapping (EAM) provides high-resolution spatial and electrogram information, but the prognostic utility of quantitative EAM features has not been systematically evaluated with contemporary artificial intelligence (AI) methods. We investigated whether an AI analysis of quantitative EAM exports from the CARTO system enhances the prediction of major arrhythmic events (MAEs). Methods: In this retrospective, multicenter cohort study, 248 consecutive patients undergoing left ventricular EAM at four tertiary electrophysiology centers were analyzed. Numerical EAM descriptors (spatial coordinates, unipolar/bipolar voltages, local activation time, impedance) were transformed into derived metrics, including local activation heterogeneity (GR), late-potential extent (LAT), bipolar–unipolar discrepancy (VLT), and low-amplitude scar extent (Scar Areas), and were spatially normalized via spherical projection. Clinical, anamnestic, and imaging variables were integrated. Machine learning and deep learning models were trained with an 80:20 train/test split and evaluated using three-fold cross-validation. Performance metrics included area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision. Results: Models incorporating both clinical and AI-processed EAM features achieved high discriminatory performance (test AUC up to 0.92; accuracy up to 0.896). Specificity was consistently high (≈0.97–0.998), whereas sensitivity remained modest (≈0.39–0.58). Among the EAM-derived features, GR was the most consistently informative predictor across algorithms and analyses; VLT, LAT, and Scar Areas also contributed substantially. Regionally, basal sub-mitral, subaortic, and posterolateral basal-to-mid zones exhibited the strongest associations with MAEs. Conclusions: AI-driven quantitative analysis of left ventricular EAM exports augments risk stratification for MAEs beyond conventional clinical and binary EAM descriptors. Reflecting local conduction heterogeneity, GR emerged as the dominant EAM predictor. Prospective validation in larger, disease-specific cohorts and real-time integration within EAM platforms are warranted. Full article

Background: Idiopathic inflammatory myopathies (IIMs), characterized by muscle weakness and chronic inflammation, currently lack highly effective therapies. This study investigated the therapeutic potential and underlying mechanism of (5R)-5-hydroxytriptolide (LLDT-8), a triptolide derivative with reduced toxicity, using an experimental autoimmune myositis (EAM) mouse model and in vitro assays. Methods: Forty female BALB/c mice were randomly assigned to five groups: normal, vehicle, methylprednisolone (MP), LLDT-8 (0.0625 mg/kg), and LLDT-8 (0.125 mg/kg). EAM mice were treated with LLDT-8 (0.0625 or 0.125 mg/kg) or methylprednisolone as a positive control. Cellular experiments and molecular docking were performed to investigate potential mechanisms of LLDT-8. Results: LLDT-8 significantly attenuated clinicopathological features, including muscle weakness and pain sensitivity, while reducing serum levels of aspartate aminotransferase and lactate dehydrogenase. Histological analysis revealed that LLDT-8 reduced inflammatory cell infiltration and the presence of CD4⁺ and CD8⁺ T cells in muscle tissues. Mechanistically, LLDT-8 inhibited the expression of nucleotide-binding oligomerization domain receptor caspase recruitment domain 5 (NLRC5), a key transcriptional regulator of major histocompatibility complex-I (MHC-I). This suppression extended to downstream antigen presentation-related molecules, including the transporter associated with antigen processing and proteasome 20S subunit beta. Molecular docking further confirmed the high binding affinity of LLDT-8 to both NLRC5 and MHC-I. Conclusions: LLDT-8 alleviates inflammatory muscle injury by targeting the NLRC5/MHC-I signaling axis, suggesting it may be a promising therapeutic candidate for IIMs. Full article