Search Results (3,255)

Unmanned spherical robots are autonomous mobile platforms with a fully enclosed spherical shell, providing high stability and strong adaptability to complex terrains. However, existing pendulum or flywheel spherical robots often suffer from limited maneuverability, whereas complex hybrid actuation schemes tend to compromise system stability. To address these issues, this study proposes an improved pendulum-driven spherical robot with a mechanically decoupled actuation design, integrating a pendulum system and a circular gear rack turning mechanism. This design enables smooth linear rolling as well as rapid in-place rotation, significantly enhancing maneuverability and motion flexibility on complex terrains. A dynamic model of the spherical robot is established to describe the decoupled actuation mechanism, and a fuzzy proportional–derivative (PD) control strategy is designed for rolling and steering control. Simulation and prototype experiments were conducted to evaluate trajectory tracking, steering response, and terrain adaptability. The results demonstrate that the proposed spherical robot achieves path following and in-place turning with robust mobility. Full article

Rational design of high-performance viscosifying polymers is critical for enhancing supercritical CO₂ flooding efficiency in enhanced oil recovery (EOR). Traditional experimental and simulation approaches are limited in exploring the vast design space of polymer architecture, flexibility, and intermolecular interactions. This work presents an integrated machine learning (ML) and mesoscopic simulation framework using Dissipative Particle Dynamics (DPD) to accelerate the development of tailored polymeric thickeners. We systematically investigate synergistic effects of linear and branched polymer blends on solvent viscosity under Poiseuille flow, representative of flow in micro-fractures and pore throats. Key molecular descriptors are varied to generate a comprehensive rheological database. This data trains a deep neural network (DNN) surrogate model linking molecular parameters to macroscopic viscosity. The DNN is coupled with gradient ascent optimization for inverse design, enabling rapid virtual screening of thousands of formulations. A focused case study demonstrates that the star-like architectures with associative cores and semi-flexible backbones outperform linear analogs for supercritical CO₂ viscosity enhancement. The optimal candidate—a four-arm star polymer with linear side chains—was validated by DPD simulation. This multiscale “simulation-to-surrogate” methodology bridges molecular design with continuum-scale flow behavior, offering a transformative tool for formulating cost-effective, efficient, and sustainable next-generation EOR chemicals. Full article

Accurate remote sensing scene classification is essential for large-scale Earth observation but remains challenging due to significant inter-class similarity and complex spatial layouts in medium- and low-resolution imagery. Conventional convolutional neural networks (CNNs) effectively capture local structural patterns but struggle to model long-range semantic dependencies, whereas Vision Transformers excel at global context modeling yet often show reduced sensitivity to fine-grained spatial structures. To address these limitations, we propose CrtNet, a structure-aware Cross-Model Residual Transformer Network that establishes a dual-stream collaborative architecture integrating convolutional structural representations with Transformer-based semantic modeling through gated residual cross-model interactions. In this framework, a convolutional branch first extracts stable local structural features with strong spatial inductive biases. These features are continuously injected into the Transformer encoding process via residual cross-model connections, enabling persistent structural guidance during global attention modeling. In addition, a sample-adaptive dynamic gating mechanism is introduced to flexibly balance structural and semantic features during prediction. Extensive experiments conducted on two public remote sensing benchmarks, EuroSAT and UCM, demonstrate that CrtNet consistently outperforms representative CNN-based, Transformer-based, and hybrid state-of-the-art models, particularly in visually ambiguous scene categories. Full article

With the growing applications of maritime unmanned systems in environmental monitoring, ocean patrol, and emergency response, achieving efficient multi-platform cooperation in complex and dynamic marine environments remains a critical challenge. Unmanned Aerial Vehicles (UAVs) provide flexible and high-coverage sensing capabilities but are constrained by limited energy capacity, whereas Unmanned Surface Vehicles (USVs) offer long endurance and can serve as mobile platforms and energy supply nodes. Existing studies mostly focus on single-factor optimization, lacking a systematic analysis of the coupled relationships among energy, information (communication and positioning), and task decision making. To address this problem, this paper proposes an Energy–Information–Decision Coupling Optimization Method for Cooperative Maritime Unmanned Systems. A unified coupling model is established to integrate task completion, energy consumption, communication delay, and replenishment scheduling into a multi-objective optimization framework. A bi-level optimization algorithm is designed: the upper layer optimizes USV trajectories and energy supply strategies, while the lower layer optimizes UAV path planning and task allocation. A closed-loop adaptive mechanism is incorporated to achieve optimal cooperation under dynamic tasks and energy constraints. Extensive simulations combined with real-world experimental data are conducted to evaluate the method in terms of mission efficiency, energy balance, communication latency, and system robustness, with ablation studies quantifying the contribution of the coupling module. Results demonstrate that the proposed method significantly outperforms non-coupled or single-factor optimization strategies across multiple performance metrics: it achieves a task completion rate exceeding 93%, reduces total energy consumption by approximately 6% and replenishes waiting latency by over 28% compared with the decoupled baseline method. This effectively enhances the cooperative efficiency and robustness of maritime unmanned systems, and provides theoretical and methodological guidance for large-scale, complex ocean missions. Full article

In emergency communication networks, service demands and user mobility change dynamically. Low service rates and limited coverage are significant challenges that hinder the effectiveness of emergency services. Due to the flexibility, low deployment cost, and adjustable coverage range of unmanned aerial vehicles (UAVs), UAV-assisted emergency communication networks can serve as a viable method to address these challenges. Given the strong coupling between UAV trajectory optimization and resource allocation, joint optimization is crucial to meet dynamic service demands and user mobility. In this paper, we establish a user mobility model based on the Maxwell–Boltzmann distribution and a service model based on the Poisson process. We formulate an optimization problem to maximize the data transmission rate of emergency services. To address the challenges of high-dimensional continuous action spaces, we propose a shared feature extraction-enhanced PPO (SPOR) algorithm for joint trajectory optimization and resource allocation. Simulation results show that the proposed SPOR algorithm significantly outperforms benchmark methods. Specifically, it achieves at least a 20% improvement in data transmission rate, a 28% improvement in emergency communication service ratio, and a 12% reduction in average service distance. Full article

Symmetry plays a crucial role in balancing production efficiency and energy consumption within distributed manufacturing systems. This study leverages symmetric decision-making structures in resource allocation and maintenance scheduling to achieve an equilibrium between productivity and sustainability. To address the multi-factory collaboration requirements for large-scale orders, a distributed dual-resource flexible job shop scheduling model considering multiple speeds and preventive maintenance on energy consumption is constructed. It aims to minimize the maximum completion time and total machine energy consumption. An artificial bee colony algorithm with adaptive scout bees is proposed to solve the model. An improved decoding method is designed according to the model characteristics to enhance convergence speed. Neighborhood structures based on preventive maintenance and machine speeds are designed, and a dynamic neighborhood search strategy is proposed to improve the local search capability. Three food source generation methods are defined as actions, and Q-learning is employed to dynamically select actions, ensuring population diversity while improving population quality. Extensive experiments are conducted to validate the effectiveness of the improved strategies, and the superiority of the proposed algorithm is verified through performance comparisons with state-of-the-art algorithms. Full article

Understanding the dynamic conformations of proteins is important for rational drug discovery. While molecular dynamics (MD) simulation is the primary tool for this purpose, it is both resource- and time-consuming. Recent advances in deep learning offer an attractive alternative by generating conformational ensembles directly from protein sequences. However, the scope of applying such models to protein dynamics studies remains underexplored. Here, we tested the performance of a representative model, BioEmu, across several tasks related to protein dynamics. Our results show that BioEmu can not only generate multiple conformations but also effectively reproduce fundamental properties including residue flexibility, motion correlations, and local residue contacts. However, it fails to predict a mutation-induced shift in conformational distribution and exhibits a preference for higher-energy conformations over lower-energy ones in some cases, indicating that it does not reproduce a right Boltzmann-weighted ensemble. Furthermore, the BioEmu-generated conformations provide only limited improvement in ensemble docking. These findings delineate the current capabilities and limitations of sequence-based generative models for conformational sampling. Also, they highlight several directions for future development—that further energy-based fine-tuning is needed for tasks related to conformational distributions and atom-level generative model is required to study the intermolecular relationship. Full article

Rotor-flexible casing rubbing can induce strong nonlinear dynamics in rotor systems. This study investigates the harmonic frequency characteristics of a rubbing rotor system with a flexible casing constraint. A nonlinear rub-impact model combined with a finite element-based rotor–casing coupling framework is developed to evaluate system responses under concentric and eccentric configurations. The harmonic components of rotor and casing vibrations are analyzed over a range of rotational speeds. Results show that, under concentric conditions, harmonic frequencies originate from rubbing-induced asynchronous motion. The harmonic sub-frequencies observed in the spectrum correspond to lobed rotor orbits formed during the transition from synchronous to asynchronous motion under continuous rubbing forces. Under eccentric rotor–casing alignment, the vibration spectrum becomes more complex and exhibits frequency clustering. The results provide insight into harmonic generation mechanisms and highlight the role of casing flexibility in rubbing-induced asynchronous motion. Full article

To ensure the safety and structural integrity of composite flexible blades under strong winds, this study investigates the extreme aeroelastic responses of the IEA 15 MW wind turbine blade during an emergency shutdown with pitch system faults. Existing studies often rely on simplified models or one-way coupling; we adopt a bidirectional computational fluid dynamics–finite element method (CFD–FEM) fluid–structure interaction (FSI) framework to examine how wind speed and pitch system faults affect aerodynamic loads, displacement responses, and structural stresses when the blade is shut down in a parked-upwind condition. The results reveal that, under the no-pitch condition, the blade experiences extreme loading, with thrust being approximately 15 times higher and the peak stress being 8.6 times that of the pitch condition. Furthermore, a high frequency of 1.969 Hz emerges, significantly increasing the risk of aeroelastic instability as the wind speed increases or under the no-pitch condition. A stress analysis identified that high stress is mainly located in the main spar region, with the peak stress location shifting closer to the blade root under the no-pitch condition. This study highlights the potential risks of composite flexible blades during shutdowns and provides a reference for structural safety design and targeted monitoring. Full article

Background: Conventional reference intervals (RIs) are typically expressed as fixed limits and may not adequately reflect continuous biological variation across age and sex. Next-generation reference intervals (NGRIs) allow dynamic modeling of laboratory parameters across the lifespan. This study aimed to establish age- and sex-specific NGRIs for routine hematological parameters using large-scale health examination data and to evaluate their temporal stability. Methods: Health examination records were linked with laboratory data, and a relatively healthy reference population was defined based on age (18–80 years), normal body mass index, normal blood pressure, and absence of documented disease history. NGRIs were constructed using generalized additive models for location, scale, and shape (GAMLSS) with the Box–Cox Cole and Green distribution. Age-dependent percentile curves (2.5th–97.5th) were generated using bootstrap resampling (100 iterations). Temporal external validation was conducted in five independent annual cohorts (2019–2023). Results: Age- and sex-dependent distributional patterns were observed across multiple hematological parameters, particularly RBC, HGB, and HCT. Continuous percentile curves demonstrated gradual age-related trends, with more pronounced changes in females after midlife. In temporal validation cohorts, the proportion of individuals classified outside the reference intervals remained consistently below 10% across years and sexes, indicating stable performance. Conclusions: Using large-scale real-world health examination data and a flexible distributional modeling framework, we established stable age-continuous next-generation reference intervals for routine hematological parameters. The proposed approach provides a reproducible strategy for modernizing laboratory reference interval construction and supports broader implementation in population-based laboratory medicine. Full article

To address global carbon reduction demands, oxy-fuel combustion in circulating fluidized beds (oxy-CFB) has emerged as a highly promising carbon capture technology, offering extensive fuel flexibility and facilitating bioenergy with carbon capture and storage (BECCS). However, its commercialization is hindered by significant energy penalties and complex scale-up challenges. This review comprehensively analyzes the fundamental multiphase mechanisms, heat transfer behaviors, and multi-pollutant emission characteristics of oxy-CFB systems, drawing upon multiscale modeling advancements and operational data from pilot to 30 MW_th industrial demonstrations. Replacing air with an O₂/CO₂/H₂O mixture fundamentally alters gas–solid hydrodynamics and char conversion pathways, necessitating active fluidization state re-specification. Despite shifting optimal desulfurization temperatures and introducing recarbonation risks, the technology demonstrates inherent advantages in synergistic pollutant control, including the complete elimination of thermal NO_x. While atmospheric oxy-CFB is technically viable, transitioning to pressurized operation is critical to minimizing system efficiency penalties. Furthermore, integrating oxygen carrier-aided combustion (OCAC) and developing advanced predictive control strategies are essential to managing multi-module thermal inertia and enabling rapid dynamic responsiveness for modern power grids. Full article

The Black Sea harbour porpoise (Phocoena phocoena relicta Abel, 1905) is an endemic cetacean with poorly understood spatial ecology in Bulgarian waters. This study aimed to update knowledge on its distribution, abundance, and habitat use throughout the Bulgarian Exclusive Economic Zone (EEZ). We conducted systematic aerial line-transect surveys in all four seasons between October 2022 and October 2023, combined with distance sampling and MaxEnt habitat modelling. Porpoises were present year-round across the EEZ, with marked seasonal shifts in distribution and habitat preferences. Highest densities were observed in spring, while winter distributions were concentrated offshore. Habitat suitability was dynamic, with key high-use areas identified near Cape Emine and in southern offshore waters near the Turkish border. Overall, about 40% of the EEZ represented high-suitability habitat. These findings provide the first comprehensive, year-round baseline for P. p. relicta in Bulgarian waters, highlighting the species’ flexible habitat use and seasonality. The study was conducted under extraordinary conditions due to regional military activity, which may have influenced porpoise behaviour and spatial patterns. The provided results are critical for designing effective conservation and management measures in the face of both natural and anthropogenic pressures and threats. Full article

Background/Objectives: Management of bipolar disorder is marked by variability in lithium response, with responders constituting a distinct clinical subgroup. Although pharmacogenetic studies implicate polymorphisms in neuroplasticity-related genes (BDNF) and hypothalamic–pituitary–adrenal (HPA) axis regulators (NR3C1), the underlying biophysical mechanisms remain poorly characterized. This study aims to bridge this structural–mechanistic gap by quantifying the atomic-level effects of key lithium-response polymorphisms on protein–protein interaction stability and conformational dynamics. Methods: Variant sequences for BDNF rs6265 and NR3C1 rs56149945 were generated and structurally modeled with SWISS-MODEL. Protein–protein interaction analyses focused on the BDNF–TrkB and NR3C1–FKBP5 systems. Structural alignment and conformational comparisons were performed with ChimeraX and US-align, while interaction energetics were evaluated with PRODIGY and HawkDock. Conformational flexibility was assessed using CABS-flex through RMSF analysis. Results: Structural validation showed acceptable model quality. Binding analyses indicated stronger interactions in the variant complexes. In the BDNF–TrkB complex, binding affinity shifted from −13.8 to −15.1 kcal/mol with an ~8.5-fold lower dissociation constant, while the NR3C1–FKBP5 variant complex shifted from −16.3 to −18.8 kcal/mol with an ~65-fold lower dissociation constant. MM/GBSA calculations supported increased stability, with binding energies changing from −61.98 to −83.91 kcal/mol (BDNF–TrkB) and from −18.88 to −31.25 kcal/mol (NR3C1–FKBP5). Structural superposition showed high conservation of global folds (pruned RMSD 0.779 Å and 0.310 Å; TM-scores 0.753 and 0.967). RMSF profiles were largely overlapping, indicating localized interface adjustments rather than global conformational changes. Conclusions: These findings suggest that lithium-response polymorphisms may modulate protein–protein interaction stability while preserving overall structure, providing a structural framework for exploring genetic influences on lithium treatment response. Full article

To meet the real-time computational requirements of active suspension control systems, this study shifts from complex microscopic physical equations to a direct nonlinear functional mapping between the relative motion states (displacement and velocity) and the output force of air springs. This approach aims to preserve critical nonlinear hysteresis characteristics while significantly reducing the computational overhead. A progressive modeling strategy is implemented to characterize these complex behaviors. Initially, polynomial fitting is employed to identify key input features; however, its limited capacity to capture intricate nonlinearities necessitates more advanced methods. Subsequently, standard Feedforward Neural Networks (FNNs) are explored for their nonlinear mapping capabilities, yet their inherent “black-box” nature often leads to convergence difficulties and restricted generalization. To address these issues, a Physics-Informed Neural Network (PINN) architecture is introduced, embedding physical governing equations as regularization constraints within the loss function to integrate data-driven flexibility with mathematical rigor. Recognizing that conventional PINNs often encounter convergence challenges due to conflicts between PDE constraints and data-driven loss terms, this research develops a Physics-Embedded Hierarchical Network (PEHN). By deriving specialized PDE constraints tailored to air spring dynamics and designing a hierarchical architecture aligned with these physical requirements, the PEHN effectively balances physical priors with experimental data. Experimental results demonstrate that, compared to the baseline models, the proposed PEHN exhibits stronger stability and superior accuracy in capturing the complex nonlinearities of air spring dynamics. Full article

Accurate and continuous monitoring of flood dynamics is fundamental to understanding wetland hydrological processes and their ecological implications, yet it remains challenging due to the inherent trade-off between spatial and temporal resolution in remote sensing observations. This study advances flood monitoring methodology by developing and validating a spatiotemporal fusion framework specifically designed for multi-source Synthetic Aperture Radar (SAR) data—an approach that has remained underdeveloped despite its critical importance for all-weather wetland observation. We propose the Fusion SAR Operational Monitoring (FSOM) framework, which integrates three established components—the Flexible Spatiotemporal Data Fusion (FSDAF) model, the Sentinel-1 Dual-Polarized Water Index (SDWI), and automated thresholding classification—into a coherent processing chain that generates consistent high-resolution flood extent time series from multi-sensor SAR data (Sentinel-1 and GF-3). The FSOM was applied to the Chen Lake Wetland from 2020 to 2024, producing a monthly flood map dataset at 5 m spatial resolution. Quantitative validation demonstrated the superiority of the FSOM-derived products. Compared to water classifications using original Sentinel-1 data, the FSOM results achieved a significantly higher overall accuracy (exceeding 90%) and Kappa coefficient (>0.90) than the Sentinel-1 results, which had overall accuracy (exceeding 86%) and Kappa coefficient (>0.75). Critically, the producer’s accuracy for water bodies consistently surpassed 91%, indicating a substantial reduction in omission errors and markedly improved detection of small water bodies. These results confirm the effectiveness of the proposed FSOM framework in mitigating the spatiotemporal resolution trade-off, thereby providing a reliable high-fidelity data foundation to support precise wetland conservation and flood disaster emergency response. The framework thus offers a practical tool for scientists and water resource managers seeking to enhance monitoring capabilities in the world’s most dynamic and ecologically significant wetland ecosystems. Full article