Search Results (181)

To address the issues of parameter coupling and high computational demands in existing feed-forward Gaussian splatting methods, we propose Gaussian Patch-level Mixture-of-Experts Splatting (GaPMeS), a lightweight feed-forward 3D Gaussian reconstruction model based on a mixture-of-experts (MoE) multi-task decoupling framework. GaPMeS employs a dual-routing gating mechanism to replace heavy refinement networks, enabling task-adaptive feature selection at the image patch level and alleviating the gradient conflicts commonly observed in shared-backbone architectures. By decoupling Gaussian parameter prediction into four independent sub-tasks and incorporating a hybrid soft–hard expert selection strategy, the model maintains high efficiency with only 14.6 M parameters while achieving competitive performance across multiple datasets—including a Structural Similarity Index (SSIM) of 0.709 on RealEstate10K, a Peak Signal-to-Noise Ratio (PSNR) of 19.57 on DL3DV, and a 26.0% SSIM improvement on real industrial scenes. These results demonstrate the model’s superior efficiency and reconstruction quality, offering a new and effective solution for high-quality sparse-view 3D reconstruction. Full article

For less-developed, carbon-dependent regions, achieving carbon decoupling while pursuing economic catch-up presents a fundamental challenge. This study investigates this persistent dilemma through the case of Jiangxi Province, China, a typical coal-reliant inland region. Utilizing data from 2000 to 2022, we estimate carbon emissions following IPCC guidelines and employ the Generalized Divisia Index Method (GDIM) to decompose emission drivers, effectively overcoming the limitation of factor independence in conventional decomposition analyses. The results identify economic scale (cumulative contribution: 97.81%) and energy consumption (51%) as the primary drivers of emission growth, while carbon intensity of output (−47.38%) emerges as the strongest inhibiting factor. The application of the Tapio decoupling model reveals that weak decoupling is the dominant state, prevailing in 91% of the study period. This persistent pattern underscores only a partial and unstable separation between economic growth and emissions, highlighting the region’s entrenched carbon lock-in. Our findings demonstrate that transcending this weak decoupling dilemma necessitates a strategic shift beyond efficiency gains. We propose that the resolution lies in accelerating structural transitions within the energy system and fostering low-carbon industrial upgrading. This study not only elucidates the dynamics of the carbon decoupling challenge in catch-up regions but also offers actionable and context-specific pathways, providing a valuable reference for analogous regions, particularly in developing and transition economies. Full article

As shale gas development extends into deeper formations, the unclear brittle-ductile transition (BDT) mechanism and low fracturing efficiency have emerged as critical bottlenecks, posing challenges to the sustainable and economical utilization of this clean energy resource. This study, focusing on the Liangshang Formation shale of Sichuan Basin’s Pingye-1 Well, pioneers a paradigm shift by identifying Poisson’s ratio ($\mathsf{\nu }$) as the master variable governing this transition. Triaxial tests reveal that $\mathsf{\nu }$ systematically increases with depth, directly regulating the failure mode shift from brittle fracture to ductile flow. Building on this, we innovatively propose the Poisson’s Ratio-regulated Energy-based Brittleness Index (PNE-BI) model. This model achieves a decoupled diagnosis of BDT by quantifying how ν intrinsically orchestrates the energy redistribution between elastic storage and plastic dissipation, utilizing ν as the sole governing variable to regulate energy weighting for rapid and accurate distinction between brittle, transitional, and ductile states. Experiments confirm the $\mathsf{\nu }$-dominated energy evolution: Low $\mathsf{\nu }$ rocks favor elastic energy accumulation, while high $\mathsf{\nu }$ rocks (>0.22) exhibit a dramatic 1520% surge in plastic dissipation, dominating energy consumption (35.9%) and confirming that $\mathsf{\nu }$ enhances ductility by reducing intergranular sliding barriers. Compared to traditional multi-variable models, the PNE-BI model utilizes $\mathsf{\nu }$ values readily obtained from conventional well logs, providing a transformative field-ready tool that significantly reduces the experimental footprint and promotes resource efficiency. It guides toughened fracturing fluid design in ductile zones to suppress premature closure and optimizes injection rates in brittle zones to prevent fracture runaway, thereby enhancing operational longevity and minimizing environmental impact. This work offers a groundbreaking and sustainable solution for boosting the efficiency of mid-deep shale gas development, contributing directly to more responsible and cleaner energy extraction. Full article

High-altitude long-endurance (HALE) UAVs require navigation payloads that are both fully autonomous and lightweight. This paper presents a full-parameter calibration method for a dual-axis rotational-modulation RINS/CNS integrated system in which the IMU is mounted on a two-axis indexing mechanism and the reconnaissance camera is reused as the star sensor. We establish a unified error propagation model that simultaneously covers IMU device errors (bias, scale, cross-axis/installation), gimbal non-orthogonality and encoder angle errors, and camera exterior/interior parameters (EOPs/IOPs), including Brown–Conrady distortion. Building on this model, we design an error-decoupled calibration path that exploits (i) odd/even symmetry under inner-axis scans, (ii) basis switching via outer-axis waypoints, and (iii) frequency tagging through rate-limited triangular motions. A piecewise-constant system (PWCS)/SVD analysis quantifies segment-wise observability and guides trajectory tuning. Simulation and hardware-in-the-loop results show that all parameter groups converge primarily within the segments that excite them; the final relative errors are typically ≤5% in simulation and 6–$16\%$ with real IMU/gimbal data and catalog-based star pixels. Full article

Trajectory clustering is of great significance for identifying behavioral patterns and vessel types of non-cooperative ships. However, existing trajectory clustering methods suffer from limitations in extracting cross-spatiotemporal scale features and modeling the coupling relationship between positional and motion features, which restricts clustering performance. To address this, this study proposes a deep ship trajectory clustering method based on feature embedding representation learning (ERL-DTC). The method designs a Temporal Attention-based Multi-scale feature Aggregation Network (TA-MAN) to achieve dynamic fusion of trajectory features from micro to macro scales. A Dual-feature Self-attention Fusion Encoder (DualSFE) is employed to decouple and jointly represent the spatiotemporal position and motion features of trajectories. A two-stage optimization strategy of “pre-training and joint training” is adopted, combining contrastive loss and clustering loss to jointly constrain the embedding representation learning, ensuring it preserves trajectory similarity relationships while being adapted to the clustering task. Experiments on a public vessel trajectory dataset show that for a four-class task (K = 4), ERL-DTC improves ACC by approximately 14.1% compared to the current best deep clustering method, with NMI and ARI increasing by about 28.9% and 30.2%, respectively. It achieves the highest Silhouette Coefficient (SC) and the lowest Davies-Bouldin Index (DBI), indicating a tighter and more clearly separated cluster structure. Furthermore, its inference efficiency is improved by two orders of magnitude compared to traditional point-matching-based methods, without significantly increasing runtime due to model complexity. Ablation studies and parameter sensitivity analysis further validate the necessity of each module design and the rationality of hyperparameter settings. This research provides an efficient and robust solution for feature learning and clustering of vessel trajectories across spatiotemporal scales. Full article

High penetration of renewable energy sources complicates static voltage stability assessment, as conventional line-based indices are typically derived under restrictive assumptions, such as neglecting voltage-angle differences or decoupling active and reactive power effects, which may lead to inaccurate proximity signals under RES-rich operating conditions. The proposed research study develops an enhanced voltage stability index (EVSI) from a two-port π line model that explicitly retains line impedance, active and reactive power terms, and voltage-angle difference between the sending and receiving ends; secure system operation satisfies EVSI < 1. Unlike classical indices, EVSI preserves the coupled physical interactions most relevant to voltage collapse while maintaining a closed-form structure suitable for online monitoring. EVSI is evaluated in a coupled transmission–distribution setting with solar photovoltaic-based distributed generation under varying penetration levels and loadings, using PV-curve nose points as collapse references, and benchmarked against classical indices. Across scenarios, EVSI remains closest to unity at the nose point, accurately tracing the collapse boundary and consistently identifying weak buses, whereas the traditional indices exhibit dispersed values and sensitivity to operating assumptions. The proposed results indicate that EVSI offers a reliable and computationally convenient indicator for online assessment and early warning of voltage instability in renewable-integrated, coupled transmission–distribution networks. Full article

This article analyzes how Austrian economic principles contribute to bioeconomic development in Ecuador, emphasizing key aspects such as property rights, spontaneous order, entrepreneurial innovation, institutional frameworks and decentralized knowledge. The relevance of incorporating an instrumental case was established, in which the scale, composition, and technology (SCT) effects model was applied to the comparative analysis of Ecuador, Chile, Argentina, and Brazil during the 2000–2023 period. This study was complemented by multiple linear regression, which was used to evaluate the relationship between economic growth, CO₂ emissions, the agricultural industry, foreign direct investment, and a composite bioeconomy index. The results showed complete decoupling between GDP and emissions in Ecuador, driven by technological improvements and transformations in key sectors such as agriculture and renewable energy. Chile and Brazil also showed paths of complete decoupling, although to a lesser extent for the latter, while Argentina exhibited relative decoupling, in which bioeconomic growth continues to be associated with an increase in emissions. The estimated models present an R² (between 0.81 and 0.91). This study shows that it is possible to move towards a sustainable bioeconomy. Full article

Generative adversarial networks (GANs) can rapidly predict urban environmental performance. However, most existing studies focus on a single target and lack cross-performance comparisons under unified conditions. Under unified urban-form inputs and training settings, this study employs the conditional adversarial model pix2pix to predict four targets—the Universal Thermal Climate Index (UTCI), annual global solar radiation (Rad), sunshine duration (SolarH), and near-surface wind speed (Wind)—and establishes a closed-loop evaluation framework spanning pixel, structural/region-level, cross-task synergy, complexity, and efficiency. The results show that (1) the overall ranking in accuracy and structural consistency is SolarH ≈ Rad > UTCI > Wind; (2) per-epoch times are similar, whereas convergence epochs differ markedly, indicating that total time is primarily governed by convergence difficulty; (3) structurally, Rad/SolarH perform better on hot-region overlap and edge alignment, whereas Wind exhibits larger errors at corners and canyons; (4) in terms of learnability, texture variation explains errors far better than edge count; and (5) cross-task synergy is higher in low-value regions than in high-value regions, with Wind clearly decoupled from the other targets. The distinctive contribution lies in a unified, reproducible evaluation framework, together with learnability mechanisms and applicability bounds, providing fast and reliable evidence for performance-oriented planning and design. Full article

3D Gaussian Splatting (3DGS) is a multi-view 3D reconstruction method that relies solely on image loss for supervision, lacking explicit constraints on the geometric consistency of the rendering model. It uses a multi-view scene-by-scene training paradigm, which limits generalization to unknown scenes in the case of single-view limited input. To address these issues, this paper proposes a Geometric Consistency-High Generalization (GC-HG), a single-view 3DGS reconstruction framework integrating depth prior and a pseudo-triplane. First, we utilize the VGGT 3D geometry pre-trained model to derive depth prior, back-projecting them into point clouds to construct a dual-modal input alongside the image. Second, we introduce a pseudo-triplane mechanism with a learnable Z-plane token for feature decoupling and pseudo-triplane feature fusion, thereby enhancing geometry perception and consistency. Finally, we integrate a parent–child hierarchical Gaussian renderer into the feed-forward 3DGS framework, combining depth and 3D offsets to model depth and geometry information, while mapping parent and child Gaussians into a linear structure through an MLP. Evaluations on the RealEstate10K dataset validate our approach, demonstrating improvements in geometric modeling and generalization for single-view reconstruction. Our method improves Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) metrics, demonstrating its advantages in geometric consistency modeling and cross-scene generalization. Full article

Wildfires in the Wildland–Urban Interface (WUI) pose escalating threats to socio-ecological systems, challenging regional resilience and sustainable recovery. Understanding the compound impacts of such fires requires an integrated, data-driven assessment of both ecological disturbance and social response. This study develops a multi-dimensional framework combining multisource remote sensing data (Landsat/Sentinel-2 NDVI and VIIRS nighttime light) with socio-structural indicators. A Composite Disturbance Index (ImpactIndex) was constructed to quantify ecological, population, and socioeconomic disruption across six fire clusters in the January 2025 Southern California wildfires. Mechanism analysis was conducted using Fixed-Effects OLS (M2) and Geographically Weighted Regression (GWR, M3) models. The ImpactIndex revealed that Eaton and Palisades experienced the most severe compound disturbances, while Border 2 showed purely ecological impacts. During-disaster CNLI signals were statistically decoupled from ecological disturbance (ΔNDVI) and dominated by site-specific effects (p < 0.001). GWR results (Adj. R² = 0.354) confirmed asymmetric spatial heterogeneity: high-density clusters (Palisades, Kenneth) exhibited a significant “Structural Burden” effect, whereas low-density areas showed weak, nonsignificant recovery trends. This “Index-to-Mechanism” framework redefines the interpretation of nighttime light in disaster contexts and provides a robust, spatially explicit tool for targeted WUI resilience planning and post-fire recovery management. Full article

Achieving decoupling between economic growth and carbon emissions is imperative for global sustainable development. This study provides a comparative analysis of this decoupling process in the European Union (EU) and BRICS countries from 1996 to 2023, employing the Tapio decoupling model and Logarithmic Mean Divisia Index (LMDI) decomposition analysis. Our findings reveal a stark contrast: the EU has achieved an average annual carbon emission growth rate of −1%, predominantly characterized by strong decoupling, whereas the BRICS nations exhibit an average growth rate of 6.26%, mainly in a state of weak decoupling. The LMDI results indicate that the intensity effect is the primary driver of carbon reduction in the EU, while the income effect is the most significant factor promoting emissions growth in the BRICS bloc. A novel finding is the identification of a near-symmetrical relationship between the energy transition effect and the fossil energy structure effect in the cumulative decomposition charts, offering a new perspective for evaluating energy system changes. The study concludes that while the EU demonstrates a more advanced decoupling pathway, significant internal disparities persist. For BRICS countries, mitigating the pressure from economic and population growth through industrial upgrading, differentiated energy policies, and enhanced renewable infrastructure is crucial. These insights provide valuable policy implications for both developed and developing economies in navigating their low-carbon transitions. Full article

Background: Repetitive transcranial magnetic stimulation (rTMS) is an established neuromodulatory method, yet its multiscale neurophysiological effects in autism spectrum disorder (ASD) remain insufficiently characterized. Recent EEG analytic advances—such as spectral parameterization, long-range temporal correlation (LRTC) assessment, and connectivity modeling—enable quantitative evaluation of excitation–inhibition (E/I) balance and network organization. Objective: This study aimed to examine whether an eight-session, EEG-guided mixed-frequency rTMS protocol—combining inhibitory 1 Hz and excitatory 10 Hz trains individualized to quantitative EEG (qEEG) abnormalities—produces measurable changes in spectral dynamics, temporal correlations, and functional connectivity in a pediatric ASD case. Methods: An 11-year-old right-handed female with ASD (DSM-5-TR, ADOS-2) underwent resting-state EEG one week before and four months after intervention. Preprocessing used a validated automated pipeline, followed by spectral parameterization (FOOOF), detrended fluctuation analysis (DFA), and connectivity analyses (phase-lag index and Granger causality) in MATLAB (2023b). No inferential statistics were applied due to the single-case design. The study was conducted at Cosmos Healthcare (London, UK) with in-kind institutional support and approved by the Atlantic International University IRB (AIU-IRB-22-101). Results: Post-rTMS EEG showed (i) increased delta and reduced theta/alpha/beta power over central regions; (ii) steeper aperiodic slope and higher offset, maximal at Cz, suggesting increased inhibitory tone; (iii) reduced Hurst exponents (1–10 Hz) at Fz, Cz, and Pz, indicating decreased long-range temporal correlations; (iv) reorganization of hubs away from midline with marked Cz decoupling; and (v) strengthened parietal-to-central directional connectivity (Pz→Cz) with reduced Cz→Pz influence. Conclusions: Mixed-frequency, EEG-guided rTMS produced convergent changes across spectral, aperiodic, temporal, and connectivity measures consistent with modulation of cortical E/I balance and network organization. Findings are preliminary and hypothesis-generating. The study was supported by in-kind resources from Cosmos Healthcare, whose authors participated as investigators but had no influence on analysis or interpretation. Controlled trials are warranted to validate these exploratory results. Full article

Sudden Stratospheric Warmings (SSWs) are extreme polar atmospheric disturbances that significantly impact mid-latitude cold surges, but their early prediction remains a challenge for conventional numerical models. In this study, we propose a video prediction framework for SSW forecasting and introduce a Decoupled Spatiotemporal Memory Flow Network (DSMF-Net) to more effectively capture the dynamic evolution of stratospheric polar vortices. DSMF-Net separates spatial and temporal dependencies using specialized memory flow modules, enabling fine-grained modeling of vortex morphology and dynamic transitions. Experiments on representative SSW events from 2018 to 2021 show that DSMF-Net can reliably predict SSW occurrences up to 20 days in advance while accurately replicating the evolution of polar vortex structures. Compared to baseline models such as the Predictive Recurrent Neural Network (PredRNN) and Motion Recurrent Neural Network (MotionRNN), our method achieves consistent improvements across various metrics, with average gains of 10.5% in Mean Squared Error (MSE) and 6.4% in Mean Absolute Error (MAE) and a 0.7% increase in the Structural Similarity Index Measure (SSIM). These findings underscore the potential of deep video prediction frameworks to improve medium-range stratospheric forecasts and bridge the gap between data-driven models and atmospheric dynamics. Full article

This study examines the dynamic decoupling relationship between energy consumption and export growth in China since its accession to the World Trade Organization (WTO) (2002–2018) by combining the noncompetitive input–output model, Tapio decoupling model, and the Logarithmic Mean Divisia Index (LMDI) model. The results reveal the substantial energy consumption generated by China’s export trade, emphasizing the urgency of reducing energy consumption in export trade for energy conservation and emissions reduction. Since its WTO accession, China has experienced sustained improvement in the energy decoupling effect during the growth of export trade, entering a period of strong decoupling from 2014 to 2018. The expanded export scale remains a major obstacle to decoupling export trade growth from energy consumption, while decreased energy intensity in exports is a significant driving force for energy decoupling, with relatively minor impact from changes in the export trade structure. By integrating non-competitive input–output modeling, Tapio decoupling analysis, and LMDI decomposition, this study develops a novel framework to investigate the structural drivers of energy–export decoupling in China from 2002 to 2018. Bridging methods from energy systems, trade economics, and policy modeling, it contributes to the field of multi-disciplinary sustainability by offering sector-level insights and decomposition-based evidence to support more efficient, equitable, and sustainable trade transitions. Full article

Using economic and water consumption data from Inner Mongolia and its 12 cities (2004–2023), this study employs the Tapio decoupling model to investigate the relationship between water consumption and economic growth. The results show a general shift from weak to strong decoupling across the region, with extreme events such as the 2020–2021 pandemic period (decoupling index, DI = 10.31) causing clear disruptions. Regional disparities followed a triple pattern: industrial areas (e.g., Ordos, Baotou) achieved strong decoupling via innovation; agricultural regions (e.g., Tongliao, Bayannur) remained in weak negative decoupling modes due to rigid water demand; and ecologically vulnerable areas (e.g., Alxa League, Xilin Gol) saw high volatility and unsustainable policy effects. Our interpretation of the three patterns highlights the need for region-specific governance. The driving mechanisms mainly include uneven adoption of water-saving technology (e.g., low drip irrigation rates in agriculture), virtual water trade shifting pressures across regions, and climate extremes worsening imbalances. Based on these findings, we recommend differentiated subsidies, regional compensation mechanisms, and adaptive policies to support sustainable water–economy coordination in arid regions. Full article