Search Results (14,081)

Reliable perception in smoke-filled tunnels is essential for rescue robots, yet the temporal evolution of LiDAR degradation under realistic smoke conditions is not well quantified. This paper investigates the degradation of LiDAR data induced by environmental factors in a full-scale tunnel experiment involving three smoke scenarios, with real combustion smoke and theatrical smoke. To enable consistent comparison across experiments with different smoke dynamics, an RGB-based visibility reference is first used for temporal alignment across measurements. Based on this alignment, physically interpretable LiDAR indicators, such as intensity attenuation and range-dependent point density loss, are used to characterize smoke-induced changes in the LiDAR data. In addition, an established Deep semi-supervised anomaly detection (DeepSAD) model is employed to derive a continuous data-driven degradation score that indicates deviations from nominal LiDAR range image patterns. The learned score remains stable under clear-air conditions, despite geometric variations caused by object and sensor movement. During smoke exposure, the score increases in all smoke scenarios, although the temporal evolution differs between scenarios. The results show that the degradation score derived from DeepSAD provides a continuous data-driven description of changes in LiDAR range images under smoke exposure. Overall, the study presents an experimental analysis of LiDAR degradation using full-scale tunnel experiments with smoke, reporting physically interpretable LiDAR indicators and a continuous data-driven degradation score. Full article

5052 aluminum alloy has been widely used in aerospace, shipbuilding, automotive, and electronic industries due to its low density, high specific strength, and excellent corrosion resistance. Understanding its laser-induced damage behavior under combined continuous-wave (CW) and nanosecond (ns) pulse laser irradiation is essential for optimizing combined laser processing. In this study, the damage behaviors induced by individual CW laser, individual ns pulse laser, and combined pulse laser were systematically investigated using high-speed imaging, infrared thermography, and three-dimensional surface characterization. The results show that the combined pulse laser significantly enhances both damage depth and material removal efficiency compared with single laser irradiation. Although the peak surface temperature remains nearly unchanged under different processing conditions, the crater morphology and penetration depth vary substantially. High-speed imaging reveals that plasma evolution and molten metal ejection dominate the material removal process. Variations in processing parameters significantly modify molten pool dynamics and plasma behavior. In particular, enhanced plasma shielding or excessive energy dissipation reduces the effective laser energy coupling, leading to decreased material removal efficiency. The synergistic interaction among molten pool evolution, plasma expansion, and molten metal ejection governs the final damage morphology. This study provides new insights into the dynamic interaction mechanisms between combined pulse laser and aluminum alloys, offering guidance for parameter optimization in high-precision laser micromachining. Full article

In recent years, the city of Naples has seen increasing attention focused on the mural dedicated to Diego Armando Maradona, located in the Spanish Quarter. Originally a spontaneous expression of street art and initially perceived by the local community as a simple artwork, the mural has gradually transformed into a powerful symbol of identity, becoming a true cultural icon of the city. This process has generated effects that transcend the artistic value of the artwork, contributing to the enhancement of the entire surrounding urban context and altering the social and economic dynamics of the quarter. By considering the Maradona Mural as the epicentre of a range of territorial and socio-economic effects, this paper aims to explore the issue of measuring the influence exerted by its presence on the market values of residential properties located in the Spanish Quarter of Naples. From this perspective, street art can be interpreted as a positive externality capable not only of fostering processes of urban regeneration and the redevelopment of formerly marginalised or degraded areas but also of influencing the dynamics of the local real estate market. Property values reflect not only the intrinsic characteristics of the properties but also the environmental and territorial attributes that contribute to the formation of market prices. From this perspective, properties can be considered as beneficiaries of externalities, whose economic value can be estimated through variations in house prices attributable to changes in environmental and territorial conditions. Full article

Global warming poses a serious environmental challenge worldwide. Agriculture, as a significant source of greenhouse gas (GHG) emissions, exerts considerable influence on the atmospheric environment. Chengdu, renowned for its thriving agricultural sector, serves as a key grain production center in China. Reducing agricultural greenhouse gas (AGHG) emissions is essential for mitigating the impact of climate change on Chengdu. Firstly, this paper employed the IPCC (Intergovernmental Panel on Climate Change) coefficient method and the Super-SBM-Undesired model to calculate the AGHG emissions and emission efficiency in Chengdu, respectively. Then, center of gravity shift analysis, kernel density estimation and spatial autocorrelation theory were used to analyze the spatial–temporal evolution characteristics of AGHG emissions. Finally, this paper conducted an in-depth analysis based on the STIRPAT model to identify key factors affecting AGHG emissions. The results show that: (1) From 2007 to 2021, Chengdu experienced an overall decline in both AGHG emissions and emission intensity, with reductions of 22.32% and 66.20%, respectively. And the AGHG emission efficiency was largely low. (2) AGHG emissions display regional variations and spatial clustering phenomena, characterized by a pattern of “high in the east, low in the west, high outside and low inside”. (3) AGHG emissions are highly increased by the sown area (S) and pesticide and fertilizer utilization (F) and may be reduced by the agricultural industrial structure (V) and the urbanization rate (U). These findings provide valuable scientific insights into the spatial–temporal dynamics of regional agricultural emissions. Furthermore, this study offers practical references for local governments to optimize agricultural resource allocation, formulate tailored low-carbon agricultural policies, and promote sustainable rural development. Full article

The digital transformation of financial services has increased the need for intelligent information systems capable of supporting credit risk assessment in dynamic and data-intensive environments. Traditional credit scoring approaches often face limitations when evaluating customers with limited financial histories, heterogeneous data sources, and rapidly evolving behavioral patterns. In response to these challenges, this study proposes an adaptive credit risk assessment framework that integrates machine learning, an Adaptive Champion–Challenger strategy, and MLOps practices within a unified information systems architecture. The proposed framework was evaluated using real operational data obtained from a Mexican FinTech company specializing in mobile phone financing. Three machine learning algorithms—Logistic Regression, XGBoost, and TabNet—were implemented and continuously evaluated through a rolling Champion–Challenger process supported by out-of-time validation and statistically validated model promotion criteria. Experimental results indicate that different algorithms became optimal during different evaluation periods, indicating that model effectiveness varied over time as customer behavior and portfolio characteristics evolved. While XGBoost served as the initial static baseline model, TabNet and Logistic Regression achieved superior performance during several evaluation periods, illustrating the potential benefits of adaptive model selection under changing data conditions. The proposed Adaptive Champion–Challenger Framework achieved a mean AUC of 0.817, compared with 0.798 obtained by the static baseline model. Statistical validation using the DeLong test for correlated ROC curves confirmed that the observed performance improvement was significant (p = 0.0021), providing evidence that the performance gains achieved by the adaptive strategy were unlikely to be attributable to random variation. From a Digital Transformation and Information Systems perspective, the findings suggest that maintaining predictive effectiveness in dynamic FinTech environments requires not only high-performing machine learning algorithms but also governance mechanisms that support continuous model evaluation, monitoring, traceability, and adaptive model selection. The results indicate that periodic model replacement based on statistically validated out-of-time performance can help maintain predictive effectiveness under changing data conditions while supporting model governance and operational reliability. Overall, the proposed framework provides a practical and scalable approach for implementing adaptive credit risk assessment systems that support continuous model governance, data-driven decision-making, and the management of machine learning models in alternative financing environments. Full article

Multiphase swirling flows in confined spaces induce highly destructive, nonlinear fluid–structure interaction (FSI) vibrations. Understanding the underlying physical mechanisms is critical for ensuring the safety of industrial operations. This study proposes a mesoscopic multiscale framework coupling the Multi-Relaxation Time Lattice Boltzmann Method with Large Eddy Simulation (MRT-LBM-LES) and the Flügge thin-walled cylindrical shell equations to analyze two-way FSI responses. Variational Mode Decomposition (VMD) and the Hilbert–Huang Transform (HHT) are employed to decouple non-stationary broadband excitation signals. The macroscopic topological evolution of the swirling air core—from initial depression to critical breakthrough—is accurately captured. Dynamic mapping reveals a strict time-domain phase-locking mechanism between macroscopic flow instability and microscopic high-frequency structural excitation caused by cavitation bubble collapse. Furthermore, a dimensionless cross-scale energy cascade index is defined to quantify energy transfer. Results indicate that while higher discharge flow rates delay the critical breakthrough, they trigger a delayed, high-amplitude step mutation in the energy cascade, amplifying the global cumulative excitation energy by nearly 75%. Notably, the dominant high-frequency excitation consistently converges within a narrow band of 760 Hz to 790 Hz, independent of flow rate variations. These findings provide a theoretical foundation for unsteady excitation source localization and targeted vibration reduction in complex industrial pipeline networks. Full article

Image reconstruction in nighttime dynamic scenes is challenged by low illumination, long exposure, rapid camera or object motion, and sensor noise. Conventional RGB cameras, therefore, struggle to recover both sufficient brightness and clear structural details in nighttime dynamic scenes. To address this problem, we propose an event-guided image reconstruction method for nighttime dynamic visual perception. The method constructs a multi-channel event voxel representation by jointly encoding event count, event intensity, timestamp distribution, and blurred-frame intensity priors. A parameter-efficient local–global reconstruction network is then designed to restore fine-grained textures and model holistic structures. In addition, edge-alignment and blur-alignment constraints are introduced to improve geometric consistency and imaging plausibility. Experiments on the HQF and REDS datasets show that the proposed method outperforms existing methods in the MSE, PSNR, and SSIM. Compared with DeblurSR, it reduces the MSE by 14.81% on HQF and 10.00% on REDS, while improving the PSNR by 1.603 dB and 1.053 dB, respectively. Qualitative results further show sharper edges, lower structural errors, and better edge consistency. Low illumination, dynamic blur, rapid brightness variation, and event noise are also common degradation factors in nighttime UAV imaging, making the investigated problem technically relevant to that setting. However, because neither REDS nor HQF was acquired during an actual UAV flight, the reported results establish benchmark-level reconstruction performance rather than UAV-specific operational effectiveness. Full article

Skeleton-based action recognition has attracted considerable research interest because skeleton data are inherently robust to illumination changes, viewpoint variation, background clutter, and camera motion. Nevertheless, extracting informative representations from skeleton sequences remains a challenging problem, as it requires capturing both the spatial co-occurrence patterns among body joints and the fine-grained kinematic cues that distinguish different actions. In this paper, we propose a single-stream architecture that constructs an action-specific skeleton graph directly from motion and processes it with a kinematic-aware Transformer. Rather than relying on a fixed skeleton topology, a motion-guided dynamic-graph construction module infers a per-frame adjacency matrix from short-term motion cues through a differentiable edge predictor and Gumbel-Softmax sparsification, allowing the model to discover action-driven connections between distant joints that lack direct bone connectivity (e.g., coordinated hand motion during clapping). Each joint is described by kinematic node features that combine its 3D position, instantaneous velocity, and limb-angle encodings within a single descriptor, so that both motion dynamics and higher-order limb configurations are available to the spatial encoder from the outset. A graph-attention network (GAT) encodes the spatial configuration of every frame over the learned graph, and the resulting sequence of frame descriptors is processed by a Transformer encoder that models long-range temporal dependencies; a learnable classification token aggregates the sequence, and a multi-layer perceptron (MLP) produces the final action classification. The entire model is trained end-to-end from action labels alone. We conduct a comprehensive ablation study and evaluate the proposed method on the large-scale NTU RGB+D 60 and NTU RGB+D 120 benchmarks, where the results demonstrate that our approach achieves competitive performance compared to state-of-the-art architectures. Full article

Under ground-based observation geometry, infrared radiant-intensity sequences of space objects are jointly influenced by object micromotion, thermal radiation, time-varying viewing conditions, and atmospheric propagation. Existing simulation studies often prescribe the line of sight or simplify the coupling between viewing geometry and atmospheric attenuation, which limits long-duration ground-based sequence analysis. This study develops a physics-informed framework for generating atmosphere-attenuated infrared radiant-intensity sequences of space objects undergoing precession or tumbling. The framework reconstructs observation geometry from azimuth–elevation–range trajectories, updates facet normals through a unified micromotion attitude model, computes visible projected area and transient facet temperature, and incorporates MODTRAN-derived elevation-dependent atmospheric transmittance. Using this framework, we construct IRPeriodic, an eight-class simulated dataset for long-duration univariate time-series classification. We further propose LPD-Net, which integrates large-kernel residual feature extraction, prototype-guided dynamic temporal alignment, and differential periodic representation to capture long-range waveform morphology, sample-dependent temporal correspondence, and segment-level local variation. On IRPeriodic, LPD-Net achieves an accuracy of 0.8618 ± 0.0057, a macro-F1 of 0.8615 ± 0.0061, and a Matthews correlation coefficient of 0.8426 ± 0.0065, outperforming the evaluated neural-network and ROCKET-type baselines. Ablation and synthetic-noise sensitivity analyses indicate that the performance gain is mainly associated with long-context feature extraction, with additional improvements from dynamic alignment and differential periodic statistics. Auxiliary experiments on selected public UCR datasets suggest that the representation is also competitive for univariate time-series classification. These results demonstrate the effectiveness of LPD-Net on the proposed physics-informed benchmark for long-duration ground-based infrared radiant-intensity sequence classification. Full article

Pathological gait classification plays an important role in objective motor function assessment, early clinical screening, and rehabilitation evaluation. However, traditional clinical gait assessment methods are highly dependent on expert experience and may fail to detect subtle gait abnormalities. Moreover, existing inertial measurement unit (IMU)-based gait recognition methods often rely on single-sensor configurations or single-scale temporal models, limiting their ability to capture complex pathological gait patterns. In this study, a convolutional neural network–bidirectional long short-term memory–temporal convolutional network (CNN-BiLSTM-TCN) multi-branch feature fusion framework was proposed for pathological gait classification using a publicly available clinical multi-inertial measurement unit dataset containing 260 subjects. The proposed model employs three parallel branches to extract local instantaneous motion variations, continuous temporal dynamics, and relatively broader temporal dependencies within the 2 s input window, respectively, followed by feature-level fusion and end-to-end joint optimization. Experimental results show that the proposed model achieves a test accuracy of 0.9818 and an F1-score of 0.9700, outperforming conventional machine learning methods, single-branch models, voting-based fusion methods, and other temporal models, including Support Vector Machine (SVM), Temporal Convolutional Network (TCN), and Convolutional Neural Network-long short-term memory (CNN-LSTM). Five repeated experiments with stratified random splits demonstrate minimal performance variation, indicating good robustness and stability. The proposed framework provides a potential approach for pathological gait screening and quantitative rehabilitation assessment. Full article

Background: Cell migration is a biological process influenced by biochemical signals and mechanical stimuli from the microenvironment. In this context, the accurate characterization of the mechanical microenvironment generated within microfluidic platforms represents an essential step for the design and interpretation of cell migration studies. Understanding how hydrodynamic forces influence the mechanical microenvironment experienced by cells remains a challenge, especially in confined and biomimetic systems. Methods: In this study, a three-dimensional microfluidic device was developed in silico to characterize the effects of flow variation on mechanofluidic parameters and to provide a quantitative basis for designing future cell-migration experiments. Computational fluid dynamics simulations were performed to characterize the velocity, pressure, and wall shear stress (WSS) distributions under different inlet flow rates (0.5, 1, and 5 µL/min) and three distinct inlet/outlet configurations within the same three-dimensional geometry. Rigid hemispherical probe structures were incorporated into the model to quantify the local shear stress acting on cell-sized surfaces. Results: The results demonstrated a direct and linear relationship between the applied flow rate and the WSS, modulated by the channel geometry and the inlet and outlet configuration. Regions near micropores and lateral channels showed high WSS values, while central regions experienced less mechanical stimulation, depending on flow conditions. Comparison with WSS values and ranges associated with cellular responses reported in the literature indicated that certain operational configurations generated mechanical conditions comparable to those previously investigated in cell-based studies, including cell migration applications. Conclusions: Overall, the study highlights the importance of controlling flow conditions in microfluidic platforms and provides a quantitative basis for the development and optimization of three-dimensional microfluidic devices intended for designing future cell-migration experiments. The systematic comparison of three inlet/outlet configurations across three flow rates within the same three-dimensional geometry provides a comparative framework for identifying configuration-dependent changes in the local mechanofluidic environment, supporting the selection of operational conditions for future mechanobiological and cell-migration studies. Full article

Hydrogenation reactors are critical equipment in the petrochemical industry, yet their material degradation is governed by coupled multi-mechanism damage. Current assessment practices largely neglect this complexity, remaining single-factor oriented and overlooking synergistic interactions and temporal evolution. This paper proposes a regionally differentiated, multi-level framework integrating 5 primary and 17 secondary indicators with a hybrid AHP-EWM weighting strategy that synthesizes expert knowledge and measured data. A multi-factor coupling correction coefficient is introduced to provide a preliminary estimate of the synergistic acceleration effect among damage mechanisms, while a GM(1,1) gray model enables dynamic trend prediction. Applied to a 25-year 2.25Cr-1Mo steel reactor, the method produces regional degradation values of 0.378, 0.607, and 0.533 for the base metal, welds, and cladding layer, respectively, with an overall baseline of 0.453 rising by 11% to 0.503 after coupling correction. Compared with exponential regression, ARIMA, and BP neural networks, GM(1,1) is selected for its balanced performance in small-sample fitting, extrapolation stability, and physical interpretability. Sensitivity analysis confirms stable degradation grading even with ±50% coupling coefficient variations. The proposed approach mitigates the underestimation inherent in conventional single-mechanism assessments and offers a quantitative tool for full-lifecycle risk management and predictive maintenance of hydrogenation reactors. Full article

Soil moisture is critical to the ecological stability and hydrological cycle. Optical remote sensing is severely constrained by cloud cover, snow, and frozen soil in alpine regions, hindering long-term soil moisture monitoring. Taking Nagqu region in the Tibetan Plateau as the study area, this paper constructs a pure microwave soil moisture inversion framework based on multi-temporal Sentinel-1 SAR data to avoid optical data dependence. Four SAR vegetation indices (DpRVI, RVI, DpSVI and PRVIc) were integrated into the coupled WCM–Oh2004 model to dynamically correct vegetation attenuation and surface scattering. The results show that the DpRVI-based model performs best, with R = 0.85 and RMSE = 0.0709 cm³/cm³, outperforming other indices. The framework maintains stable accuracy in the growing season and effectively captures spatiotemporal soil moisture variations. The proposed SAR-only method agrees well with official downscaled soil moisture products, proving its applicability for continuous soil moisture monitoring in optically inaccessible alpine regions. Full article

This study investigates the relationship between cultural compliance and late dry-season vegetation dynamics in Southwest Sumba, Indonesia, by analysing a decade of August Normalised Difference Vegetation Index (NDVI) data (2016–2025) and Cultural Compliance Scores (CCS) across three sites. Underpinned by lende ura, a local customary philosophy conceptualising the forest as a ‘rain bridge’ that sustains water availability, this cultural framework fosters community commitment to forest conservation. Empirically, this contrast is evident where Delo, actively practising this philosophy, maintained a high mean NDVI (0.90) with a markedly low Coefficient of Variation (CV: 1.96%), whereas Mangga Nippi and Lua Koba exhibited lower mean NDVI (0.58 and 0.66) and higher variability (CV: 26.54% and 11.06%), indicating biocultural divergence. Consequently, this erosion of traditional practices is associated with changes in vegetation and observed declines in hydrological functions, making water increasingly difficult to access and heightening economic vulnerability within an environmental poverty trap. These findings indicate that late dry-season vegetation stability in this dryland setting is closely associated with the strength of customary governance, suggesting that traditional institutions may complement the formal conservation of natural habitats. By pairing a CCS with a decade of NDVI variability, this study provides a replicable framework and testable hypotheses for examining biocultural dynamics in comparable seasonally dry tropical landscapes. Full article

Woody biomass is often promoted as a low-carbon energy source in global decarbonization efforts. However, LCA (life cycle assessment) evaluations of woody biomass-to-energy systems show very different environmental performance. Variations in technology and methodology across investigations can cause these inconsistencies. This review paper analyzes methodologies of LCAs of woody biomass conversion routes such as combustion, combined heat and power, gasification, pyrolysis, torrefaction-assisted systems, and new bioenergy with carbon capture configurations. A systematic literature review was conducted using Scopus, SpringerLink, and ScienceDirect, identifying 4272 records, of which 98 studies were retained for detailed analysis following the application of defined inclusion and exclusion criteria. Functional unit selection, from biomass mass per unit to power or heat per unit, is highly variable, affecting comparability. Forest carbon stock fluctuations, infrastructure, and end-of-life treatment are inconsistently included in cradle-to-grave system boundaries. Static GWP100 methods are often used in biogenic carbon removal without considering temporal carbon dynamics. The importance of pretreatment steps like drying, pelletizing, and torrefaction cannot be overstated, even though they have a direct impact on the quality of the fuel, the efficiency of transportation, and the effectiveness of the conversion process downstream. The large range of stated emission levels for comparable technologies is further influenced by logistics assumptions, plant scale, and allocation mechanisms in cogeneration systems. The review synthesizes these methodological differences and proposes a harmonization methodology to increase woody biomass LCA transparency and comparability. By identifying important sources of outcome variability, this study helps policymakers, project developers, and industry stakeholders evaluate biomass energy investments and bring clarity to environmental decisions. Full article