Search Results (6,185)

Base excision repair (BER) is an important mechanism for maintaining genomic integrity and preventing DNA damage and mutations induced by oxidative stress. This study aimed to examine the relationship between oxidative stress and BER activity in newborn piglets by supplementing their mothers’ diets during pregnancy with long-chain n-3 polyunsaturated fatty acids (PUFAs) from algal and fish oils, provided either in natural form or as nanoparticles. BER enzyme activity was assessed using a nicking assay, and their gene expression levels by RT-qPCR in the livers of pregnant gilts and their offspring. Preliminary results indicated that maternal supplementation with oils rich in long-chain n-3 PUFAs significantly reduced (by 32%) BER capacity in the livers of their offspring. A corresponding decrease in mRNA expression of BER genes (TDG, MPG, OGG1) was observed in piglets from gilts receiving fish and algal oil supplements. Maternal supplementation with long-chain n-3 PUFAs may protect foetuses and neonates against oxidative stress, reducing DNA damage and enhancing genomic stability, which could positively influence early postnatal growth. The observed reduction in BER enzyme activity in newborn piglets likely reflected improved DNA integrity, and natural oil forms appeared more effective than their nanoparticle formulations. Disparities in socioeconomic areas related to access to functional foods with health-promoting properties highlight the importance of targeted strategies that integrate local systems and promote nutritional equity. Full article

Deep-sea submersibles, often featuring a symmetrical design for hydrodynamic stability, operate as safety-critical systems in extreme environments, where the tight dynamic coupling between subsystems like hydraulics and propulsion creates complex failure modes that are challenging to diagnose. A localized fault in one system can propagate, inducing anomalous behavior in another and confounding conventional single-system monitoring approaches. This paper introduces a novel unsupervised anomaly detection framework, the Dual-Stream Coupled Autoencoder (DSC-AE), designed specifically to address this cross-system fault challenge. Our approach leverages a dual-encoder, single-decoder architecture that explicitly models the normal coupling relationship between the hydraulic and propulsion systems by forcing them into a shared latent representation. This architectural design establishes a holistic and accurate baseline of healthy, system-wide operation. Any deviation from this learned coupling manifold is robustly identified as an anomaly. We validate our model using real-world operational data from the deep-sea submersible, including curated test cases of intra-system and inter-system faults. Furthermore, we demonstrate that the proposed framework offers crucial diagnostic interpretability; by analyzing the model’s reconstruction error heatmaps, it is possible to trace fault origins and their subsequent propagation pathways, providing intuitive and actionable decision support for submersible operation and maintenance. This powerful diagnostic capability is substantiated by superior quantitative performance, where the DSC-AE significantly outperforms baseline methods in detecting propagated faults, achieving higher accuracy and recall, among other performance metrics. Full article

We develop a comprehensive dynamic Walrasian framework entirely in $L^{\infty }$ so that prices and allocations are essentially bounded, and market clearing holds pointwise almost everywhere. Utilities are allowed to be locally Lipschitz and quasi-concave; we employ Clarke subgradients to derive generalized quasi-variational inequalities (GQVIs). We endogenize inventories through a capital-accumulation constraint, leading to a differential QVI (dQVI). Existence is proved under either strong monotonicity or pseudo-monotonicity and coercivity. We establish Walras’ law, and the complementarity, stability, and sensitivity of the equilibrium correspondence in $L^2$-metrics, incorporate time-discounting and uncertainty into $\Omega \times [0,T]$, and present convergent numerical schemes (Rockafellar–Wets penalties and extragradient). Our results close the “in mean vs pointwise” gap noted in dynamic models and connect to modern decomposition approaches for QVIs. Full article

This study focuses on improving the wear resistance of cutting tools and extending their service life under intense mechanical, thermal, and radiation loads in nuclear power plant environments. This research investigates the potential of electrospark alloying (ESA) using W–Zr–B system electrodes obtained from disks synthesised by spark plasma sintering (SPS). The novelty of this work lies in the use of SPS-synthesised W–Zr–B ceramics, which are promising for nuclear applications due to their high thermal stability, radiation resistance and neutron absorption, as ESA electrodes. This work also establishes the relationship between discharge energy, coating microstructure and performance. The alloying electrode material exhibited a heterogeneous microstructure containing WB₂, ZrB₂, and minor zirconium oxides, with high hardness (26.6 ± 1.8 GPa) and density (8.88 g/cm³, porosity <10%). ESA coatings formed on HS6-5-2 steel showed a hardened layer up to 30 µm thick and microhardness up to 1492 HV, nearly twice that of the substrate (~850 HV). Elemental analysis revealed enrichment of the surface with W, Zr, and B, which gradually decreased toward the substrate, confirming diffusion bonding. XRD analysis revealed a multiphase structure comprising WB₂, ZrB₂, WB₄, and BCC/FCC solid solutions, indicating the formation of complex boride phases during the ESA process. Tribological tests demonstrated significantly enhanced wear resistance of ESA coatings. The results confirm the efficiency of ESA as a simple, low-cost, and energy-efficient method for local strengthening and restoration of cutting tools. Full article

In this paper, we investigate the stability of a triangularly coupled triple-loop thermosyphon system with momentum and heat exchange at the coupling point as well as the existence of disturbances. The controller consists of a single, local-state feedback. From the stability analysis, we obtain explicit bounds on the feedback gains, which depend on the Rayleigh numbers and the momentum coupling parameter, but independent of the thermal coupling parameter. The existence of the stability bounds allows us to design decentralized adaptive controllers to automatically search for the feasible gains when the system parameters are unknown. In the case of existing disturbances in the system, we approximate the disturbances via an extended-state observer for the purpose of disturbance rejection. Numerical results are given to demonstrate the performance of the proposed decentralized disturbance rejection controller design. Full article

In this paper we consider a reaction–diffusion model with nonlocal diffusion and a nonlinear reaction term analogous to a local reaction–diffusion problem with Robin boundary conditions. Firstly, we investigate the existence of solutions in a two-dimensional spatial domain. Then we attach a semi-implicit numerical scheme by using finite differences in order to approximate the solution. We use the iterative Newton method to numerically solve the resulting implicit problem. Based on theoretical results we generate an adaptive mesh in time that ensures the stability of the corresponding numerical scheme. Numerical experiments that illustrate the effectiveness of the theoretical results are provided. Full article

The Dung Beetle Optimizer (DBO) has shown promise in solving complex optimization problems, yet it often suffers from premature convergence and limited accuracy. To overcome these limitations, this paper proposes the Enhanced Reproductive Dung Beetle Optimizer (ERDBO). The ERDBO introduces a three-stage mechanism: (1) a larval growth phase using experiential learning to enrich population diversity and improve global exploration; (2) a reproduction and nurturing phase that employs parent–offspring verification and a teaching strategy to strengthen local exploitation; and (3) a predator avoidance phase integrating Lévy flight and sinusoidal perturbations to enhance adaptability and accelerate convergence. The effectiveness of the proposed algorithm is assessed using the CEC2017 benchmark functions, where it is contrasted with several advanced metaheuristic approaches. The experimental findings highlight its advantages in terms of convergence rate, stability, and solution precision. Furthermore, the ERDBO is applied to three well-known engineering design tasks—namely the tension/compression spring, the three-bar truss, and the pressure vessel problem. The outcomes verify both its efficiency and applicability, indicating that the ERDBO provides a robust and competitive optimization framework for tackling challenging real-world engineering scenarios. Full article

Virtual synchronous generator (VSG) simulates the dynamic characteristics of synchronous generator, offering significant advantages in flexibly adjusting virtual inertia and damping parameters. However, their dynamic stability is susceptible to constraints such as control parameter design, grid disturbances, and the intermittent nature of distributed power sources. This study addresses the degradation of transient performance in traditional sliding mode control for VSG, caused by insufficient multi-parameter cooperative adaptation. It proposes an adaptive sliding mode control strategy based on radial basis function (RBF) neural networks. Through theoretical analysis of the influence mechanism of virtual inertia and damping coefficient perturbations on system stability, the RBF neural network achieves dynamic parameter decoupling and nonlinear mapping. Combined with an integral-type sliding surface to design a weight-adaptive convergence law, it effectively avoids local optima and ensures global stability. This strategy not only enables multi-parameter cooperative adaptive regulation of frequency fluctuations but also significantly enhances the system’s robustness under parameter perturbations. Simulation results demonstrate that compared to traditional control methods, the proposed strategy exhibits significant advantages in dynamic response speed and overshoot suppression. Full article

The objective of this study is to analyze spatial entropy flows that reveal the directional dynamics of climate change—patterns that remain obscured in traditional statistical analyses. This approach enables the identification of pathways for “climate information transport”, highlights associations with atmospheric circulation types, and allows for the localization of both sources and “informational voids”—regions where entropy is dissipated. The analytical framework is grounded in a quantitative assessment of long-term climate variability across Europe over the period 1901–2010, utilizing Shannon entropy as a measure of atmospheric system uncertainty and variability. The underlying assumption is that the variability of temperature and precipitation reflects the inherently dynamic character of climate as a nonlinear system prone to fluctuations. The study focuses on calculating entropy estimated within a 70-year moving window for each calendar month, using bivariate distributions of temperature and precipitation modeled with copula functions. Marginal distributions were selected based on the Akaike Information Criterion (AIC). To improve the accuracy of the estimation, a block bootstrap resampling technique was applied, along with numerical integration to compute the Shannon entropy values at each of the 4165 grid points with a spatial resolution of 0.5° × 0.5°. The results indicate that entropy and its derivative are complementary indicators of atmospheric system instability—entropy proving effective in long-term diagnostics, while its derivative provides insight into the short-term forecasting of abrupt changes. A lag analysis and Spearman rank correlation between entropy values and their potential supported the investigation of how circulation variability influences the occurrence of extreme precipitation events. Particularly noteworthy is the temporal derivative of entropy, which revealed strong nonlinear relationships between local dynamic conditions and climatic extremes. A spatial analysis of the information entropy field was also conducted, revealing distinct structures with varying degrees of climatic complexity on a continental scale. This field appears to be clearly structured, reflecting not only the directional patterns of change but also the potential sources of meteorological fluctuations. A field-theory-based spatial classification allows for the identification of transitional regions—areas with heightened susceptibility to shifts in local dynamics—as well as entropy source and sink regions. The study is embedded within the Fokker–Planck formalism, wherein the change in the stochastic distribution characterizes the rate of entropy production. In this context, regions of positive divergence are interpreted as active generators of variability, while sink regions function as stabilizing zones that dampen fluctuations. Full article

The increasing integration of High-Voltage Direct Current (HVDC) systems and renewable energy challenges traditional grid strength assessment. This paper proposes a comprehensive framework that combines a composite strength index with an interpretable importance analysis to address this issue. First, a composite index is developed using the AHP-CRITIC method to fuse structural and fault withstand metrics. Then, to identify the factors influencing this index, SHapley Additive exPlanations (SHAP) is employed, accelerated by a high-fidelity Gaussian Process Regression (GPR) surrogate model that overcomes the computational burden of large-scale simulations. This GPR-SHAP approach provides both global parameter rankings and local, scenario-specific explanations, overcoming the limitations of conventional sensitivity analysis. Validated on a detailed model of the Southwest Power Grid in China, the framework successfully quantifies grid strength and pinpoints key vulnerabilities. Verification through a typical scenario demonstrates that implementing coordinated increases in both generation and load (each by 1000 MW) in the Chengdu area, as guided by local SHAP explanations, significantly improves the grid strength index from 33.73 to 47.61. It provides operators with a dependable tool to transition from experience-based practices to targeted, proactive stability management. Full article

In an era of global warming, sustainable agriculture, which emphasises the conservation of biodiversity and the rational use of natural resources, is growing in importance. One of the key elements is to increase the genetic diversity of crops through the use of crop wild relatives (CWRs) and local varieties, which provide a source of genes for resistance to biotic and abiotic stresses. Modern agricultural systems are characterised by low biodiversity, which increases the susceptibility of plants to diseases and pests. Growing mixtures of varieties, both intra- and interspecific, is a practical strategy to increase plant resistance, stabilise yields and reduce pathogen pressure. This manuscript has a review character and synthesises the current literature on the use of CWRs, local varieties, and variety mixtures in sustainable agriculture. The main research question of the study is to what extent plant genetic resources, including CWRs and local varieties, as well as the cultivation of variety mixtures, can promote plant resistance, stabilise yields and contribute to sustainable agriculture under climate change. The objectives of the study are to assess the role of genetic resources and variety mixtures in maintaining biodiversity and yield stability, and to analyse the potential of CWRs and local varieties in enhancing plant resistance. Additionally, the study investigates the impact of variety mixtures in reducing disease and pest development, and identifies barriers to the use of genetic resources in breeding along with strategies to overcome them. The study takes an interdisciplinary approach including literature and gene bank data analysis (in situ and ex situ), field trials of cultivar mixtures under different environmental conditions, genetic and molecular analysis of CWRs, the use of modern genome editing techniques (CRISPR/Cas9) and assessment of ecological mechanisms of mixed crops such as barrier effect, and induced resistance and complementarity. In addition, the study considers collaboration with participatory and evolutionary breeding programmes (EPBs/PPBs) to adapt local varieties to specific environmental conditions. The results of the study indicate that the integration of plant genetic resources with the practice of cultivating variety mixtures creates a synergistic model that enhances plant resilience and stabilises yields. This approach also promotes agroecosystem conservation, contributing to sustainable agriculture under climate change. Full article

To address issues such as low visual SLAM (Simultaneous Localization and Mapping) positioning accuracy and poor map construction robustness caused by light variations, foliage occlusion, and texture repetition in unstructured orchard environments, this paper proposes an orchard robot navigation method based on an improved RTAB-Map algorithm. By integrating ORB-SLAM3 as the visual odometry module within the RTAB-Map framework, the system achieves significantly improved accuracy and stability in pose estimation. During the post-processing stage of map generation, a height filtering strategy is proposed to effectively filter out low-hanging branch point clouds, thereby generating raster maps that better meet navigation requirements. The navigation layer integrates the ROS (Robot Operating System) Navigation framework, employing the A* algorithm for global path planning while incorporating the TEB (Timed Elastic Band) algorithm to achieve real-time local obstacle avoidance and dynamic adjustment. Experimental results demonstrate that the improved system exhibits higher mapping consistency in simulated orchard environments, with the odometry’s absolute trajectory error reduced by approximately 45.5%. The robot can reliably plan paths and traverse areas with low-hanging branches. This study provides a solution for autonomous navigation in agricultural settings that balances precision with practicality. Full article

Hybrid PV-TEG systems can harvest both solar electrical and thermoelectric power, but their operating point drifts with irradiance, temperature gradients, partial shading, and load changes—often yielding multi-peak P-V characteristics. Conventional MPPT (e.g., P&O) and fixed-structure integer-order PID struggle to remain fast, stable, and globally optimal in these conditions. To address fast, robust tracking in these conditions, we propose an adaptive fractional-order PID (FOPID) MPPT whose parameters (K_p, K_i, K_d, λ, μ) are auto-tuned by the red-billed blue magpie optimizer (RBBMO). RBBMO is used offline to set the controller’s search ranges and weighting; the adaptive law then refines the gains online from the measured ΔV, ΔI slope error to maximize the hybrid PV-TEG output. The method is validated in MATLAB R2024b/Simulink 2024b, on a boost-converter–interfaced PV-TEG using five testbeds: (i) start-up/search, (ii) stepwise irradiance, (iii) partial shading with multiple local peaks, (iv) load steps, and (v) field-measured irradiance/temperature from Shanxi Province for spring/summer/autumn/winter. Compared with AOS, PSO, MFO, SSA, GHO, RSA, AOA, and P&O, the proposed tracker is consistently the fastest and most energy-efficient: 0.06 s to reach 95% MPP and 0.12 s settling at start-up with 1950 W·s harvested (vs. 1910 W·s AOS, 1880 W·s PSO, 200 W·s P&O). Under stepwise irradiance, it delivers 0.95–0.98 kJ at t = 1 s and under partial shading, 1.95–2.00 kJ, both with ±1% steady ripple. Daily field energies reach 0.88 × 10⁻³, 2.95 × 10⁻³, 2.90 × 10⁻³, 1.55 × 10⁻³ kWh in spring–winter, outperforming the best baselines by 3–10% and P&O by 20–30%. Robustness tests show only 2.74% power derating across 0–40 °C and low variability (Δv^max typically ≤ 1–1.5%), confirming rapid, low-ripple tracking with superior energy yield. Finally, the RBBMO-tuned adaptive FOPID offers a superior efficiency–stability trade-off and robust GMPP tracking across all five cases, with modest computational overhead. Full article

The ODYSEA (Ocean DYnamics and Surface Exchange with the Atmosphere) mission will provide simultaneous two-dimensional measurements of currents and winds for the first time. According to the ODYSEA radar concept, with a high incidence angle, current noise is primarily driven by backscattered power, which is triggered by wind speed. Therefore, random noise will affect the quality of observations. In low wind conditions, the absence of surface roughness increases the noise level considerably, to the point where the measurement becomes unusable, as the error can exceed 3 m/s at 5 km posting compared to mean current amplitudes of tens of cm/s. Winds higher than 7.5 m/s enable current measurements at 5 km posting with an RMS accuracy below 50 cm/s, but derivatives of currents will amplify noise, hampering the understanding of ocean dynamics and the interaction between the ocean and the atmosphere. In this context, this study shows the advantages and limitations of using noise-reduction algorithms. A convolutional neural network, a UNet inspired by the work of the SWOT (Surface Water and Ocean Topography) mission, is trained and tested on simulated radial velocities that are representative of the global ocean. The results are compared with those of classical smoothing: an Adaptive Gaussian Smoother whose filtering transfer function is optimized based on local wind speed (e.g., more smoothing in regions of low wind). The UNet outperforms the kernel smoother everywhere with our simulated dataset, especially in low wind conditions (SNR << 1) where the smoother essentially removes all velocities whereas the UNet mitigates random noise while preserving most of the signal of interest. Error is reduced by a factor of 30 and structures down to 30 km are reconstructed accurately. The UNet also enables the reconstruction of the main eddies and fronts in the relative vorticity field. It shows good robustness and stability in new scenarios. Full article

Teeth are the hardest organs in the human body. As mineralized structures, they possess a unique microstructure composed of orderly arranged piezoelectric materials such as hydroxyapatite crystals and collagen fibers. Teeth exhibit effective piezoelectric coefficients of approximately 1.2–1.6 pC/N. This inherent property enables teeth to function as natural piezoelectric sensors, converting routine mechanical stresses (e.g., chewing and biting forces, typically ranging from 22.4 to 68.3 kg) into localized electrical signals. This characteristic is of great importance in dentistry and materials science, offering new perspectives into a deeper understanding of the physiological functions and pathological mechanisms of teeth. Despite promising advances, challenges regarding the clinical translation, long-term stability, and biosafety of piezoelectric materials in the oral environment remain unresolved. This review highlights the biological functions of the piezoelectric properties of teeth, discusses recent applications and notable advancements of piezoelectric materials in dentistry, and outlines the challenges and research priorities for future clinical applications. Full article