Search Results (429)

Vehicular platoons under realistic scenarios are prone to actuator saturation, model uncertainties, and external disturbances, which degrade transient tracking and spacing stability. Conventional prescribed-performance control (PPC) strictly requires initial errors to lie within a predefined envelope, while finite/fixed-time schemes cannot directly assign the settling-time bound. To resolve these limitations, this paper proposes a practical predefined-time sliding-mode adaptive platoon control strategy under input saturation constraints. Specifically, a smooth hyperbolic-tangent approximation combined with a mean-value-theorem-based gain formulation is utilized to handle saturation nonlinearity and simplify stability analysis. A novel initial-error transformation is developed to eliminate the stringent envelope constraint on the original initial tracking error. Furthermore, a predefined-time sliding variable and an adaptive compensation mechanism are synthesized to guarantee that tracking errors converge into a bounded neighborhood of the origin within a user-specified time. Numerical simulations and comparisons with predefined-time sliding-mode and PID controllers demonstrate that the proposed strategy eliminates initial error restrictions and suppresses chattering. Compared to the alternative schemes, the proposed method restricts the maximum tracking error within 0.05 m—representing reductions of approximately 77% and 91%, respectively—and shortens the settling time to within 2 s. These results validate its effectiveness for robust cooperative platoon control. Full article

This paper investigates an observer-based secure control method for networked non-Lipschitz nonlinear systems subject to unknown nonlinearities, external disturbances, sensor noises, and intermittent denial-of-service (DoS) attacks. Multi-layer neural networks (MNNs) are adopted to compensate for non-smooth, non-Lipschitz terms, guaranteeing bounded approximation errors. A resilient high-gain observer fused with the MNN is developed to continuously reconstruct system states. When DoS attacks block sensor channels, the observer acts as a virtual dynamic engine to substitute for lost real-time measurements, providing uninterrupted feedback to the controller. Furthermore, to optimize communication efficiency, an observer-based static event-triggered mechanism (SETM) coupled with a hold-input strategy is integrated. Employing the Lyapunov–Krasovskii functional method, sufficient conditions are derived to prove that the closed-loop system remains uniformly ultimately bounded (UUB) under the joint effects of approximation errors, disturbances, and attacks. Simulation results on a two-link manipulator demonstrate that the proposed secure control scheme effectively counters aggressive DoS attacks while achieving a 56.8% reduction in network transmissions compared with conventional periodic sampling paradigms, striking a favorable balance between tracking accuracy and resource efficiency. Full article

Background: Although the gut microbiota is generally recognized to remain relatively stable in healthy individuals, its taxonomic composition still undergoes subtle temporal fluctuations. To systematically characterize these dynamic variations, we adopted “enterotypes” as a macroscopic and practical metric to evaluate the structural dynamics of the intestinal microbial community. Methods: We longitudinally recruited a cohort of healthy adults and collected a total of 72 shotgun metagenomic fecal samples across approximately 40 days. All samples underwent metagenomic sequencing, and subjects were grouped by their predominant enterotypes and longitudinal fluctuation patterns. We evaluated the microbial markers and the longitudinal co-occurrence network topologies of different groups to clarify the potential factors causing gut microbial fluctuations. Results: Longitudinal tracking revealed that those undergoing persistent alterations in microbial communities exhibited diarrhea symptoms, accompanied by markedly greater variability in gut microbiota. The reduction in Alistipes shahii is a potential predictive marker for community instability, exhibiting a cross-validated AUC of 0.824 (95% CI: 0.760–0.888). Furthermore, the co-occurrence network and correlation analysis indicated that fluctuating communities exhibited significantly higher clustering coefficients and denser connectivity among core taxa. Rather than indicating robustness, this dense architecture reflected an increased degree of microbial interdependence within the unstable gut microbial community. Conclusions: This preliminary study discovered candidate bacteria taxa that may serve as indicators of disturbances in the gut microbiota. Furthermore, the hyper-connectivity during continuous fluctuations suggested that increased interdependent microbial relationships meant diminished gut resilience. These results offer a new perspective for detecting early signals of dysbiosis and understanding mechanisms underlying stability of gut microbiota. Full article

Bolted joints are critical load-transfer components in bridges, wind turbines, aerospace systems, mechanical equipment, and offshore platforms, where preload loss can degrade stiffness, accelerate fatigue, and compromise safety. For structural health monitoring, early monitoring of preload reduction before marked loosening is essential, yet existing ultrasonic guided wave indicators remain affected by frequency dependence, non-monotonic responses, amplitude drift, and environmental disturbances. This study proposes an early-warning-oriented preload identification method that combines broadband excitation, multi-frequency narrowband reconstruction, spectral centroid extraction, optimized weighted fusion, and fixed SC-domain linear calibration from one reference loading group. Using a 20–250 kHz Chirp response, 14 narrowband signals from 50 to 180 kHz were reconstructed for an M20 single-bolt specimen tested over 50–90 N·m. The fused spectral centroid index exhibited a stable, monotonic, and approximately linear relationship with preload. When fixed weights and calibration coefficients were transferred to held-out repeated-loading groups, all Pearson correlation coefficients exceeded 0.99. Feature-level robustness tests showed that the arithmetic mean of the spectral centroid reduced temperature-induced Range% by 98.42–99.08% and RSD by 98.89–99.31% relative to energy-based features. This work provides an interpretable multi-frequency spectral descriptor and a calibration transfer framework for repeatable early warning of preload loss in a controlled single-bolt configuration. Full article

Distributed-drive all-wheel steering (AWS) six-axle vehicles possess distinct advantages in power performance, maneuverability, and environmental adaptability. However, when navigating tight curves under sudden low-friction road conditions, their inherent long wheelbase and strong inter-axle coupling typically lead to compromised spatial maneuverability, trajectory decoupling between the vehicle nose and tail, and lateral dynamic instability. To resolve these critical issues, this paper proposes a geometry–dynamics coupled lateral control scheme with adaptive speed planning for six-axle vehicles under confined spatial and low-friction conditions by seamlessly fusing a dual-point preview mechanism with multi-mode steering mappings. First, a three-degree-of-freedom nonlinear vehicle dynamic model incorporating longitudinal, lateral, and yaw motions is constructed, alongside the formulation of extended Ackermann kinematic steering manifolds for three distinct modes: rear-axle steering, center steering, and crab steering. To rectify the kinematic under-constrained deficiency inherent in conventional single-point preview path-tracking architectures, a joint front-and-rear dual-point preview constraint mechanism is established. This framework permits the quantitative derivation of a spatial geometric reconstruction method for the instantaneous center of rotation (ICR), which algebraically maps the ideal ICR trajectory requirements onto the physical constraints of the selected steering modes. Consequently, complete geometric constraints on both the front and rear trajectories are achieved, enabling active compression of the vehicle’s turning radius. Furthermore, to handle sudden low-friction disturbances, road adhesion limits and vehicle lateral stability boundaries are explicitly incorporated to design a multi-scale adaptive preview distance dynamic scaling mechanism driven by dynamic safety margin corrections. By adaptively scaling the spatial constraint at the geometric layer, this mechanism proactively mitigates nonlinear tire sideslip force saturation via feedforward action, thereby preventing tracking divergence and catastrophic sideslip instability under physical adhesion limits. Co-simulations based on the high-fidelity TruckSim-Simulink platform demonstrate that, in standard curves, the proposed dual-point preview manifold fusion strategy reduces the minimum turning radius by 9.6–10.1% and shortens the cornering transit time by 7.5% compared with the traditional single-point preview mechanism. By actively constraining the front and rear trajectories, the trajectory decoupling between the vehicle nose and tail is effectively resolved. Under narrow-lane scenarios, the maximum lateral error is restricted within 0.78 m, representing a 37.6% reduction relative to the single-point preview, while the maximum steering angle of the front axle is compressed by approximately 18%, thereby significantly improving spatial passability and preventing intermediate body interference. Most notably, under low-friction surface disturbances, the dynamic-margin-corrected adaptive preview adjustment mechanism exhibits remarkable robustness, constraining the maximum lateral tracking error to within 0.68 m. The proposed geometry–dynamics coupled lateral control strategy successfully elevates the tight-curve maneuverability of heavy transport vehicles while concurrently reinforcing their lateral dynamic stability under limit combined spatial and adhesion constraints. Full article

Carbon has increasingly been incorporated into economic and financial architectures as a tradable commodity within contemporary climate governance. Yet, carbon is not produced, stored, or mobilized in abstract space; it emerges from territorially specific land-use systems, ecological processes, and socio-spatial trajectories. This study examines carbon as a territorial commodity by analyzing long-term land-use and land-cover (LULC) dynamics in the municipality of Alegrete, located in the Brazilian Pampa biome, between 1985 and 2024. Based on MapBiomas Collection 10, and using cloud-based processing in Google Earth Engine combined with reproducible statistical workflows in R, the analysis identifies structural land-use trajectories shaping carbon-relevant territorial conditions. Results reveal a strong contraction of native grasslands, corresponding to approximately 17.8% of the municipal territory and a 24.2% reduction relative to the 1985 grassland area, alongside the expansion of mechanized agriculture, particularly soybean cultivation (+10.8% of the territory; +1343% relative to 1985 soybean area), and the consolidation of flooded rice systems (+7.6% of the territory; +146% relative to 1985 rice area). Rather than estimating carbon stocks or fluxes, the study establishes a territorial baseline linking land-use trajectories to key carbon-relevant processes, including soil carbon stability, disturbance intensity, permanence constraints, and multi-gas trade-offs. From a historical–structural perspective, these trajectories contrast with prevailing policy narratives and market-based instruments that assume an expanding carbon sequestration capacity, revealing a governance gap between valuation mechanisms and land-use realities. By conceptualizing carbon as a territorially embedded economic asset linked to land-use trajectories, the article contributes to interdisciplinary debates on climate governance, MRV integrity, environmental valuation, and the structural limits of market-based environmental instruments. Full article

This paper presents a control framework that integrates adaptive sliding mode control (ASMC), time-delay control (TDC), and admittance filtering to achieve robust force and position tracking in robot manipulators. TDC is employed to estimate unmodeled dynamics using delayed measurements, while ASMC enhances robustness by compensating for time-delay estimation (TDE) errors and mitigating chattering effects. An adaptive law incorporating a decline-rate reduction factor is introduced to explicitly regulate the decay of the adaptive gain inside the boundary layer, thereby preserving compensation capability against time-delay estimation errors and external disturbances for a longer duration while improving position tracking performance. In addition, the admittance mechanism converts force-tracking errors into position correction signals, enabling force tracking without modifying the underlying position control structure. The stability of the closed-loop system is analyzed based on Lyapunov theory, ensuring bounded tracking performance in the presence of estimation errors and uncertainties. Simulation results demonstrate that the proposed method improves position tracking accuracy—reducing the root mean square error (RMSE) from 0.0522 mm to 0.019 mm—while maintaining reliable force tracking performance. Full article

The increasing demand for clean, reliable, and sustainable energy has intensified the need for advanced control strategies in modern wind energy conversion systems. Although conventional backstepping control (BC) offers strong stability and robustness, its performance may deteriorate under parameter uncertainties and dynamic operating conditions, leading to power fluctuations and reduced energy quality. To overcome these challenges, this study proposes an intelligent fuzzy fractional-order BC (FFOBC) strategy for multi-machine wind energy systems. By integrating fuzzy logic with fractional-order calculus into the classical BC framework, the proposed approach enhances adaptability, dynamic response, and robustness against system disturbances and nonlinearities. The controller is implemented at the machine-side inverter and validated in MATLAB/Simulink under varying wind and load conditions. Comparative results demonstrate that the proposed FFOBC significantly outperforms conventional sliding mode control in terms of overshoot reduction, steady-state accuracy, response smoothness, and total harmonic distortion minimization. Furthermore, the proposed strategy improves energy conversion efficiency, reduces mechanical and electrical stress, and ensures stable power injection into the grid. These findings highlight the potential of the proposed intelligent control framework to support sustainable, resilient, and high-quality wind energy integration in future smart power systems. Full article

Land use/land cover (LULC) is one of the core driving factors affecting terrestrial ecosystem carbon storage and exacerbating global warming. As an area with the most intense land–sea interactions, China’s coastal zone has experienced drastic LULC transition and carbon storage fluctuations during the rapid urbanization process. Based on the InVEST model, this study analyzes the spatiotemporal dynamics of LULC and carbon storage (CS) in China’s coastal regions from 2000 to 2024, and simulated multi-scenario carbon storage trajectories for 2050 integrating the SSP-RCP scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, the XGBoost-SHAP and generalized additive models (GAMs) were introduced to deeply analyze the nonlinear characteristics and temporal heterogeneity of the driving mechanisms of CS evolution. The results show the following: (1) During the study period, the LULC structure of the coastal region was dominated by cropland and forestland consistently accounting for over 85%, but exhibited a competitive pattern characterized by the continuous expansion of built-up land severely squeezing ecological spaces. (2) The total regional CS showed an overall phased downward trend, accompanied by increasing fragmentation of high carbon sink areas. Notably, as the core carbon pool, the reduction in forest area was the dominant factor causing regional net carbon losses. (3) CS remained relatively stable under SSP1-2.6, representing a sustainable development pathway with low greenhouse gas emissions. In contrast, SSP2-4.5, SSP3-7.0, and SSP5-8.5 exhibited more pronounced declines in carbon storage by 2050, indicating that SSP1-2.6 is the most favorable pathway for maintaining long-term carbon storage stability in China’s coastal regions. (4) The driving mechanism of CS has undergone a profound shift from being dominated by natural ecological baselines to human activities. Land use intensity (LUI) has emerged as the strongest predictor in the model, and the nonlinear impacts of human activities have grown increasingly complex over time. This study highlights the complex impacts of high-intensity human disturbances on the coastal carbon cycle, providing a scientific basis for formulating differentiated carbon management strategies and adaptive spatial land-use planning oriented toward the “Dual Carbon” goals. Full article

Nature provides efficient strategies for fluid transport and thermal regulation through evolved structural features. This review summarizes recent progress in biomimetic thermal–fluid structures for enhancing fluid flow and heat transfer, with emphasis on the links among biological inspiration, engineering geometry, transport mechanisms, and application performance. Representative designs are classified into tree-like branching and fractal networks, compact hexagonal layouts, and bio-inspired curved morphologies, including riblets, grooves, fins, fluctuating channels, and TPMS structures. Their enhancement mechanisms involve flow redistribution, boundary-layer disturbance, secondary-flow and vortex generation, local acceleration, enlarged heat-transfer area, drag reduction, and compact flow organization. Applications using biomimetic structures are assessed in detail, such as in battery thermal management, electronic cooling, etc. The reviewed studies indicate that biomimetic structures can improve temperature uniformity, suppress hotspots, and enhance thermohydraulic performance, but the gains may be accompanied by pressure-drop or pumping-power penalties. Therefore, coupled thermal–hydraulic evaluation is essential for objective comparison. Key challenges of practical usage are identified in mechanism-based design, manufacturability, reliability, etc. This work establishes the guidance for translating biological forms into practical thermal–fluid structures with balanced efficacy. Full article

Autonomous navigation of a vehicle in an environment where there are obstacles is difficult due to low onboard sensing technology, high measuring noise, and external interference, which collectively result in poor tracking performance of the vehicle’s trajectory and compromise safety. In this paper, a UAV-assisted Distributed Sliding Mode Control (DSMC) is proposed to robustly and safely implement path tracking for autonomous ground vehicles (AGVs). The proposed system utilizes an aero-sensor layer for enhanced perception, such as obstacle sensing, reference path preview, and look-ahead trajectory information, and it shares this information with the vehicle via wireless communication. The fundamental scheme, called DSMC, is based on a conventional Sliding Mode Control (SMC) technique and uses UAV preview-based feedback. This allows anticipation of control actions to enhance tracking performance and achieve more timely, smoother obstacle avoidance than baseline SMC. The proposed method is designed to overcome the limitations of traditional SMC strategies, such as chattering and poor responsiveness. The proposed method features continuous nonlinear approximation and damping mechanisms to reduce chattering and improve response characteristics, thereby enhancing stability and reducing oscillations. Strict safety enforcement through constraint is always achieved by keeping the vehicle and obstacles separated by a minimum distance only; that is, a minimum distance is always guaranteed: a Constraint Barrier Function (CBF)-based constraint is used. By combining UAV-assisted perception with DSMC and CBF the system can guarantee its formal safety in the presence of disturbances and sensing uncertainties while maintaining accurate trajectory tracking. Based on our simulation results, the proposed UAV-assisted DSMC method is shown to be significantly superior to conventional SMC and Model Predictive Controller (MPC) in terms of tracking accuracy, control smoothness, and adherence to the safety margin. Our simulation results demonstrate that the proposed method significantly outperforms conventional SMC and MPC control. Specifically, it achieves a 22.9% reduction in RMSE (0.135 m vs. 0.175 m) and 63% lower mean control effort, and it strictly maintains the minimum safety distance under both static and dynamic obstacles. The algorithm runs in real-time with an average execution time of 1.85 ms (>200 Hz), making it highly suitable for embedded deployment. These results highlight the effectiveness of combining UAV-assisted preview, adaptive robust control, and formal safety constraints for reliable autonomous navigation in complex environments. Full article

Introduction: The rationale of traditional toxicity assessments, such as fish lethality tests (e.g., OECD TG 203), is to use forced exposure tests to characterise the ecotoxicity of chemicals by deriving concentration–response relationships based on observed physiological effects to estimate environmental risks. This approach assumes that physiological mechanisms, such as detoxification, are the main means that organisms use to minimise contamination effects. However, non-forced exposure approaches, where organisms can freely move along a contamination gradient, show that mobile species like fish can avoid adverse contaminant levels and escape to favourable areas. As populations are exposed to disturbed habitats, direct ecosystem-level effects may occur through population downsizing, even in the absence of individual suffering. Contaminants may thus act as habitat disturbers, regulating fish dispersion patterns by provoking emigration from contaminated areas at concentrations well below lethal levels. Spatial avoidance responses therefore align with a key priority in environmental risk assessment (ERA): progressing beyond standard tests to gain ecological realism when assessing impacts on biodiversity, habitats, ecological processes and recovery. Objective: To increase ecological relevance in ERA while halting animal distress, pain and suffering. This study reviews existing data on fish avoidance tests, with the ultimate goal of discussing their value and fostering their implementation as an ecologically relevant and more humane alternative to fish lethal testing in chemical ERA. Methodology: This review analyses results from studies using two main non-forced multi-compartment exposure systems: linear systems and the bi-dimensional HeMHAS (Heterogeneous Multi-Habitat Assay System), compared with traditional forced exposure tests. Conclusions: Spatial avoidance is generally triggered after short exposure periods (5 min to 48 h) at concentrations causing no mortality. Fish populations may therefore become locally extinct before any deaths occur, as individuals promptly emigrate without physiological impairment. The simplicity of experimental design provides strong potential for standardisation and routine implementation in ERA. Fish avoidance tests represent a key ecologically relevant tool at ecosystem and landscape levels and support the 3Rs (replacement, reduction and refinement) as well as the new 3Rs (reproducibility, relevance and regulatory applicability), helping reduce uncertainty in chemical assessment, as urged by many EU legislations. Full article

Cold-chain supply systems depend on a sequence of linked production and logistics decisions. In prepared-food cold chains, quality may deteriorate not because one visible failure occurs, but because testing, traceability records, temperature monitoring, and abnormal-condition reporting are gradually weakened under cost pressure. Once such hidden effort reduction accumulates, external disturbances may push the system from strict assurance to weakened governance. To explain this nonlinear process, an asymmetric evolutionary game is built between prepared-food producers and cold-chain logistics providers, each choosing between strict and weakened quality assurance. White Gaussian noise is introduced to represent random operating shocks, and the two-population strategy system is projected onto a system-level quality-governance coordinate, $q$. This projection is used as a transparent baseline coordinate rather than as an assumption of linear system evolution. The reduced system is then transformed into a stochastic cusp catastrophe model, with a resilience indicator used to measure the distance from critical transition conditions. Numerical simulations show that quality assurance costs and short-term cost-saving benefits move the system toward a weakened-governance basin, whereas external incentives, coordination degree, and credible accountability mechanisms support recovery toward strict collaboration. The framework offers a scenario-based resilience diagnosis approach for identifying threshold effects in cold-chain quality governance. Digital traceability, temperature-data sharing, incentive alignment, and accountability rules are further interpreted as operational innovations that improve resilience and reduce avoidable quality losses in sustainable cold-chain operations. Full article

Digital isolation represents a contemporary paradox in which increased connectivity through social media and digital technologies does not necessarily translate into improved social integration or psychological well-being. This review synthesizes current evidence on the relationship between digital environments and mental health, with a focus on mechanisms underlying loneliness, anxiety, depression, and related outcomes. The findings indicate that problematic and passive use of social media—particularly when associated with social comparison processes and Fear of Missing Out (FoMO)—is consistently linked to increased levels of depressive symptoms, anxiety, sleep disturbances, and reduced well-being. At the same time, the evidence highlights substantial heterogeneity, suggesting that the impact of digital technologies is moderated by user characteristics, age, patterns of engagement, and psychosocial context. Importantly, digital technologies may also serve compensatory and protective functions by facilitating social support, especially in conditions of objective isolation. Key mediating mechanisms include cyberbullying, social exclusion, emotional contagion, and internalization of body image standards. The concept of “digital loneliness” emerges as a useful framework for understanding the discrepancy between constant connectivity and perceived relational insufficiency. Practical implications emphasize the need for targeted interventions focusing on digital literacy, healthy usage patterns, and psychosocial support rather than simplistic reduction in screen time. Overall, digital isolation should be conceptualized as a qualitative dysfunction of mediated social interaction rather than a purely quantitative effect of technology exposure. Full article

Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration and exploitation and avoiding local optima. To deal with this issue, this paper proposes a new method called the Blackcap Optimization Algorithm (BCOA), which is inspired by the navigation and migration behavior of Blackcap birds. Instead of using complicated distance calculations, the proposed method is based on angular movement vectors. The movement of each search agent is controlled by an angle-based mathematical model that combines the global best angle, a successful neighboring angle, and an adaptive exponential disturbance factor. In addition, the algorithm uses a quasi-genetic path transition mechanism to combine successful parent paths together, along with a territorial competition stage. This structure helps reduce computational cost and improves the balance between exploration and exploitation. The performance of the proposed algorithm is tested on 32 benchmark functions and seven engineering and network optimization problems. The simulation results show that BCOA has a good ability to avoid local optima and can achieve acceptable convergence speed and cost reduction compared to several existing methods. Full article