Search Results (252)

We develop a symmetry-based variational theory that shows the coarse-grained balance of work inflow to heat outflow in a driven, dissipative system relaxed to the golden ratio. Two order-2 Möbius transformations—a self-dual flip and a self-similar shift—generate a discrete non-abelian subgroup of $\mathrm{PGL}(2,\mathbb{Q}\left(\sqrt{5}\right))$. Requiring any smooth, strictly convex Lyapunov functional to be invariant under both maps enforces a single non-equilibrium fixed point: the golden mean. We confirm this result by (i) a gradient-flow partial-differential equation, (ii) a birth–death Markov chain whose continuum limit is Fokker–Planck, (iii) a Martin–Siggia–Rose field theory, and (iv) exact Ward identities that protect the fixed point against noise. Microscopic kinetics merely set the approach rate; three parameter-free invariants emerge: a $62\%:38\%$ split between entropy production and useful power, an RG-invariant diffusion coefficient linking relaxation time and correlation length ${\mathcal{D}}_{\alpha }={\xi }^z/\tau$, and a $\vartheta ={45}^{\circ }$ eigen-angle that maps to the golden logarithmic spiral. The same dual symmetry underlies scaling laws in rotating turbulence, plant phyllotaxis, cortical avalanches, quantum critical metals, and even de-Sitter cosmology, providing a falsifiable, unifying principle for pattern formation far from equilibrium. Full article

Transformer-based models have demonstrated outstanding performance in trajectory prediction; however, their complex architecture demands substantial computing power, and their performance degrades significantly in long-term prediction. A transformer model was developed to predict vehicle trajectory in urban low-speed T-intersections. Microscopic traffic simulation data were generated to train the trajectory-prediction model; furthermore, validation data focusing on atypical scenarios were also produced. The appropriate loss function to improve prediction accuracy was explored, and the optimal input/output sequence length for efficient data management was examined. Various driving-characteristics data were employed to evaluate the model’s generalization performance. Consequently, the smooth L1 loss function showed outstanding performance. The optimal length for the input and output sequences was found to be 1 and 3 s, respectively, for trajectory prediction. Additionally, improving the model structure—rather than diversifying the training data—is necessary to enhance generalization performance in atypical driving situations. Finally, this study confirmed that the additional features such as vehicle position and speed variation extracted from the original trajectory data decreased the model accuracy by about 21%. These findings contribute to the development of applicable lightweight models in edge computing infrastructure to be installed at intersections, as well as the development of a trajectory prediction and accident analysis system for various scenarios. Full article

Accurate landslide time prediction holds critical significance for ensuring safety and efficient production in open-pit mining operations. While the inverse velocity method serves as a prevalent data-driven forecasting approach, conventional single-point monitoring implementations frequently yield substantial deviations. This study proposes a multi-point collaborative inverse velocity landslide time prediction methodology using nonlinear least squares, which is based on slope radar multi-point group displacement monitoring data. Systematic stability evaluations were conducted for both single-point predictions and multi-point ensemble forecasts. Experimental results demonstrate that single-point-based predictions generally confine errors within 5 h, including the case of traditional smoothing treatments of velocity curves. The developed multi-point collaborative methodology achieves prediction errors below 1 h, with temporal forecast position variations and spatial point quantity adjustments inducing marginal error fluctuations under 2 h based on strict data exclusion. Enhanced data volume implementation significantly improves prediction accuracy and stability. These findings will provide substantive technical references and methodological guidance for advancing landslide temporal prediction research in open-pit mining engineering. Full article

Reclaiming coastal wetlands for agricultural purposes has led to intensified farming activities, which are anticipated to affect greenhouse gas (GHG) flux processes within coastal wetland ecosystems. However, how greenhouse gas exchanges respond to variations in agricultural reclamation activities across different years remains uncertain. To address this knowledge gap, this study characterized dynamic exchanges within the soil–plant–atmosphere continuum by employing continuous monitoring across four representative coastal wetland soil–vegetation systems in Jiangsu, China. The results show the carbon dioxide (CO₂) and nitrous oxide (N₂O) flux exchanges between the system and the atmosphere and soil–vegetation carbon pools, which revealed the drivers of carbon dynamics in the coastal wetland system. The four study sites, converted from coastal wetlands to agricultural lands at different times (years), generally act as CO₂ sinks and N₂O sources. Higher levels of CO₂ sequestration occur as the age of reclamation rises. In terms of time scale, crops lands were found to be CO₂ sinks during the growing period but became CO₂ sources during the crop fallow period. Although the temporal trend of the N₂O flux was generally smooth, reclaimed farmlands acted as net sources of N₂O, particularly during the crop-growing period. The RDA and PLS-PM models illustrate that soil salinity, acidity, and hydrothermal conditions were the key drivers affecting the magnitude of the GHG flux exchanges under reclamation. This study demonstrates that GHG emissions from reclaimed wetlands can be effectively regulated through science-based land management, calling for prioritized attention to post-development practices rather than blanket restrictions on coastal exploitation. Full article

Recent developments in deep learning have underscored prizing effective feature extraction in scenarios with limited or unlabeled data. This study introduces a novel unsupervised multi-scale feature extraction framework based on a multi-branch auto-encoder architecture. The proposed method decomposes input images into smooth, detailed and residual components, using variational loss functions to ensure that each branch captures distinct and non-overlapping representations. This decomposition enhances the information richness of input data while preserving its structural integrity, making it especially beneficial for grayscale or low-resolution images. Experimental results on classification and image segmentation tasks show that the proposed method enhances model performance by enriching input representations. Its architecture is scalable and adaptable, making it applicable to a wide range of machine learning tasks beyond image classification and segmentation. These findings highlight the proposed method’s utility as a robust, general-purpose solution for unsupervised feature extraction and multi-scale representation learning. Full article

The global tourism industry is currently experiencing a significant boom, leading to increasing prosperity in the tourism economy. However, litigation disputes and conflicts between tourism consumers and operators have become more frequent, severely disrupting the smooth functioning of tourism markets. Therefore, clarifying the spatiotemporal attributes and distributional characteristics of tourism disputes in destinations holds substantial significance for destination market governance and the sustainable development of tourism systems. Taking China’s Yangtze River Economic Belt as a case study, this research employs the geographic concentration index, the gravity center model, and the Dagum Gini coefficient to analyze the spatiotemporal patterns of different types of tourism disputes and their watershed-specific variations from 2013 to 2024. The results demonstrated that tourism disputes exhibited an increase–decrease–increase inter-annual trend. The downstream basin had the most disputes, followed by the upstream and midstream ones. Areas with a high and low incidence of disputes were interspersed, with low spatial agglomeration. The gravity center was in Hubei Province. Basin differences changed in a fluctuating manner. Basin differences were large at the beginning of the study period, and thereafter the basin differences decreased in a fluctuating manner. The inter-basin differences were more significant for travel agency disputes and catering disputes. Overall, this study effectively presented the temporal distribution characteristics, spatial evolution characteristics, and basin differences in tourism disputes using mathematical statistics, geospatial analysis, and other methods. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

This study investigates the organized flow structures and turbulence quantities in a transitional oscillatory boundary-layer flow over a smooth bed using a DNS model set up by the open-source framework Nektar++ (v5.2.0). The present model was validated against the results of a previous study involving a bypass transition mechanism in the intermittently turbulent regime. To trigger the initial perturbations, a roughness element was placed on the bed and removed at the very moment a two-dimensional vortex tube, caused by an inflectional-point shear-layer instability, was observed on it. Then, the turbulent spots where the flow experienced intense fluctuations in an otherwise laminar boundary layer were identified from the bed shear-stress distribution on the bed, which served as a reliable indicator of turbulence. These flow features emerged as the first sign of the initiation of turbulence. Several measurement points were selected to follow the bed shear-stress variations and to observe the spatial and temporal development of turbulent spots at a low-wave Reynolds number, $Re=1.8\times {10}^5$. Along with these observations, phase-resolved turbulence quantities were also investigated over successive half-cycles for the first time in the literature to understand how turbulence develops and spreads over the flow domain. The results show that the turbulence generated in the near-bed region becomes stronger in the deceleration stage due to the adverse pressure gradient and diffuses away from the bed during the subsequent phases of the developing oscillatory boundary-layer flow. The findings related to the turbulence quantities also indicate that the turbulence gradually evolves and spreads into the fluid domain in successive half-cycles. Full article

The main objective of this work was to model the failure mechanisms of brittle materials subjected to thermal and mechanical loads. A diffusive representation of the crack topology provides the basis for the regularized kinematic framework used. With a smooth transition from the undamaged to the fully damaged state, the fracture surface was roughly represented as a diffusive field. By integrating a staggered scheme and spectral decomposition, the variational formulation was used after being mathematically written and developed. Its effectiveness was analyzed using extensive benchmark tests, demonstrating the effectiveness of the phase-field model in modeling the behavior of brittle materials. This proposed approach was experimentally tested through the examination of crack propagation paths in brittle materials that were subjected to variable mechanical and thermal loads. This work focused on the integration of a spectral decomposition-based phase-field model with thermo-mechanical coupling for dynamic fracture, supported by benchmark validation and the comparative assessment of energy decomposition strategies. The results highlight the accuracy and robustness of numerical and experimental methodologies proposed to model fracture mechanics in brittle materials subjected to complex loading conditions. Full article

With the growing demand for personalized and flexible production, human–robot collaboration technology receives increasing attention. However, enabling robots to accurately perceive and align with human motion intentions remains a significant challenge. To address this, a novel human–robot collaborative control framework is proposed, which utilizes electromyography (EMG) signals as an interaction interface and integrates human skill imitation with reinforcement learning. Specifically, to manage the dynamic variation in muscle coordination patterns induced by joint angle changes, a temporal graph neural network enhanced with an Angle-Guided Attention mechanism is developed. This method adaptively models the topological relationships among muscle groups, enabling high-precision three-dimensional dynamic arm force estimation. Furthermore, an expert reward function and a fuzzy experience replay mechanism are introduced in the reinforcement learning model to guide the human skill learning process, thereby enhancing collaborative comfort and smoothness. The proposed approach is validated through a collaborative assembly task. Experimental results show that the proposed arm force estimation model reduces estimation errors by 10.38%, 8.33%, and 11.20% across three spatial directions compared to a conventional Deep Long Short-Term Memory (Deep-LSTM). Moreover, it significantly outperforms state-of-the-art methods, including traditional imitation learning and adaptive admittance control, in terms of collaborative comfort, smoothness, and assembly accuracy. Full article

This study aims to clarify how porosity and frequency interact to influence permeability and flow behavior in porous media subjected to sinusoidal pressure variations. Specifically, we investigate oscillatory flow at 1 Hz and 100 Hz under varying porosity conditions using a pore-scale Computational Fluid Dynamics (CFD) model. The model is validated against the Johnson–Koplik–Dashen (JKD) model to ensure accuracy in capturing dynamic permeability. At 1 Hz, where the oscillation period greatly exceeds the system’s time constant $\tau$, the flow reaches a quasi-steady state with dynamic permeability approximating static permeability. Increasing porosity enhances Darcy velocity, with minimal phase difference between velocity and pressure. At 100 Hz, flow behavior depends on the ratio of the oscillation period $T$ to $\tau$. For high porosity ($\phi =0.840$, $T\approx \tau$), the flow does not fully develop before the pressure gradient reverses, leading to significant phase lag. For low porosity ($\phi =0.370$, $T\approx 12\tau$), the phase lag is smaller but remains non-zero due to the smooth temporal variation in pressure. This work contributes to the understanding of porous flow dynamics by revealing how porosity modulates both the amplitude and phase angle of dynamic permeability in frequency-dependent porous flows, providing a framework for predicting phase lag in frequency-sensitive applications. Full article

Background: Developing versatile dressings that offer wound protection, maintain a moist environment, and facilitate healing represents an important therapeutic approach for burn patients. Objectives: This study presents the development of new smart pH-sensitive collagen-hydroxyethylcellulose films, incorporating naproxen and phenol red, designed to provide controlled drug release while enabling real-time pH monitoring for burn care. Methods: Biopolymeric films were prepared by the solvent-casting method using ethanol and glycerol as plasticizers. Results: Orange-colored films were thin, flexible, and easily peelable, with uniform, smooth, and nonporous morphology. Tensile strength varied from 0.61 N/mm² to 3.33 N/mm², indicating improved mechanical properties with increasing collagen content, while wetting analysis indicated a hydrophilic surface with contact angle values between 17.61° and 75.51°. Maximum swelling occurred at pH 7.4, ranging from 5.65 g/g to 9.20 g/g and pH 8.5, with values from 4.74 g/g to 7.92 g/g, suggesting effective exudate absorption. In vitro degradation proved structural stability maintenance for at least one day, with more than 40% weight loss. Films presented a biphasic naproxen release profile with more than 75% of the drug released after 24 h, properly managing inflammation and pain on the first-day post-burn. The pH variation mimicking the stages of the healing process demonstrated the color transition from yellow (pH 5.5) to orange (pH 7.4) and finally to bright fuchsia (pH 8.5), enabling easy visual evaluation of the wound environment. Conclusions: New multifunctional films combine diagnostic and therapeutic functions, providing a promising platform for monitoring wound healing, making them suitable for real-time wound assessment. Full article

The emergence of new-generation hyperspectral satellites offers more potential for mapping soil properties. This study presents the first assessment of EnMAP (Environmental Mapping and Analysis Program) hyperspectral imagery for soil organic matter (SOM) prediction and mapping using actual spectral data from 282 soil samples. Different spectral preprocessing techniques, including Savitzky–Golay (SG) smoothing, the second derivative of SG, and Standard Normal Variate (SNV) transformation, were evaluated in combination with embedded feature selection to identify the most relevant wavelengths for SOM prediction. Partial Least Squares Regression (PLSR) models were developed under different pre-treatment scenarios. The best performance was obtained using SNV preprocessing with the top 30 EnMAP bands (wavelengths) selected, giving R² = 0.68, RMSE = 0.34%, and RPIQ = 1.75. The combination of SNV with feature selection successfully identified significant wavelengths for SOM prediction, particularly around 550 nm in the Vis–NIR region, 1570–1630 nm, and 1600 nm and 2200 nm in the SWIR region. The resulting SOM predictions exhibited spatially consistent patterns that corresponded with known soil–landscape relationships, highlighting the potential of EnMAP hyperspectral data for mapping soil properties despite its limited geographical availability. While these results are promising, this study identified limitations in the ability of PLSR to extrapolate predictions beyond the sampled areas, suggesting the need to explore non-linear modeling approaches. Future research should focus on evaluating EnMAP’s performance using advanced machine learning techniques and comparing it to other available hyperspectral products to establish robust protocols for satellite-based soil monitoring. Full article

Transitioning to clean energy in off-grid remote locations is essential to reducing fossil-fuel-generated greenhouse gas emissions and supporting renewable energy growth. While hybrid renewable energy systems (HRES), including multiple renewable energy (RE) sources and energy storage systems are instrumental, it requires technical reliability with economic efficiency. This study examines the feasibility of an HRES incorporating solar, wind, hydrogen, and biofuel energy at a remote location in Australia. An electric vehicle charging load alongside a residential load is considered to lower transportation-based emissions. Additionally, the input data (load profile and solar data) is validated through statistical analysis, ensuring data reliability. HOMER Pro software is used to assess the techno-economic and environmental performance of the hybrid systems. Results indicate that the optimal HRES comprising of photovoltaic, wind turbines, fuel cell, battery, and biodiesel generators provides a net present cost of AUD 9.46 million and a cost of energy of AUD 0.183, outperforming diesel generator-inclusive systems. Hydrogen energy-based FC offered the major backup supply, indicating the potential role of hydrogen energy in maintaining reliability in off-grid hybrid systems. Sensitivity analysis observes the effect of variations in biodiesel price and electric load on the system performance. Environmentally, the proposed system is highly beneficial, offering zero carbon dioxide and sulfur dioxide emissions, contributing to the global net-zero target. The implications of this research highlight the necessity of a regional clean energy policy facilitating energy planning and implementation, skill development to nurture technology-intensive energy projects, and active community engagement for a smooth energy transition. Potentially, the research outcome advances the understanding of HRES feasibility for remote locations and offers a practical roadmap for sustainable energy solutions. Full article

Biosensors based on the surface plasmon resonance (SPR) technique are widely used for analyte detection due to their high selectivity and real-time detection capabilities. However, conventional SPR spectrum analysis can be affected by experimental noise and environmental variations, reducing the accuracy of results. To address these limitations, this study presents the development of an open-source computational tool to optimize SPR biosensor characterization, implemented using MATLAB App Designer (Version R2024b). The tool enables the importation of experimental data, application of different smoothing methods, and integration of traditional and hybrid approaches to enhance accuracy in determining the resonance angle. The proposed tool offers several innovations, such as integration of both traditional and hybrid (angle vs wavelength) analysis modes, implementation of four advanced curve smoothing techniques, including Gaussian filter, Savitzky–Golay, smoothing splines, and EWMA, as well as a user-friendly graphical interface supporting real-time data visualization, experimental data import, and result export. Unlike conventional approaches, the hybrid framework enables multidimensional optimization of SPR parameters, resulting in greater accuracy and robustness in detecting resonance conditions. Experimental validation demonstrated a marked reduction in spectral noise and improved consistency in resonance angle detection across conditions. The results confirm the effectiveness and practical relevance of the tool, contributing to the advancement of SPR biosensor analysis. Full article