Search Results (1,890)

The chlorophyll molecule is considered a low-cost material, easy to synthesize, and easily extracted from plant leaves. It exhibits high chemical stability, structural flexibility, and high absorbance ability at the visible range of electromagnetic radiation. In this work, the geometrical, electronic, and optical properties of pure, dissolved, and doped chlorophyll (C1) natural organic dye were computed by density functional theory (DFT) and time-dependent density functional theory (TD-DFT). The solvents considered include water (H₂O), acetone (C₂H₆O), dichloromethane (CH₂Cl₂), chloroform (CH₃Cl), and dimethyl-sulfoxide (DMSO) (C₂H₆OS). The solar photovoltaic parameters, such as light-harvesting efficiency (LHE), oscillation strength (f), free energy of electron injection (${\Delta \mathrm{G}}_{\mathrm{I}\mathrm{n}\mathrm{j}.}$) and regeneration (${\Delta \mathrm{G}}_{\mathrm{R}\mathrm{e}\mathrm{g}.}$), open-circuit voltaic (V_OC), and efficiency ($\eta$), were also investigated. The evaluated energy gap slightly shifted from 1.920 eV to 1.980 eV based on the solvent polarity, while the UV-Visible absorption spectrum red-shifted from 422.3 nm to 439.8 nm, improving the overall efficiency up to 21.5% in DMSO solvent. The (LHE) and (${\Delta \mathrm{G}}_{\mathrm{I}\mathrm{n}\mathrm{j}.}$) properties regarding Cl molecules improved up to 69.1% and −1.384 eV when dissolved in chloroform and DMSO solvents, respectively. Doping C1 molecule via metal transition atoms such as zinc (Zn), nickel (Ni) and copper (Cu) further modified the optical and photovoltaic performance. Doped C1 molecule via Cu atom shows the best photonic results, including the highest open-circuit voltage (V_oc) and conversion efficiency ($\mathrm{Ƞ}$), while the Ni-doped C1 dye displays the longest lifetime, 1.699 µs, and the highest electronic coupling constant, 1.975 eV; thus, it has the superior photovoltaic performance. These results demonstrate that both solvents and transition metal atom modification significantly improve C1 performance, making metal-doped C1 a promising low-cost and eco-friendly sensitizer for dye-sensitized solar cells (DSSCs). Full article

The study investigates the catalytic activity of vanadium-containing catalysts in the selective oxidation of 4-methylpyridine (4-MP) in the gas phase. V-Cr, V-Ti, and V-Ti-Cr catalysts were synthesised and studied. The phase composition and structural features of the catalysts were determined by X-ray diffraction (XRD) and Raman spectroscopy, and their thermal stability was investigated using thermogravimetric analysis (TGA/DTA). Textural characteristics were evaluated by low-temperature nitrogen adsorption–desorption (BET, BJH), surface morphology was studied using scanning electron microscopy (SEM), and the distribution of elements was investigated using energy-dispersive X-ray spectroscopy (EDX). The chemical composition of the catalysts was determined using inductively coupled plasma atomic emission spectrometry (ICP-OES) and catalytic activity was evaluated in the selective gas-phase oxidation reaction of 4-methylpyridine in the temperature range 280–380 °C. It was found that an increase in temperature is accompanied by an increase in the conversion of 4-methylpyridine, but at the same time, deep oxidation reactions intensify. The best result is achieved on the V-Ti-Cr catalyst, for which the conversion of 4-MP reaches 86.88% and the selectivity is 73.06% at 320 °C. However, V-Ti provides moderate stable performance, while V-Cr demonstrates relatively low efficiency. Thus, it can be concluded that the nature of the temperature dependence of 4-methylpyridine conversion reflects the different nature of the active centres and their stability. Full article

The long-term prestress relaxation of soil anchors embedded in saturated clay is a critical issue affecting the safety of geotechnical structures such as slopes and foundation pits. Traditional integer-order constitutive models are often unable to accurately describe the nonlinear and time-dependent relaxation behavior observed in such anchorage systems. Based on fractional calculus theory, this study establishes a constitutive model for the coupled stress relaxation behavior of soil anchors and saturated clay. The Riemann–Liouville fractional derivative and the two-parameter Mittag-Leffler function are introduced to represent the material memory effect and continuous relaxation characteristics. To achieve reliable parameter identification, a hybrid optimization strategy combining the Adaptive Hybrid Differential Evolution (AHDE) algorithm and the Levenberg–Marquardt (L-M) method is proposed. The proposed model and identification approach are validated using field monitoring data from soil anchors in a slope engineering project at the Guangxi Friendship Pass Port. The results show that the proposed model can accurately reproduce the entire stress relaxation process, with a coefficient of determination of R² = 0.9517. Parameter sensitivity analysis further clarifies the influence of key parameters, including the fractional order and viscosity coefficient. The proposed approach provides a systematic theoretical framework and practical reference for the analysis and prediction of long-term prestress relaxation in soil anchorage systems. Full article

Sustainable agriculture in arid and semi-arid regions critically depends on the interactions between soil physical properties, water dynamics, and mechanized field operations. In this context, soil physical attributes, such as texture, bulk density, aggregate stability, and soil water potential, play a crucial role in determining soil–water–machine interactions. Soil attributes such as texture, bulk density, aggregate stability, and soil water potential govern both water movement and retention, as well as traction efficiency, draft energy, and compaction under mechanized traffic. Deviations from the optimal soil moisture range in sandy or calcareous soils increase wheel slip, energy consumption, and soil structural degradation, resulting in uneven infiltration and reduced water-use efficiency. This review synthesizes recent research on these coupled processes, emphasizing how soil mechanics and hydraulics collectively influence irrigation performance and mechanization energy requirements. The novelty of this study lies in presenting an integrated soil–machine–water conceptual framework that captures the continuous interactions and interdependencies among soil physical state, machine behavior, and water movement. By highlighting these dynamic relationships, this review provides a systems-level perspective on energy and water interactions in dryland agroecosystems, offering a foundation for predicting the environmental implications of mechanized operations under arid conditions. Overall, the review demonstrates that sustainable mechanized agriculture in arid regions requires integrated management of soil physical state, machine operation, and irrigation timing, where maintaining soil moisture within an optimal operational range is the key factor for reducing energy losses, preventing soil compaction, and improving water productivity. Full article

Helical gear transmissions in wind turbine gearboxes operate under high torque, variable speed, and complex rolling–sliding contact conditions, where friction-induced wear evolves in a spatially non-uniform manner. However, most existing dynamic models assume uniform or mild wear and therefore fail to capture the nonlinear coupling between localised tooth surface degradation, gear mesh dynamics, and vibration response. In this work, a nonlinear dynamic model of a helical gear pair is formulated by incorporating time-varying mesh stiffness, elasto-hydrodynamic lubrication (EHL)-based friction forces, and wear-dependent contact geometry. The governing equations of motion are derived to explicitly account for the influence of inhomogeneous tooth wear on the contact load distribution and frictional excitation during meshing. Wear evolution is represented as a spatially varying modification of tooth surface topology, enabling the progressive coupling between wear depth, mesh stiffness perturbations, and dynamic transmission error. The model is employed to analyse the effects of non-uniform wear on system stability, vibration spectra, and dynamic response under wind turbine operating conditions. Numerical results reveal that uneven wear introduces nonlinear modulation of gear mesh forces and generates characteristic sidebands and amplitude variations in the vibration signal that are absent in conventional mild-wear formulations. These wear-induced dynamic features provide mathematically traceable indicators for the onset and progression of uneven tooth degradation. The proposed framework establishes a physics-based link between wear evolution and measurable vibration responses, providing a rigorous foundation for advanced vibration-based diagnostics and model-driven condition monitoring of wind turbine gearboxes. Full article

Civil aviation VHF communication is safety-critical, yet operational links are routinely disturbed by atmospheric effects, aging hardware, and electromagnetic interference. The resulting anomalies are typically weak, intermittent, and extremely rare, which makes real-time detection difficult under strong temporal dependence and severe class imbalance. We propose an end-to-end framework that couples (i) a learnable kernel projection for adaptive nonlinear feature extraction, (ii) a differentiable relevance–redundancy objective for feature refinement, and (iii) conditional temporal generation to augment minority anomaly patterns. A lightweight CNN–LSTM head is used for streaming inference. Training uses a mixture of operational anomalies and simulated degradation scenarios, while evaluation is conducted using operational data only. Experiments on 1.2 million VHF frames collected from real flight operations and ground station monitoring achieve an F1-score of 0.947, ROC-AUC of 0.972, and PR-AUC of 0.968, with an average inference latency of 34.7 ms. Full article

Predicting the future impact of technical economic documents at release time is challenging due to delayed supervision signals, long-tailed label distributions, and time- and domain-dependent shifts in language and topics. Moreover, similarity graphs derived from text embeddings can be noisy due to boilerplate and evolve under temporal drift, making robustness and leakage-free evaluation essential. We formulate grant-time patent impact prediction as a node classification and within-domain ranking problem on a large-scale semantic similarity document graph built from patent text embeddings, avoiding any future citation leakage. The document graph is constructed via ANN Top-K retrieval and similarity thresholding, enabling scalable and reproducible sparsification on hundreds of thousands of nodes. We propose GraphGPT-Patent, which adapts a reversible graph-to-sequence foundation backbone to local subgraphs extracted from the similarity network. The model incorporates time- and domain-conditioned edge reliability to suppress drift-induced and template-driven pseudo-similarity, and optimizes a joint objective coupling high-impact classification with ranking consistency within comparable groups. Experiments on USPTO granted patents (2000–2022) across three high-volume CPC domains and three evaluation horizons show consistent gains over text-only and GNN baselines, achieving up to 0.94 recall for the positive class and improved macro-average recall across nine settings. Temporal shift analyses further quantify the effect of training-data freshness, while explanation subgraphs provide auditable structural evidence of model decisions. The proposed framework offers an effective graph-based learning pipeline for scalable impact prediction and downstream triage under strict information constraints. Full article

We propose Adaptive Inference, a portfolio management framework extending Active Inference to non-stationary financial environments. The framework integrates inference, control, and execution under endogenous uncertainty, modeling investment decisions as coupled dynamics of belief updating, preference encoding, and action selection rather than optimization over fixed objectives. In this approach, portfolio behavior is governed by the expected free energy (EFE) minimization, showing that classical valuation models emerge as limiting cases when epistemic components vanish. Using train–test evaluation on the ARKK Innovation ETF (2015–2025), we identify a Passivity Paradox: frozen belief transfer outperforms naive adaptive learning. A Professional Agent achieves a Sharpe ratio of 0.39 while its adaptive counterpart degrades to $-0.28$, reflecting belief contamination when learning from policy-dependent signals. Crucially, the architecture is not designed to generate alpha but to perform endogenous risk management that mitigates overtrading under regime ambiguity and distributional shift. Adaptive Inference Agents maintain long exposure most of the time while tactically reducing positions during high-entropy periods, implementing uncertainty-aware passive investing. All agents reduce realized volatility relative to ARKK Buy-and-Hold (43.0% annualized). Cross-asset validation on the S&P 500 ETF (SPY) shows that inference-guided risk shaping achieves a positive Entropic Sharpe Ratio (ESR), defined as excess return per unit of informational work, thereby quantifying the economic value of information under thermodynamic constraints on inference. Full article

Background/Objectives: Humans have at least 26 bitter taste receptors (T2Rs), and among these, bitter taste receptor 14 (T2R14) is highly expressed in both oral and extraoral tissues. Over 100 bitter ligands can activate T2R14, including hormones, vitamins, plant compounds, and peptides. Previous studies suggest that bitter tastants such as quinine and caffeine can inhibit G protein-coupled receptor kinases (GRKs) and delay T2R signal termination. Our earlier research showed that peptides from alcalase and chymotrypsin hydrolysates of beef proteins inhibited quinine-dependent calcium release through T2R4, with AGDDAPRAVF and ETSARHL showing the greatest effectiveness. However, the effect of these antagonistic peptides on other T2Rs, such as T2R14 signaling, remains unknown. This study aimed to evaluate the ability of these beef protein-derived peptides to activate or inhibit T2R14 signaling and the involvement of GRK2 in signal termination. Methods and Results: Our results indicate that the above two antagonist peptides significantly inhibit T2R14 activity. Furthermore, GRK2 knockout in HEK cells stably expressing T2R14 decreases intracellular calcium release, as measured by the area under the curve (AUC), and also delays the fall time (indication of desensitization) of the calcium response when exposed to the T2R14 agonist diphenhydramine (DPH) or beef protein-derived agonist peptide TMTL. Next, we measured the effects of these ligands on cAMP accumulation, and our results suggest no significant change in cAMP levels upon treatment with beef protein-derived peptides. Conclusions: Thus, this study showed that beef protein-derived peptides can function as both T2R inhibitors and mediate T2R14 desensitization through GRK2 signaling. These antagonistic food protein-derived peptides inform strategies to enhance nutrition, such as promoting healthier food choices by reducing bitterness and thereby improving the palatability of health-promoting bitter foods, such as fruit and vegetable extracts, as well as bitter medications. Full article

Aerobic composting shows state-dependent dynamics in key parameters such as temperature, moisture content, oxygen concentration, and pH, and these variables are strongly coupled over time. This coupling makes accurate state identification and process regulation challenging when relying on single-parameter thresholds or experience-based control. To enable robust recognition of composting states throughout the process, we propose an IRRTO-optimized CNN–LSTM–attention model (IRRTO–CNN–LSTM–attention). The model uses a convolutional neural network (CNN) to extract discriminative multivariate features, a long short-term memory (LSTM) network to model temporal dependencies, and an attention module to adaptively emphasize informative features. To address the hyperparameter selection challenge, the Rapidly-exploring Random Tree Optimizer (RRTO) was introduced and further enhanced via four strategies (fluctuating attenuation adaptive regulation, dual-mode guided update, dynamic dimension adaptive perturbation, and dual-mechanism adaptive perturbation regulation), forming the improved IRRTO. The proposed approach was validated using sensor data from windrow composting of pig manure and corn straw. The IRRTO–CNN–LSTM–attention model achieved an overall accuracy of 98.31% in classifying the four states (mesophilic/heating, thermophilic, cooling, and abnormal) on the independent test set, which was 3.39 percentage points higher than the RRTO-based model. These results suggest that the proposed method can accurately identify composting states and support early warning and state-specific regulation in practical aerobic composting systems. Full article

This study developed and evaluated an integrated experimental–computational framework to quantify coconut-oil transport and marbling stabilization in soy protein concentrate (SPC) during static holding and co-extrusion with a cooling die. Temperature-sweep rheology and Differential Scanning Calorimetry (DSC) identified the main gelation transition at 65–78 °C, with oil shifting gelation to higher temperatures and increasing enthalpy, supporting an exit/cooling target of 70–75 °C. Static drop tests at 100 °C for 60 s were analyzed by depth-resolved imaging and coupled with a single-phase CFD model to inversely calibrate an effective diffusion coefficient for coconut oil in SPC (D_ref = 4.86 × 10⁻¹⁸ m²/s). A viscosity-coupled fractional Stokes–Einstein relationship then gave temperature-dependent effective diffusivities of 1.89 × 10⁻¹⁸ to 4.86 × 10⁻¹⁸ m²/s over 60–100 °C, indicating reduced oil mobility during cooling. Additional static time-temperature comparisons suggested limited redistribution beyond ~50 s. Co-extrusion simulations and product imaging further indicated that staged hot-zone residence followed by rapid cooling can help stabilize oil domains into marbling-like structures. The framework can support selection of cooling-die temperatures, residence times, and oil-injection conditions. Future work should extend the framework by linking marbling microstructure with sensory performance, oxidative stability, and sensitivity analysis of key transport parameters. Full article

The load transfer method is important for the settlement prediction of axially loaded piles, but in multi-layered complex soils, it lacks analytical solutions. Traditional numerical methods such as the finite element method suffer from strong dependence on mesh generation, time-consuming iterative calculations, and high computational costs for back-analysis. This paper proposes a load transfer analysis model based on a Domain Decomposition Physics-Informed Neural Network. A multi-subnet parallel architecture is adopted to simulate multi-layered soils, solving the problem of inter-layer stress–strain discontinuity through interface coupling and gradient continuity constraints; a non-dimensionalization system and a hard constraint mechanism are introduced to enhance training efficiency and physical consistency; and a two-stage analysis framework comprising surrogate model forward analysis and field data inversion is established. Numerical experimental results indicate that the forward analysis of this model is in high agreement with FEM simulation results, and computational efficiency is improved by six orders of magnitude; based on a small amount of field static load test data, multi-layer soil parameters are accurately inverted, achieving more precise pile settlement prediction than FEM. Comparative analysis validates the effectiveness of the domain decomposition multi-subnet over a single network, demonstrating extensibility to hyperbolic and exponential multi-soil constitutive models. Full article

Continuous blood pressure (BP) monitoring is essential for the prevention and management of cardiovascular diseases. Traditional cuff-based methods cause discomfort during repeated measurements, and wearable sensors require direct skin contact, limiting their applicability. Radar-based contactless BP measurement has emerged as a promising alternative. However, radar pulse wave (RPW) signals are susceptible to motion artifacts, respiratory interference, and environmental clutter, posing persistent challenges to estimation accuracy and robustness. In this paper, we propose MARU-MTL, a Mamba-enhanced multi-task learning framework for continuous BP estimation using a single millimeter-wave radar sensor. To address signal quality degradation, a Variational Autoencoder-based Signal Quality Index (VAE-SQI) mechanism is proposed to automatically screen RPW segments without manual annotation. To capture long-range temporal dependencies across cardiac cycles, we integrate a Bidirectional Mamba module into the bottleneck of a U-Net backbone, enabling linear-time sequence modeling with respect to the segment length. We also introduce a multi-task learning strategy that couples BP regression with arterial blood pressure waveform reconstruction to strengthen physiological consistency. Extensive experiments on two datasets comprising 55 subjects demonstrate that MARU-MTL achieves mean absolute errors of 3.87 mmHg and 2.93 mmHg for systolic and diastolic BP, respectively, meeting commonly used AAMI error thresholds and achieving metrics comparable to BHS Grade A. Full article

Slow lateral wandering and trochoidal-like motion are commonly observed in intense atmospheric vortices, yet most reduced-order vortex models assume a fixed axis or represent centre motion as purely advective. In this work, we propose a minimal reduced-order framework in which slow gyroscopic precession is introduced as an explicit degree of freedom superimposed on a rapidly rotating vortex core. The vortex is represented by a Burgers–Rott-type velocity field with time-dependent stretching rate and circulation, while the vortex centre undergoes a slow precessional motion governed by a time-dependent rate ${\Omega }_p\left(t\right)$. The evolution of the vortex parameters is coupled to environmental variability through simple relaxation laws driven by standard large-scale diagnostics, including convective available potential energy, vertical shear, and background vorticity. A tracker-only analysis of tropical cyclone best-track data is used to constrain the appropriate dynamical regime at the track scale, indicating that observed centre wandering typically occurs in a slow-precession limit P = ${\Omega }_p/{\omega }_c\ll 1$. Numerical demonstrations in cyclone-like configurations show that, despite the smallness of the precession number, cumulative lateral displacement and enhanced Lagrangian dispersion can develop over the vortex lifetime. The proposed framework is intended as a proof-of-concept reduced-order model that isolates the role of weak, environmentally forced precession in modulating vortex wandering and transport, and complements more detailed numerical and observational studies. Full article

In this paper I study a two-dimensional discrete-time evolutionary logistic-type model describing the coupled dynamics of population density and a continuously evolving trait. I provide a local bifurcation analysis of the equilibria, deriving explicit conditions for their existence and local stability. In particular, I show that the boundary and interior equilibria exchange stability through a transcritical bifurcation, and I characterize analytically the subsequent loss of stability of the interior equilibrium via period-doubling and Neimark–Sacker bifurcations. Transversality is established in all cases, and the criticality of the bifurcations is determined through normal form and Lyapunov coefficient computations. I show that the period-doubling bifurcation can be supercritical or subcritical, while the Neimark–Sacker bifurcation is generically nondegenerate and may be either supercritical or subcritical, depending on parameter values. Full article