Search Results (14,115)

In the context of highly interconnected global markets and the rapid dissemination of multilingual information, traditional risk prediction methods that rely on single numerical sequences or monolingual text are insufficient for achieving early perception of cross-market risks. To address this issue, a cross-market risk early warning framework based on multilingual large language models and multimodal sensing fusion is proposed. The proposed approach is centered on a unified risk semantic space, where cross-lingual semantic alignment is employed to reduce semantic discrepancies across languages. Furthermore, a semantic–volatility coupling attention mechanism is introduced to capture the dynamic relationship between textual semantic evolution and market fluctuations. In addition, cross-market knowledge transfer and low-resource enhancement strategies are incorporated to improve the model’s generalization capability across multilingual and multi-market environments, thereby establishing an intelligent perception and early warning system for complex sensing scenarios. Experimental results demonstrate that the proposed method significantly outperforms multiple baseline models in multilingual cross-market risk prediction tasks. In the main experiment, the model achieves a root mean squared error (RMSE) of $0.1127$, an mean absolute error (MAE) of $0.0846$, and an area under the curve (AUC) of $0.8879$, while the early warning gain is improved to $5.2$ days, which is substantially better than the Transformer model (RMSE $0.1365$, AUC $0.8042$) and the multilingual BERT-based fusion model (AUC $0.8395$). In terms of classification performance, higher accuracy, precision, and recall are consistently achieved, with overall accuracy exceeding $0.88$, and both precision and recall are maintained above $0.85$, indicating strong discriminative capability in risk identification tasks. Cross-lingual generalization experiments further verify the robustness of the proposed framework. When trained solely on the English market, the model achieves AUC values of $0.8624$ and $0.8471$ on the Chinese and European markets, respectively, with RMSE reduced to $0.1185$, significantly outperforming competing methods. Overall, the proposed approach achieves substantial improvements in prediction accuracy, cross-lingual generalization, and early warning performance, providing an effective solution for artificial intelligence-driven sensing and risk early warning. Full article

To ensure safe and stable operation of towed array systems in complex marine environments, the concept of a Safe Maneuvering Envelope (SME) for towing maneuvers is proposed based on flexible cable dynamics theory. The dynamic equations of the towed array are established using the Lumped Mass Method. Using diving depth and breaking tension as boundaries, array configuration data sets are calculated for combinations of main cable outer diameter, vessel speed, and deployed cable length. Mapping relationships between vessel speed, cable deployment length, diving depth, and breaking strength are presented to construct the maneuvering safety envelope. This envelope defines the operational range where the array meets design maneuverability criteria. The safety envelope concept provides quantitative operational guidelines for towed array systems and offers crucial theoretical foundations and methodological support for safe system design and risk assessment. Full article

In recent years, multi-view learning has received extensive research interest. Most existing multi-view learning methods often rely on well-annotated data to improve decision accuracy. However, noisy labels are ubiquitous in multi-view data due to imperfect annotations. Although some methods have achieved promising performance using robust-loss designs and implicit regularization, they fail to explicitly model the reliability of the supervision signal and fail to dynamically correct noisy labels during training. Clearly, this largely constrains their performance ceiling. To deal with this problem, we propose an Error-Entropy-Guided Distillation network (EGD) for noisy multi-view classification. In this framework, we first design an Error-Entropy (EE) metric to explicitly evaluate the reliability of sample-wise supervision, which serves as the basis for identifying and filtering noisy labels. On this basis, we adopt the distillation paradigm based on Error-Entropy (EE). The teacher model provides the student with soft label distributions that are less affected by noisy labels in the early training stage. To further mitigate noise memorization and accumulated confirmation bias, we propose a periodic memory-clearing strategy and supervision signal update strategy to prevent the teacher from error memorization and accumulating confirmation bias. Meanwhile, the student model learns from the soft supervision of the teacher to capture structured inter-class relationships. Additionally, a consistency module is employed to enhance the consistency of the student across multiple views. Extensive experiments on five benchmark datasets demonstrate that EGD consistently outperforms state-of-the-art multi-view learning methods under various noise levels. Full article

Folic acid, an essential vitamin for human health, plays a crucial role in maintaining intestinal homeostasis and functional stability, and its absorption is frequently impaired in Crohn’s disease, where it is closely associated with clinical complications and nutritional management. Nevertheless, the quantitative relationship between the complex multiscale architecture of intestinal villi, their morphological dynamics, and the efficiency of folic acid absorption remains insufficiently understood, primarily because existing studies rely on oversimplified representations of villous geometry and neglect the internal vascular structure, thereby limiting their ability to capture the coupled transport processes within individual villi. While existing studies have considered the influence of villous morphology on intestinal absorption, they generally rely on oversimplified representations and do not account for the internal structural organization of villi. This study aims to elucidate the quantitative relationship between villous multiscale architecture and folic acid absorption efficiency under pathological conditions of Crohn’s disease. Herein, a two-dimensional multiphysics numerical model is developed that integrates the external environment of intestinal villi with their internal microstructure, simulating folic acid transport via diffusion and Michaelis–Menten kinetics, coupled with convection–diffusion in the microvascular network under Stokes flow conditions. We find a reduction in villus height to 400 μm or local blood flow velocity to 0.01 mm/s leads to a marked decrease in folic acid absorption capacity, by approximately 57% and 50%, respectively. These changes are primarily attributed to inflammation-induced villus atrophy, which reduces the effective absorptive surface area. Furthermore, reduced blood flow velocity lowers the Peclet number, facilitating the accumulation of folic acid within the villi, which in turn further reduces the efficiency of folic acid absorption. This work contributes to a deeper understanding of how diseases affect the absorptive function of intestinal villi and provides a theoretical basis for the pathological mechanisms of the gut. Full article

With the transformation of resource-based cities and the restructuring of industrial sectors, the sustainable conservation of coal mining heritage has become a global focus. In China, coal mining heritage faces challenges such as degradation and inadequate management, highlighting the urgent need for more context-sensitive and systematic conservation approaches. This study develops an integrated, landscape-oriented analytical framework for stakeholder identification to address these challenges and to better understand stakeholder differentiation in coal mining heritage conservation. The research objectives are as follows: (1) to bring together a three-dimensional framework based on material-technical, socio-cultural, and experiential dimensions; (2) to analyse the roles and interactions of stakeholders; and (3) to explore how technical knowledge, socio-cultural memory, and daily experiences influence the protection and reuse of coal mining heritage sites. The study integrates the theoretical frameworks of landscape character assessment, historic urban landscape, and experiential landscape, using data from field observations and interviews analysed via ATLAS.ti. The findings show that the proposed framework offers a more systematic understanding of the dynamic relationships between stakeholders and heritage landscapes, thereby providing practical guidance for local governments and relevant institutions in developing inclusive and context-sensitive conservation strategies. Full article

Energy use remains central to Colombia’s economic growth, yet its composition shapes the scale and direction of environmental outcomes. This study investigates how coal, oil, and hydroelectricity influence ecological degradation within the context of economic growth. The study applies cross-quantilogram and bootstrap Fourier Granger causality techniques to capture directional dependence and predictive causality across different quantiles, respectively. The findings show that the relationships are heterogeneous rather than uniform across the distribution. Economic growth exhibits a predominantly negative dependence on ecological footprint, suggesting that higher output is associated with lower ecological pressure under several environmental states. Hydroelectricity also shows a largely negative dependence, indicating its general contribution to environmental sustainability, although this effect weakens under extreme conditions. By contrast, the effects of coal and oil are more conditional and vary across quantiles, reflecting the complex role of fossil fuels in Colombia’s environmental dynamics. The bootstrap Fourier Granger causality results further reveal that causality is not constant across the distribution, but emerges only at specific quantiles. The central policy implication from this result lies in adopting an adaptive environmental strategy in which preventive measures dominate under low degradation, green-supportive policies are emphasized under moderate degradation, and stronger corrective interventions are implemented under high ecological stress. Full article

Accurately quantifying carbon dioxide (CO₂) emissions from heavy-duty diesel trucks (HDTs) is crucial for developing effective transportation emission reduction strategies. In this study, we adopted a bottom–up approach and, in conjunction with the “International Vehicle Emissions” (IVE) model, constructed a high-resolution 1 × 1 km CO₂ emission inventory for the urban area of Kunming, China. Using data from 1.24 million track points collected from 5996 heavy-duty diesel trucks, we implemented a map matching algorithm based on a simplified hidden Markov model (HMM) to efficiently process large-scale GPS data. Furthermore, we improved upon traditional spatial allocation methods by dynamically integrating track point density with static road network density. The results indicate that although higher driving speeds correspond to lower CO₂ emission rates, heavy-duty diesel trucks typically operate within an observed speed range of 40–60 km/h, with an average emission factor of approximately 500 g/km. Vehicles compliant with the “National III” emission standards remain the primary source of CO₂ emissions in this region. Correlation analysis reveals a significant positive relationship (p < 0.01) between emissions from heavy-duty diesel trucks and both traffic volume and mileage. Notably, daytime vehicle restriction policies led to a temporal redistribution of emissions rather than a net reduction in emissions; this resulted in increased activity levels of heavy-duty diesel trucks at night, leading to a surge in nighttime emissions. In terms of spatial distribution, the “dual-density” allocation method proposed in this study more accurately captured emission hotspots, revealing that CO₂ emissions are primarily concentrated in the southeastern part of the city—a distribution pattern largely influenced by the city’s industrial layout. Full article

Internal phosphorus loading is a key process sustaining eutrophication in stratified Baltic Sea coastal systems, yet its long-term dynamics in the Archipelago Sea remain poorly quantified due to limited deep-water monitoring and the absence of sediment time series. This study provides a multidecadal assessment of internal loading from the early 1980s to 2025 using two complementary indicators: (i) seasonal accumulation of total phosphorus in the surface layer (ΔTP) and (ii) the covariation between near-bottom oxygen depletion and dissolved inorganic phosphorus (DIP) release. Temporal associations with external phosphorus inputs from marine fish farming—highly variable during the study period—were analyzed to evaluate whether cumulative loading trajectories coincided with phases of intensified ΔTP. New measurements of drifting filamentous macroalgae from 2025 were additionally used to assess their seasonal contribution to the internal phosphorus pool and their relevance for mitigation. Results show a pronounced multidecadal strengthening of internal loading signals in the mid and inner Archipelago Sea. At the Seili station, ΔTP increased by approximately 6.8 µg L⁻¹ (≈3.4-fold) since the early 1980s. This trend coincided with long-term deterioration of near-bottom oxygen conditions and increasing DIP concentrations, consistent with enhanced sediment phosphorus release. Although cumulative aquaculture loading exhibited simple correlations with ΔTP, detrended analyses indicate that these relationships largely reflect shared long-term trends rather than direct causal linkages. Drifting filamentous macroalgae formed a substantial seasonal phosphorus reservoir (≈146 t P). Overall, internal phosphorus input to the Archipelago Sea has intensified markedly—by an estimated ~70% since the 1980s—highlighting the growing importance of sediment–water feedbacks and legacy phosphorus. Effective mitigation therefore requires strategies that address both internal recycling processes and external nutrient inputs. Targeted removal of drifting filamentous macroalgae may provide a complementary nutrient-export pathway in coastal management. Full article

Hybrid supramolecular–nanocomposite hydrogels based on polyethylene glycol (PEG), β-cyclodextrin–adamantane host–guest interactions, and silica nanoparticles represent an important class of hierarchical soft materials with tunable viscoelastic and transport properties. This review critically analyzes recent progress in cyclodextrin–silica hybrid PEG hydrogels, focusing on the mechanistic coupling between stiffness, stress relaxation, and molecular transport arising from the interplay between reversible supramolecular crosslinks and nanoparticle-induced confinement effects. Particular attention is given to how host–guest exchange kinetics regulate dynamic bond rearrangement and affinity-mediated retention of hydrophobic cargo, while silica nanoparticles enhance mechanical reinforcement and modify diffusion pathways through tortuosity and interfacial polymer–particle interactions. The analysis highlights how nanoparticle size, loading level, and surface functionalization influence relaxation spectra and network topology, as well as how environmental stimuli may affect supramolecular bond stability and overall material performance. Comparison with alternative inorganic fillers and mesoporous silica architectures further clarifies the specific advantages of silica in achieving balanced mechanical stability and controlled transport behavior. Overall, current evidence indicates that hybrid CD–silica networks enable partial decoupling of stiffness, relaxation dynamics, and diffusion, although complete independence remains constrained by fundamental polymer physics relationships. These insights support the development of predictive structure–property frameworks for advanced biomedical and controlled release applications. Full article

This study investigates the structural relationship between inflation and key macroeconomic fundamentals in Türkiye, an emerging economy characterized by persistently high and divergent inflation dynamics. Using monthly data for the 2011–2024 period, we apply the Kapetanios, Shin, and Snell (KSS) nonlinear cointegration framework, which captures asymmetric adjustment dynamics that standard linear models fail to detect. The aggregate model reveals no long-run cointegration between inflation and monetary and fiscal fundamentals, indicating that conventional transmission channels have weakened and inflation has become decoupled from its traditional determinants. Pairwise analyses show that this decoupling is not complete; rather, the relationship persists in a fragmented, nonlinear, and variable-specific manner. Short-run Granger causality tests further reveal that only fiscal expansion and real money supply retain explanatory power over inflation, while the policy rate proves ineffective. Collectively, these findings indicate that inflation in Türkiye has increasingly evolved into an endogenous and self-reinforcing process, shaped more by policy incoherence than by any single macroeconomic driver. Restoring a coordinated, rule-based monetary and fiscal policy framework emerges as a necessary condition for re-establishing the link between inflation and macroeconomic fundamentals and ensuring durable price stability. Full article

Tourist congestion and load imbalance during peak periods pose critical challenges to the safe operation and experience assurance of large scenic areas. To address the limitations of traditional management approaches in capturing the dynamic and stochastic nature of tourist flows, this study develops a dynamic visitor guidance modeling and analysis framework based on a Timed Petri Net. The proposed model provides a formal representation of tourist movements, scenic spot load evolution, and guidance decision mechanisms within a scenic area. Under unified parameter settings and controlled random conditions, multiple visitor guidance strategies with different information coverage scopes are designed, and minute-level simulation experiments are conducted using the Huangshan Scenic Area as a case study. The simulation results show that, compared with unguided tourist flows, the proposed strategies significantly reduce average load levels, alleviate spatial load imbalance, and enhance TS. Using mean–standard deviation analysis, distributional analysis, and dynamic evolution analysis, differences among guidance strategies in terms of load control, visitor experience, and operational stability are systematically evaluated. Furthermore, a quantitative relationship model between tourist satisfaction and scenic area load is constructed, revealing a consistent inverted-U pattern. Robustness tests under multiple random seeds indicate that the main conclusions are not sensitive to specific stochastic realizations. Overall, the simulation results suggest that dynamic visitor guidance may improve load control, visitor experience, and system stability by optimizing the spatiotemporal distribution of tourist flows, thereby providing simulation-based quantitative insights for peak-period management in large scenic areas. Full article

The integration of automated learning and video analysis enables the development of intelligent systems that can operate effectively in uncertain scenarios. These systems can autonomously identify dominant motion dynamics, depending on the theoretical framework used for representation and the learning process used for pattern identification. Current literature offers a state-based approach to describe the key temporal and spatial relationships required to understand motion dynamics. An important aspect of this approach is determining when the number of positively learned rules from a given information source is sufficient to detect dominant motion in automatic surveillance scenarios. This is crucial, as it affects both the variability of movements that monitored subjects can exhibit within the camera’s field of view and the resources needed for effective implementation. This study addresses these gaps through a grammar-based sufficiency criterion, which posits that learning is complete when production rule growth stabilizes, under the assumption of system stationarity. The stability criterion evaluates whether the most probable rules are learned over time, and whenever a high-growth rule is added, it is used to update the criterion. We outline several benefits of having a formal criterion for determining when a symbolic surveillance system has a robust model that explains the observed motion dynamics. Our hypothesis is that a correct model can consistently account for the majority of motion dynamics over time in an automated learning process. The proposed approach is evaluated by modeling motion dynamics in several scenarios using the SEQUITUR algorithm as input and computing the probability of stability along the learning curve, which indicates when the model reaches a steady state of consistent learning. Experimental validation was conducted in real-world scenarios under varying acquisition conditions. The results show that the proposed method achieves robust modeling performance, with accuracy values ranging from 83.56% to 95.92% in dynamic environments. Full article

This study systematizes the form generation process using machine learning-driven motion-tracking data and investigates the interrelationships between the characteristics of generated data and forms generated according to data-processing methods. Through the vision-based machine learning motion estimation (VideoPose3D) algorithm, 3D motion data are extracted from 2D video and categorized into point (joint), curve (bone), and boundary (range of motion) types. Furthermore, this study analyzes the form generation characteristics and limitations associated with each type of motion-tracking data derived from dynamic-to-dynamic physical activities with postural transitions. A data-processing methodology based on artistic practice from prior research is applied. The characteristics of generated data and the morphological characteristics of generated forms are then analyzed according to non-processed and processed methods. Results suggest a potential correlative tendency between the characteristics and generated forms of each type of motion data value information. A bidirectional complementary relationship exists between non-processed and processed motion-tracking data. The data-based form generation methodology demonstrates potential applicability in architectural design. This study expands design possibilities by supporting decisions early in the architectural design process and immediately generating diverse alternatives; it also proposes a standardized framework for a universal data-centric design process applicable to diverse data types, including motion data. Full article

The growing number of on-orbit spacecraft and the increasing volume of telemetry data have made intelligent anomaly detection in multi-channel telemetry essential for mission operations. Current spacecraft anomaly detection methods primarily rely on statistical models or time-series deep learning approaches, which often fail to explicitly model spatiotemporal dependencies across multiple telemetry channels. This shortcoming limits their ability to capture the dynamically evolving and intricately coupled relationships between variables. To overcome this limitation, a Progressive Spatiotemporal Graph (PSTG) model is proposed for anomaly detection in multi-channel spacecraft telemetry. PSTG employs a multi-scale patch embedding module to extract hierarchical semantic features from multi-channel time series, effectively reducing the dimensionality of the spatiotemporal graph. It constructs a sparse adjacency matrix using a multi-head attention mechanism that integrates intra-channel temporal dynamics, inter-channel spatial correlations, and cross-channel spatiotemporal interactions. An improved multi-head graph attention network then captures pairwise dependencies among nodes within the adjacency matrix. As a result, PSTG encodes rich spatiotemporal representations derived from intricate variable interactions, enabling accurate, real-time prediction of multi-channel telemetry. Furthermore, a dynamic thresholding mechanism is incorporated into PSTG to perform online anomaly detection based on prediction residuals. Extensive experiments on real-world spacecraft telemetry data collected over 84 months show that PSTG outperforms eleven state-of-the-art benchmark methods in almost all cases across multiple evaluation metrics. Finally, visualizations of the learned adjacency and attention matrices are presented to interpret the spatiotemporal modeling process, providing operators with actionable insights into the detected anomalies and facilitating root cause analysis. Full article

Reliable sensor fault detection is critical for the safe and efficient operation of complex industrial systems, such as thermal power plants. However, traditional data-driven methods and standard deep learning models often struggle to detect incipient gradual drift faults under severe environmental noise, primarily because they ignore the inherent physical correlations among multivariate sensor signals. To address this challenge, this paper proposes a novel Physics-Coupled Deep Long Short-Term Memory Autoencoder (PC-Deep-LSTM-AE). Specifically, we integrate a deep LSTM architecture with an explicit non-linear information compression bottleneck and layer normalization to enhance robust feature extraction in high-noise environments. Furthermore, we innovatively introduce a Physics-Coupling Loss (PCC Loss) that jointly optimizes the mean squared reconstruction error and the Pearson correlation coefficient, forcing the model to strictly preserve the dynamic physical relationships among multivariable signals. Extensive experiments were conducted on a real-world thermal power plant dataset with severe noise injection. The results demonstrate that the proposed PC-Deep-LSTM-AE achieves an outstanding F1-score of over 0.98, significantly outperforming mainstream baseline models, including Vanilla LSTM-AE, GRU-AE, Bi-LSTM-AE, and CNN-AE. The proposed method exhibits exceptional robustness and high interpretability for root-cause analysis, highlighting its immense potential for real-world industrial deployment. Full article