Search Results (6,107)

Artificial neural networks (ANNs) are widely used to approximate nonlinear mappings, yet their ability to capture thermodynamic behaviour in dynamic physical systems remains insufficiently characterised. This study investigates how representational capacity influences surrogate modelling accuracy for a crank-angle-resolved internal combustion engine (ICE) simulation with a maximum dynamic state dimension of six. Two feedforward ANN configurations are evaluated: a low-capacity 5–5 architecture containing 84 trainable parameters and a high-capacity 25–25–25 architecture containing 1554 parameters (18.5× larger). Both networks approximate the nonlinear mapping from five embedded operating parameters to four peak thermodynamic outputs (maximum pressure, pressure phasing, maximum temperature, and temperature phasing). Evaluation across 53,178 operating points demonstrates that the high-capacity configuration reduces root mean squared error by factors of 30–50× relative to the low-capacity network, decreasing peak temperature error from 17.68 K to 0.36 K and peak pressure error from 0.116 MPa to 0.0025 MPa. Although both models achieve coefficients of determination exceeding 0.99, the low-capacity network exhibits heavy-tailed residual distributions and regime-dependent error amplification, whereas the high-capacity model reduces both central dispersion and extreme-case error. These results demonstrate that high correlation alone does not guarantee engineering reliability in nonlinear thermodynamic systems. Distribution-level analysis, including percentile and extreme-case characterisation, is required to evaluate engineering robustness. The findings provide a quantitative framework linking ANN capacity, nonlinear dynamic system representation, and predictive robustness. Full article

This study investigates the utilization of Abies alba exudate resin for the development of hybrid resins intended as matrices for composite materials. The novelty of this work lies in demonstrating that physically hybridized, bio-derived resin systems based on Abies alba exudate can exhibit distinct mechanical and dynamic behaviors solely by adjusting the solvent-assisted formulation route, without intentional chemical modification and without spectroscopic evidence of co-network formation within the limits of ATR-FTIR analysis, although limited interfacial interactions cannot be excluded. Two formulation routes were explored: (i) dilution of Abies alba exudate in turpentine derived from pine buds, (ii) dilution in ethanol (96%). The diluted resins were subsequently blended with a commercial epoxy system, which was cured with its amine hardener to form solid matrices in which the Abies alba component was physically incorporated. The resulting hybrid resins were characterized by multiple testing methods and further applied in the fabrication of cotton fiber-reinforced composites. The turpentine-based hybrid resin (HR1) showed a rigid mechanical response, with tensile strengths of approximately 13.2–13.5 MPa, compressive strengths of about 30 MPa, Shore D hardness values of 56–58.5, and a low damping ratio (≈0.026). In contrast, the ethanol-based hybrid resin (HR2) exhibited a highly deformable mechanical response, characterized by low tensile strength (≈0.5 MPa), very high elastic recovery, low hardness (<10 Shore D), and a significantly higher damping ratio (≈0.139). To demonstrate their applicability in composite manufacturing, the HR1 matrix was reinforced with cotton fabric, leading to a substantial improvement in tensile strength (25–26 MPa) and flexural strength (35–36 MPa), together with an increased natural frequency. Water absorption tests revealed limited moisture uptake for the neat hybrid resins (≤0.04 g), while the cotton-reinforced composite exhibited higher but largely reversible water absorption (≈21.5%), associated with the hydrophilic nature of the reinforcement. Full article

The paper suggests a model-dependent theoretical framework for designing optimal ranking algorithms to achieve desirable macroscopic opinion configurations. We consider an opinion formation process in which agents communicate through stochastic pairwise interactions, with the outcomes of these interactions being a function of the interacting agents’ opinions and individual attributes (types). For the model, we write a mean-field approximation (MFA)—a coarse-grained nonlinear ordinary differential equation—which accommodates network modularity and assortativity, agents’ activity heterogeneity, and the curation of a ranking system that can prohibit interactions with opinion- and type-dependent probabilities. Upon MFA, we formulate a control problem for dynamically adjusting the ranking algorithm’s parameters. The existence of a solution is proved, and certain properties of optimal controllers are derived. For the case of a two-element opinion alphabet, we obtain a solution to the control problem using finite-difference schemes. This solution holds for any number of agent types and does not depend on external factors, such as the influence of social bots. Numerical tests corroborate our findings and also enable us to investigate the control problem for high-dimension opinion spaces, wherein we consider two primary scenarios: depolarization of an initially polarized society and nudging a social system towards a fixed endpoint of an opinion spectrum. Full article

Accurate delineation of stream networks in low-gradient wetlands remains challenging due to subtle topographic variation and dense vegetation cover. This study systematically evaluated 48 Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) processing workflows through 1128 pairwise comparisons to identify optimal approaches for mapping fine-scale channels in Japan’s Kushiro Wetland, a Ramsar-designated ecosystem. The workflows combined three ground filtering methods (Progressive Morphological Filter, Cloth Simulation Filter, Multiscale Curvature Classification), four interpolation techniques (Inverse Distance Weighting, Triangulated Irregular Network, Kriging, Multilevel B-spline Approximation), two sink-filling algorithms (Planchon & Darboux; Wang & Liu), and two flow direction models (D8, D-infinity). Performance was first assessed using pixel-based Intersection over Union (IoU) metrics to quantify inter-method consensus. Independent plausibility-based validation was then conducted using near-contemporaneous Sentinel-2 imagery. Although pairwise statistical analysis identified workflows that achieved high inter-method consensus (median IoU = 0.90), external validation demonstrated that the CSF-MBA-Planchon-D8 workflow provided the most realistic presentation of optically observable channel corridors (validation IoU ≈ 0.85). These findings reveal that high inter-method agreement does not necessarily imply accurate landscape representation; multiple workflows may converge on systematically biased solutions. Ground filtering exerted the strongest influence on pairwise consensus, whereas plausibility-based validation highlighted the importance of selecting workflow combinations that preserve subtle channel morphology. Sink-filling and flow direction choices exerted comparatively minor effects in this low-gradient setting. The proposed dual-validation framework provides methodological guidance for wetland restoration planning and highlights the necessity of external validation in LiDAR-derived hydrological feature extraction. Full article

Parameter estimation for biochemical reaction networks is computationally demanding, especially for systems with oscillatory nonlinear dynamics, where standard iterative optimization strategies, including genetic algorithms, often struggle with prohibitive computational costs. We introduce an efficient parameter estimation framework that combines a real-coded genetic algorithm with a novel adaptive simulation termination strategy. This strategy defines a time-dependent termination boundary based on population quantiles, which is permissive during early transients and becomes progressively stricter as simulations advance, explicitly accounting for the temporal structure of oscillatory behavior. Crucially, this mechanism facilitates the efficient identification and early simulation termination of poor parameter candidates, thus avoiding the computational expense of full-horizon simulations. The framework further integrates global exploration with the modified Powell method for rapid local refinement. Numerical experiments on two benchmark oscillatory models—the Lotka–Volterra and Goodwin oscillators—demonstrate that the framework reduces computational cost by approximately 30–50% compared to a baseline GA without this strategy. For the parameter-sensitive Goodwin model, the framework efficiently identifies candidates evolving toward damped oscillations caused by subtle parameter variations. Sensitivity analysis also confirms robustness across diverse hyperparameter settings, indicating that adaptive simulation termination provides a practical acceleration mechanism for inverse problems in systems biology where iterative objective function evaluation dominates runtime. Full article

The aerodynamics of avian flight provides critical inspiration for the design of bioinspired aerial vehicles, yet the quantitative characterization of free-flight wing kinematics remains challenging. This study employs a neural-network-based motion tracking approach (DeepLabCut) to analyze wingbeat kinematics in free-flying birds from video data. We automatically digitize key wing points and reconstruct three-dimensional trajectories to quantify asymmetric flapping patterns. Our analysis reveals that while wing oscillations approximate sinusoidal motion, they exhibit statistically significant velocity differences between upstroke and downstroke phases, confirming the stroke asymmetry of avian flapping. Furthermore, using video of a flying frigatebird (Fregata ariel), we quantify the changes in the effective wing area throughout the wingbeat cycle, showing a ~19% variation that significantly impacts lift generation efficiency. These findings provide quantitative benchmarks for avian-inspired wing design and offer insights for optimizing flapping kinematics in bioinspired aerial systems, particularly for enhancing takeoff and landing capabilities in micro air vehicles. Full article

Granular materials exhibit complex mechanical behaviors during unloading, yet the underlying micro- and meso-scale mechanisms remain unclear. This study employs a discrete element method to simulate a series of triaxial tests on sand and pebble specimens with varying initial densities under different unloading stress paths. While dense specimens demonstrate strain softening and dilatancy, loose samples exhibit shear contraction. To quantify the underlying fabric evolution, persistent homology (PH) theory is adopted to analyze the particle contact force networks. The results reveal that the average strength of this network correlates strongly with the macroscopic stress–strain response. For dense samples, network strength rapidly increases to a peak coinciding with the deviatoric stress maximum, then gradually decreases with further shear. Crucially, this evolution is path-dependent: the average contact force network strength increases approximately 20% more during unloading in the minor principal stress direction compared to unloading in the major principal stress direction. This quantitative analysis of force chain degradation provides a mechanistic explanation for the observed strain softening, highlighting the dominant role of the unloading stress path. In contrast, loose specimens, which initially lack an obvious force chain network, show negligible microstructural evolution during unloading. Full article

Lost circulation remains a persistent and costly challenge in drilling operations for oil, gas, and geothermal energy systems, particularly when wide fractures and cavernous formations are encountered. Although a wide range of lost circulation materials (LCMs) is commercially available, multiple laboratory studies report that many conventional products are unable to effectively seal fractures of approximately 5 mm width under controlled conditions. In contrast, recent investigations of shape memory polymer (SMP)-based LCMs have demonstrated successful sealing of fractures up to approximately 12 mm in width. This review examines recent advances in SMP-based LCMs as an emerging class of smart materials capable of overcoming geometric and operational constraints associated with drilling equipment, particularly bottom-hole assembly (BHA) components. Through thermomechanical programming, these materials are transformed into compact temporary shapes suitable for seamless circulation and are subsequently triggered by reservoir temperatures to recover permanent geometries up to an order of magnitude larger. Upon activation, these discrete elements function collectively as a hierarchical, jammed system. The resulting multiscale networks—comprising ladder-shaped elements, interwoven fibers, and granular particles—bridge large apertures, enhance mechanical interlocking, and achieve superior hydraulic isolation. Full article

This study presents AgroNova—a hybrid IoT architecture for autonomous monitoring and management of microclimate in greenhouse environments. The system combines a capillary wireless sensor network, gateway-level rule-based agents, a server agent, cloud services and an advisory component based on a large language model (LLM) that supports local decision-making by incorporating external contextual information from meteorological services. The proposed architecture was validated through a seven-month deployment in an unheated tomato greenhouse, during which more than 380,000 environmental measurements were collected from five sensor nodes. The system operated continuously under real agricultural conditions, including during temporary internet connectivity interruptions, due to the autonomous gateway-level control and deterministic fallback mechanisms. The analysis of the collected data includes 3110 environmental threshold exceedance events, in which recovery dynamics, reaction latency, and actuator activation frequency were evaluated. The results show that the architecture supports stable autonomous operation under limited actuation conditions, with an average local reaction latency of less than 1 s, while physical actuator operations occur in approximately 2.3% of all control decisions. This behavior reflects a conservative control strategy that limits unnecessary mechanical operations and contributes to stable system operation. The experimental integration of a consultative LLM module within the server-side agent demonstrates the potential for context-enriched decision support using external meteorological data, while final control decisions remain under the authority of the gateway-based deterministic control mechanism. The main contribution of this study is the demonstration of a hybrid IoT architecture that combines edge-level autonomy with context-assisted reasoning, validated through deployment in a real greenhouse environment. Full article

Populus euphratica is a critical constructive species in arid desert regions, serving as a “natural barrier” for oasis protection. The sustainable management of Populus euphratica forests is directly related to regional ecological security, and the fine identification of sparse Populus euphratica forests is essential for the conservation of natural Populus euphratica forests. Currently, most mapping studies on Populus euphratica distribution focus on the extraction of dense, contiguous Populus euphratica forests, with insufficient attention paid to the identification of sparse Populus euphratica forests. This study utilizes Gaofen-2 (GF-2) satellite imagery as the data source and takes a typical sparse Populus euphratica forests distribution area in the Tarim River Basin as the study site. It systematically evaluates the performance of nine mainstream deep learning models, including U-Net, DeepLabV3+, and SegFormer, in the task of sparse Populus euphratica forests identification. The results indicate that: (1) The false-color sample set, synthesized from near-infrared, red, and green bands, contributes to improved model accuracy. Compared to the true-color (red, green, blue bands) dataset, the average Intersection over Union (IoU) of the nine models shows a relative improvement of approximately 20%. (2) For the sparse Populus euphratica forests identification task based on the false-color dataset, four models—U-Net, U-Net++, MA-Net, and DeepLabV3+—exhibited excellent performance, with IoU exceeding 75%. (3) Using U-Net as the baseline model, this study integrated the max-pooling indices mechanism, atrous spatial pyramid pooling, and residual connection modules to construct a semantic segmentation network tailored for sparse Populus euphratica forests, named Sparse Populus euphratica Segmentation Network (SPS-Net). This model achieved an IoU of 80%, a relative improvement of approximately 6.3% over the baseline model, and demonstrated good stability in large-scale classification tests. The identification scheme for sparse Populus euphratica forests constructed using GF-2 imagery and deep learning models proposed in this study can provide effective technical support for the refined monitoring and protection of natural Populus euphratica forests. Full article

This study presents a machine learning framework for the reconstruction of fatigue life and fracture toughness in natural fiber-reinforced composites, evaluating the predictive accuracy of six regression algorithms—Random Forest, Gradient Boosting, Support Vector Machine, Neural Network, Ridge Regression, and Lasso Regression—using a controlled synthetic dataset of 600 samples generated from established Basquin fatigue and Rule of Mixtures fracture equations, incorporating stochastic noise calibrated to experimental scatter (CV = 15–50%), with log-normal noise standard deviation of 0.20 for fatigue life and Gaussian noise standard deviation of 0.15 for fracture toughness. The dataset encompasses eight natural fiber types (flax, jute, sisal, hemp, bamboo, coconut, banana, and pineapple) and five matrix systems (epoxy, polyester, PLA, vinyl ester, and polyurethane). Models were evaluated using a 70-15-15 train–validation–test split with 5-fold cross-validation and exhaustive grid search hyperparameter optimisation. Gradient Boosting achieved R² = 0.93 for fatigue life and Stacking Ensemble achieved R² = 0.87 for fracture toughness, representing 97% and 89% of their respective noise-ceiling values (theoretical maximum R² of 0.96 and 0.98 given the programmed noise levels). The ML models perform supervised function approximation—learning to reconstruct the programmed generation equations rather than discovering novel physical composite behaviour—and function as automated surrogates for the governing equations. Feature importance analysis identified engineered composite indicators, stress amplitude, and fiber length as the most influential parameters. The framework provides a reproducible ML evaluation pipeline as a methodological template for future experimental composite studies. Full article

Snow accumulation and ice formation can significantly reduce pavement friction, posing a serious threat to traffic safety during winter. Traditional snow-removal methods, including mechanical removal, chemical de-icing agents, and heated pavement systems, suffer from several limitations such as low efficiency, environmental impacts, and high operational costs. Electrically conductive asphalt concrete (ECAC) has therefore emerged as a promising active snow-melting technology. When an electric current passes through the conductive network formed within the asphalt mixture, heat is generated through the Joule heating effect. After incorporating conductive fillers, the electrical resistivity of ECAC mixtures can be reduced from approximately 10⁶–10⁸ Ω·cm for conventional asphalt mixtures to about 10⁻¹–10² Ω·cm. Under an applied voltage typically ranging from 30 to 60 V, ECAC pavements can increase the surface temperature by 10–30 °C within 10–30 min, thereby enabling rapid snow melting and ice removal. Meanwhile, an optimized conductive network can maintain sufficient mechanical performance, with dynamic stability generally exceeding 3000 cycles/mm. When the conductive filler content is reasonably controlled, only a limited reduction in fatigue resistance is observed. This paper presents a comprehensive review of electrically conductive asphalt concrete technologies for snow-melting pavements. The background, underlying mechanisms, material development, system configuration, and field applications of ECAC are systematically summarized. Finally, the current challenges are discussed, including the stability of conductive networks, the trade-off between electrical conductivity and pavement performance, and electrical safety. Future research directions focusing on material optimization, intelligent power control, and long-term field performance evaluation are proposed to support the practical application of ECAC pavements in sustainable winter road maintenance. Full article

Unmanned Aerial Vehicles (UAVs) are gradually emerging as key platforms for Integrated Sensing, Communication, and Computation (ISCC) systems in next-generation wireless networks. However, strict resource constraints and task coupling make static allocation inefficient in dynamic environments. This paper studies a UAV-driven ISCC system in which a single UAV dynamically allocates communication bandwidth, sensing resources, and computing power. Considering that sensing data in mission-critical applications is highly time-sensitive, minimizing the response time is paramount. To reduce system latency while maintaining sensing quality and energy efficiency, we propose D3PG-Light, a deployment oriented and stability-enhanced refinement of the deep reinforcement learning framework, specifically tailored for real-time resource scheduling under UAV hardware constraints. D3PG-Light incorporates an adaptive gradient stabilization mechanism, Long Short-Term Memory (LSTM), and feature fusion to enhance training stability. Simulation results based on real air–ground channel measurements show that D3PG-Light converges faster and achieves more stable learning behavior than DDPG, TD3, and the original D3PG. In particular, the proposed method reduces the 95th-percentile latency from over 100 ms to approximately 24 ms, achieves higher converged reward values, and requires fewer than 50 k model parameters. These results demonstrate the effectiveness of D3PG-Light for latency-sensitive UAV-ISCC applications. Full article

The effects of red seaweed polysaccharides, e.g., carrageenan, agar gum, Porphyra haitanensis polysaccharide (PHP), and Bangia fusco-purpurea polysaccharide (BFP), on the physicochemical properties and in vitro antioxidants of silver carp surimi gels were studied. Adding appropriate concentrations of carrageenan and agar gum increased hydrophobic interactions, resulting in a denser and more uniform gel network as observed by SEM, and shortened the relaxation time of the fish balls, thus improving the gel strength and hardness of the products. When adding 0.75% carrageenan and 0.50% agar gum, the gel strength of the fish balls reached its maximum value, increasing by approximately 28.84% and 12.08%, respectively, compared to the control group (p < 0.05). However, with the over-addition of PHP and BFP, the cross-linking of surimi proteins was inhibited, resulting in a decrease in gel strength and hardness. In addition, red seaweed polysaccharide improved the free radical scavenging activity of fish balls, especially fish balls with 0.50% and 1.00% PHP and BFP exhibited better free radical scavenging activity after digestion. These findings offer insights and actionable strategies for enhancing the gel properties and function of surimi products with seaweed polysaccharides. Full article

Low Earth Orbit (LEO) satellite signals of opportunity (SOP) provide a viable positioning alternative in GNSS (Global Navigation Satellite System)-denied environments, yet their accuracy is fundamentally constrained by the low-quality orbital data typically available, such as SGP4 (Simplified General Perturbations model 4) predictions derived from Two-Line Elements (TLEs). To address this limitation, this paper proposes a dynamics-informed non-causal deep learning framework that enhances low-quality orbital data into high-fidelity trajectories for accurate SOP positioning. The proposed Non-Causal Dynamics-Informed Representation Temporal Convolutional Network (Non-Causal DIR-TCN) integrates phase space reconstruction and a Temporal Convolutional Network to explicitly model the chaotic dynamics inherent in LEO orbits, while relaxing the causality constraints of standard temporal convolutions to utilize both past and future context from the available SGP4 stream. Experimental results demonstrate that the framework significantly reduces orbit estimation errors and accelerates model convergence. When applied to LEO-SOP positioning, it achieves approximately 20% improvement in 2D positioning accuracy compared to conventional SGP4-based methods. This work effectively bridges the gap between accessible low-precision orbital data and high-accuracy state estimation, advancing the practical deployment of opportunistic signals for resilient positioning in challenging environments. Full article