Search Results (856)

Promoting the agricultural recycling of biogas slurry (BS) is crucial for sustainable development, yet its long-term ecological impacts remain unclear. Through a multi-year field trial in a sugarcane system, this study examined the effects of BS application (0, 3, and 6 years) on the soil properties, bacterial communities, and functional genes for C, N, P, and S cycling. The results revealed distinct two-phase patterns of changes in soil properties, microbial communities, and functional genes. Short-term (3-year) application induced a “disturbance” phase, characterized by significant acidification (pH decreased by 17.91%), a surge in nitrate-N (increased by 757.27%), and a transient decline in bacterial richness. Long-term (6-year) application drove a “functional restructuring” phase, reversing acidification and significantly increasing soil organic matter (29.05%) and total nitrogen (TN) (20.81%). Bacterial richness recovered, and community composition distinctively restructured. Functional gene analysis revealed shifts in gene abundance that transitioned from high abundance in the short term to a new balance favoring processes like N fixation. Co-occurrence network analysis indicated that this functional shift was associated with core microbial modules (e.g., Firmicutes) and changes in soil pH and SOM. This study suggests that, although short-term application causes significant adjustments, sustained and appropriate BS application can ultimately enhance soil fertility and promote a functionally reorganized state by reshaping microbial interaction networks. It presents a microbial ecological basis for the safe and sustainable use of BS in circular agriculture. Full article

Acinetobacter baumannii (A. baumannii) represents a major threat because of its multidrug resistance, achieved through its ability to control virulence, and its mechanisms of drug efflux resistance. In this study, we used a combined experimental–computational approach to create and evaluate antimicrobial peptides that targeted the two essential pathogenic proteins, BfmR and AbaQ. The genomic analysis of a clinical isolate showed an extensive resistome and virulence profile, which matched high-risk global lineages. This study conducted molecular docking of an experimental AMP (cathelicidin KR-12 screened from the literature) and a rationally designed synthetic AMP (modified KR-12 analog) with pathogenic proteins, followed by 200 ns molecular dynamics simulations to evaluate both the binding stability and inhibitory potential of the compounds. The disk diffusion assay and microdilution assay were performed against A. baumannii. The study used comparative trajectory analyses, including RMSD, RMSF, radius of gyration, solvent-accessible surface area, principal component analysis, and MM-PBSA free energy calculations, to show that the synthetic AMP created stable electrostatic and hydrogen-bond networks, which caused conformational locking, and reached lower energy states than the experimental peptide. The synthetic AMP showed significant inhibition in validation in vitro. Contrastingly, the experimental AMP had transient interactions and no specificity. The study demonstrates that rationally designed AMPs have therapeutic potential, while the results create a reliable in silico framework to combat multidrug-resistant A. baumannii. Full article

One of the key factors determining energy consumption and control stability in hydraulic servovalves with direct electric drive is the flow forces acting on the spool. These forces are complex in nature and consist of both steady-state and transient components, with the steady-state component exerting the dominant influence on the performance and dynamics of spool valves. In recent years, this issue has become the subject of intensive research aimed at reducing undesirable hydraulic loads while maintaining high nominal flow capacity, strong energy efficiency, and low manufacturing cost. In engineering practice, the most effective approach has proven to be the modification of the spool geometry in order to control the direction and jet angle of the outflow while keeping the valve sleeve design as simple as possible. This solution reduces the forces acting on the spool without the need to redesign the flow channels or increase production complexity. This study presents classical analytical methods used to calculate flow forces in typical spool valve designs, which serve as a reference point for subsequent investigations. Then, using CFD simulation tools, a method of flow force compensation is demonstrated for selected spool geometries, followed by a detailed comparative analysis of their effectiveness. The results may provide a foundation for developing more energy-efficient and dynamically stable direct-drive servovalve constructions. Full article

High-performance operation of permanent magnet synchronous motors (PMSMs) strongly depends on the reliable availability of rotor position and speed information. Although this information is commonly obtained using physical position sensors, such sensors increase system cost and structural complexity and may reduce long-term reliability, particularly in demanding operating environments. In this study, a model-based, discrete-time, nonlinear gradient observer is adapted for the sensorless estimation of rotor speed and position in PMSMs. The developed Runge–Kutta model-based gradient observer (RKGO) utilizes stator voltage inputs and measured stator currents within a mathematical motor model to estimate the system states. In contrast to conventional sensorless estimation approaches, the adopted observer framework exploits discretization-based gradient dynamics to enhance numerical robustness and convergence behavior under nonlinear operating conditions. The observer design specifically targets stable and accurate state estimation in discrete-time implementations, with a particular focus on low-speed operating conditions. The performance of the adapted method is experimentally evaluated under low-speed operating conditions, including transient and steady-state operation. Real-time implementation is carried out on a dSPACE DS1104 control platform, including loaded acceleration scenarios to assess practical robustness. In addition, a comparative analysis with the Extended Kalman Filter (EKF) and the Runge–Kutta Extended Kalman Filter (RKEKF) is conducted at 60 rad/s under identical experimental conditions. Experimental results show that the RKGO method achieves accurate steady-state speed and position estimation with acceptable transient performance. The findings demonstrate that RKGO can be considered a viable alternative for low-speed sensorless PMSM drive applications. Full article

As modern power systems grow increasingly complex, there is a pressing need for stability analysis methods capable of handling nonlinear dynamics while providing physically meaningful and reliable stability indices. Port-Hamiltonian (PH) frameworks have emerged as strong candidates in this regard, offering inherently stable formulations, energy-consistent representations, and modular plug-and-play scalability. However, the practical deployment of PH-based stability analysis remains hindered by the absence of reliable, high-fidelity parameter identification methods that rely on sensor measurements to capture system dynamics while remaining compatible with PH model structures. This paper addresses that gap by proposing a comprehensive three-stage data-driven identification framework for PH modeling of synchronous generators—the central dynamic component of any power system. While the IEEE Standard 115 provides established procedures for transient parameter identification, it exhibits fundamental limitations when applied to PH modeling, including single-scenario identifiability constraints, noise-sensitive derivative-based formulations that amplify sensor measurement errors, and the inability to decouple generator-internal damping from grid contributions. The proposed framework resolves these limitations through multi-scenario excitation using sensor-acquired voltage and current signals, derivative-free state consistency optimization, and physics-based regularization that enforces PH structure preservation. Complete identification of eight key parameters (H, D, $X_d$, $X_q$, $X_d^{\prime }$, $X_q^{\prime }$, $T_{do}^{\prime }$, $T_{qo}^{\prime }$) is achieved with errors ranging from 1.26% to 9.10%, and validation confirms RMS rotor angle errors below 1.2° and speed errors below 0.15%, demonstrating suitability for transient stability analysis, passivity-based control design, and oscillation damping assessment. Full article

This study investigates the transient wheel–rail contact mechanics of welded joints in heavy-haul rails via a validated 3D finite element model, and analyzes the stick-slip behavior, dynamic response and elastoplastic characteristics in the base material zone, heat-affected zone and weld bead zone. Results show a distinct contact state transition from stick-slip in the base material to predominant slip within the welded zones, indicating higher wear susceptibility. Dynamic response analysis reveals the highest and lowest contact-point acceleration amplitudes in the base material and heat-affected zone, respectively, due to material heterogeneity. Plastic deformation consistently initiates at the rail surface, where stress and strain concentrate, establishing it as the primary site for damage nucleation. A systematic parametric study shows that plastic deformation can be effectively mitigated by increasing the yield strength and elastic modulus of the welded joint material, or reducing the wheelset velocity, unsprung mass and wheel–rail friction coefficient. In contrast, adjusting the primary suspension and fastener parameters exerts a negligible influence on plastic deformation control. These findings provide a mechanistic basis for optimizing the performance and maintenance of welded joints in heavy-haul rail operations. This study reveals the coupling law of multiple mechanisms among contact behavior, dynamic response and material failure during the damage initiation process of rail welded joints from the mechanistic perspective, which provides a theoretical basis for the structural optimization, condition assessment and maintenance of rail welded joints in heavy-haul railways. Full article

DC motors are widely used in mechatronic systems; however, their performance degrades significantly in the presence of nonlinear mechanical loads, parameter variations and sensing uncertainties. This paper proposes three control strategies (i.e., PID, optimal, and hybrid controllers) for discrete-time DC motor systems to overcome the disturbances caused by nonlinear mechanical loads and parameter variations. Optimal control of nonlinear discrete-time systems is formally characterized by the Hamilton–Jacobi–Bellman (HJB) equation, whose analytical solution is generally intractable. To address this challenge, a learning-based optimal control strategy based on the Heuristic Dynamic Programming (HDP) framework is developed to approximate the HJB equation, supported by a formal convergence proof. For that purpose, Neural Networks (NNs) are employed to approximate both the cost function and the optimal control policy, enabling near-optimal performance with manageable computational complexity. Although the resulting optimal control achieves fast convergence, it may introduce overshoot and steady-state offset under nonlinear disturbances. To address this limitation, a hybrid control framework is proposed, where nonlinear optimal corrections are integrated with the robustness and adaptability of Proportional–Integral–Derivative (PID) control through error-dependent gating and gain-scheduling mechanisms. A structured evaluation framework is conducted, including nominal analysis, motor-parameter stress testing across nine nonlinear scenarios, controller-design sensitivity analysis, and stochastic measurement-noise assessment under filtered sensing conditions. Results demonstrate that the hybrid controller preserves transient speeds within 5–10% of the optimal controller while effectively eliminating overshoot and steady-state offset under nominal conditions. The hybrid design reduces the accumulated tracking error by more than 95% compared to the optimal controller, while incurring only negligible additional control effort. Under aggressive supply-sag disturbances, the hybrid controller significantly limits peak deviation and reduces accumulated tracking error by over 90%, while maintaining comparable control cost. Overall, the hybrid framework provides a convergence-proven and practically deployable control solution that combines near-optimal convergence speed with robust, overshoot-free performance for intelligent motion-control and robotics applications. Full article

Secondary succession in tropical forests is traditionally described as a linear process driven by time since disturbance. However, growing evidence suggests that recovery pathways depend strongly on historical and environmental contexts. We evaluated how disturbance legacies and edaphic constraints interact to shape successional trajectories in a tropical karst landscape of the Maya Forest, Mexico. We sampled 100 plots along a chronosequence, quantifying vegetation structure, floristic diversity, biomass (NDVI), disturbance legacies, and soil properties. Using unsupervised clustering (K-means) and multivariate ordination, we identified four contrasting ecological typologies that represent distinct successional states rather than transient stages. Our results show a pronounced dichotomy in vegetation dynamics following the abandonment of land-use practices: while some sites are experiencing diverse development due to positive forest legacies (Typology B), others remain stalled (Typology C), dominated by lianas, where biotic barriers inhibit tree regeneration despite decades of abandonment. Additionally, we documented an asynchronous recovery between floristic recovery and vertical development; in sites with edaphic constraints, forests reach high diversity and biomass but exhibit stunted growth (Typology D). This suggests that severe abiotic constraints—specifically high rockiness and shallow soils—limit the dominance of highly competitive species, thereby acting as a filter that maintains high levels of diversity despite structural limitations. Edaphic analysis confirmed that chemical fertility and physical constraints (rockiness and shallow depth) act as orthogonal filters. This explains the persistence of structurally constrained yet functionally mature forests as stable, edaphically determined outcomes. Overall, secondary succession in tropical karst is nonlinear and path-dependent, governed by a hierarchical filtering model where historical land use dictates community identity and physical substrate limits structural architecture. These findings highlight the need for trajectory-specific management and the abandonment of uniform expectations of forest recovery in karst landscapes. Full article

Under high-altitude, low-Reynolds-number conditions, flow instability in confined dual-fan configurations severely limits the propulsion and thermal management efficiency of heavier-than-air aircraft. This study establishes a high-fidelity 3D transient numerical model using curvature-corrected shear stress transport (SST) turbulence modeling, integrated with proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and nonlinear stability analysis to investigate rotational direction control mechanisms. Results indicate that co-rotating configurations trigger intense low-frequency pulsations and significant flow skewness due to wall-adhesion effects. Conversely, the counter-rotating layout reconstructs vortex topology by forming a strong interaction shear layer, which enhances local momentum exchange and suppresses large-scale coherent structures. While counter-rotation exhibits a higher initial growth rate, its significantly enhanced nonlinear aerodynamic damping forces the flow into a low-amplitude quasi-steady state, reducing inlet non-uniformity by 74% and increasing mass flow by 5.19%. These findings clarify the physical mechanisms of vortex interference in regulating stability and provide critical design insights for optimizing compact propulsion systems in heavier-than-air high-altitude platforms, such as long-endurance UAVs. Full article

Monitoring peptide and protein self-association is essential for understanding biological function, formulation stability, and aggregation mechanisms. While size-exclusion chromatography (SEC) is routinely used to quantify protein-size variants under native conditions, its hyphenation to high-resolution mass spectrometry (HRMS) for simultaneous structural characterization remains limited. Here, we report the development and validation of a robust SEC-UV/HRMS method optimized for native-like analysis of bovine serum albumin (BSA) monomers and higher-order oligomers using standard-flow electrospray ionization. Systematic evaluation of source parameters, mobile-phase composition, and chromatographic conditions enabled retention of native BSA structure, minimized in-source unfolding, and enhanced MS sensitivity, allowing detection of oligomers up to the heptamer. A short, narrow-bore 200 Å UHPLC SEC separation column was used. Low-flow separations (~0.05 mL/min) enabled efficient ionization and 10 min run times. An accelerated 60 °C stress-testing protocol demonstrated that SEC-MS can semi-quantitatively monitor oligomerization dynamics, complementing UV-based quantification and revealing transient species not resolved by UV alone. The method showed acceptable linearity, precision, and sample stability, and comparison with SEC-RALS/LALS confirmed molecular-weight trends across aggregation states. Overall, the developed SEC-UV/HRMS workflow provides a rapid, sensitive, and widely accessible approach for UV-based quantification of monomer- and HRMS-based characterizing protein aggregation in research and quality control in pharmaceutical laboratories. Full article

High-precision trajectory tracking of robotic manipulators is fundamentally challenged by strong nonlinear dynamics, unmodeled uncertainties, and external disturbances. This paper proposes a Reciprocal Neural State–Disturbance Observer (RNSDO) featuring a neural activation mechanism for adaptive gain modulation and a reciprocally coupled state–disturbance estimation architecture. By reshaping the observer error dynamics through mutual feedback between state and disturbance estimation, the proposed structure alleviates the conflict between fast transient disturbance reconstruction and steady-state noise suppression, while requiring only position measurements. A decentralized position controller is designed based on RNSDO. The global asymptotic stability of the resulting closed-loop system is rigorously established via Lyapunov analysis. Extensive simulations on a PUMA 560 and experiments on a 7-DOF Franka FR3 robotic manipulator demonstrate highly consistent performance trends. The proposed method achieves improved state and disturbance estimation accuracy and enhanced robustness against unmodeled dynamics and payload variations compared with a linear Improved Extended State Observer (IESO), a classical Nonlinear Extended State Observer (NLESO), and a model-based Nonlinear Disturbance Observer-based Adaptive Robust Controller (NDO-ARC). Furthermore, the algorithm exhibits excellent real-time feasibility with a minimal computational footprint. Full article

Molecular interactions underpin the functioning of the living cell. Molecules exist in distinct quaternary structural forms, associate with molecular partners in signaling cascades, form transient quinary interactions, localize in membrane domains, and cluster in membrane-less condensates. Measuring the concentration, size, and dynamics of these molecular assemblies remains an enduring biophysical challenge, particularly in cells, where heterogeneity is the rule rather than the exception. Orthogonal signals derived from fluorescence lifetime, fluorescence fluctuations, and fluorescence polarization provide valuable metrics for probing interactions and environments, concentration and size, and rotational dynamics, respectively. This paper combines fluorescence lifetime imaging microscopy with image correlation analysis and polarization to determine the concentrations, brightness, lifetime, and rotational correlation time of different fluorescent states. A two-population model is examined as a prototypical example of a heterogeneous system. The analysis is illustrated on a simple fluorescence model system, where cluster densities, relative brightnesses, lifetimes, and rotational correlation times are extracted. Full article

The study examined how season, age, sex, and pregnancy outcomes influenced serum total thyroxine (TT4) and triiodothyronine (TT3) levels in killer whales (Orcinus orca). Total T4 and TT3 concentrations were quantified in 1513 serum samples collected voluntarily over ~40 years from 14 males and 24 females (ages 1–54) under managed care. Data were analyzed using LMM to determine the effects of age, sex, season, and pregnancy status (normal vs. abnormal outcomes). Age, season, and pregnancy significantly influenced thyroid hormone concentrations, while sex did not. Juveniles exhibited higher concentrations consistent with increased thermoregulatory needs and growth demands. Seasonal analysis showed TT4 peaked in summer and declined in winter suggesting thermoregulatory adaptation. Pregnancies with abnormal outcomes (abortion, dystocia, stillbirth) were associated with atypical thyroid hormone profiles; specifically, dystocia was linked to consistently low TT3/TT4, while stillbirths correlated with elevated late-term TT3. Females experiencing abortion showed decreased TT3 and TT4 during the late gestation. These findings suggest that in pregnancies with adverse outcomes, metabolic imbalances or transient hyperthyroid-like states may negatively impact fetal health. Consequently, in killer whales, variation in thyroid hormone levels may reflect a complex interplay between environmental adaptation, reproductive status, and underlying evolutionary physiology. Full article

Background/Objectives: HBeAg-positive chronic hepatitis B (CHB) patients with very high viral replication are often clinically considered a homogeneous, low-risk population. However, substantial biochemical, virological, and fibrosis-related heterogeneity may exist. This study aimed to characterize this heterogeneity in treatment-naive, HBeAg-positive CHB patients with ultra-high viral loads (HBV DNA ≥ 7.0 log₁₀ IU/mL). Furthermore, we sought to identify predictors of significant fibrosis and detect clinically relevant discordant phenotypes, such as silent disease progression despite normal alanine aminotransferase (ALT) levels. Methods: This single-center, retrospective, cross-sectional study analyzed consecutively screened eligible patients. A liver stiffness measurement (LSM, kPa) and controlled attenuation parameter (CAP, dB/m) were obtained via transient elastography. Significant fibrosis was defined as an LSM ≥ 7.0 kPa. Statistical evaluations included Spearman’s correlation, multivariable regression, ALT-LSM stratification, and K-means clustering. Results: Among 413 included patients, age and aspartate aminotransferase (AST) emerged as independent risk factors for significant fibrosis, whereas log10 HBV DNA and log10 HBsAg were independent negative predictors. Patients with HBsAg ≥ 25,000 IU/mL exhibited significantly lower LSM values than those with lower HBsAg levels. Notably, 18.4% of patients with strictly normal ALT (≤40 U/L) presented with an LSM ≥ 7.0 kPa, indicating silent progression. Cluster analysis further identified two distinct patient phenotypes characterized by differing age, ALT, viral load, and fibrosis profiles. Conclusions: An ultra-high viral load in HBeAg-positive CHB does not guarantee a uniformly benign clinical state. By quantifying biochemical, virological, and fibrotic heterogeneity, this study highlights a critical subgroup with silent fibrosis progression that risks being overlooked by ALT-based assessments alone. Full article

Mulberry (Morus alba) is an economically important forest tree species, yet cutting propagation is constrained by low adventitious rooting efficiency. Although ABT, a composite rooting promoter, can improve cutting survival, its molecular basis remains unclear. Here, cuttings of the cultivar Qiangsang 1 were treated with ABT, NAA, or IAA (200–1000 mg/L) and subjected to transcriptome profiling to elucidate how ABT enhances rooting. Hormone-related analyses showed that ABT upregulated GH3 (auxin-amido synthetase) at days 0 and 20, implicating auxin homeostasis. ERF1/2 (ethylene response factors) exhibited a temporal oscillation, with induction at day 10 followed by repression from days 20 to 30, consistent with a shift from developmental programs to defense-related processes. In parallel, JAZ (jasmonate ZIM-domain) genes were downregulated at day 0 and subsequently upregulated; together with CYP94C1, these changes may attenuate jasmonate-associated defense signaling. For cell remodeling and defense coordination, ABT reduced the expression of genes associated with cell-wall rigidity while inducing EXPA11 (expansin) at day 20, potentially facilitating root primordium emergence. Meanwhile, PR-1 (pathogenesis-related protein 1) was transiently upregulated at days 0, 20, and 30, and the concomitant modulation of WRKY transcription factors and RPM1 suggests enhanced defense readiness. Integrative network analysis further indicated that a GH3–ERF1/2–PR-1 module links hormonal and defense cues and may activate BAT1 (energy metabolism) and RBOHB (ROS production) to support adventitious root elongation. Collectively, these results suggest that ABT improves rooting efficiency by reshaping hormonal homeostasis and coordinating cell-wall reconstruction with a pre-activated defense state, thereby providing a conceptual framework for balancing root induction and defense responses during vegetative propagation in forest trees. Full article