Search Results (1,566)

Under the background of the Rural Revitalization Strategy, Zhejiang Province is promoting “Zhejiang-style Vernacular Dwellings” as a crucial measure to enhance the rural living environment and architectural appearance. However, traditional stylistic control tools, such as standardized rural housing design atlases, exhibit limitations including weak responsiveness to villagers’ individualized needs and high professional thresholds. Consequently, they struggle to address the bottlenecks in grassroots governance efficiency caused by massive and personalized housing demands. Meanwhile, when applied to architectural design, general generative AI technologies often suffer from “structural hallucinations” and the weakening of regional characteristics due to a lack of physical tectonic constraints. Oriented towards the governance requirements of the Zhejiang Provincial Rural Housing Design Guidelines, this study proposes a compliance evaluation-driven “Contour-Semantic-Image” hierarchical generative control framework. This aims to construct a visual scheme generation and pre-screening workflow that deeply adapts to the logic of rural governance. At the data level, this research aggregates multi-source materials, including official standardized atlases, government stylistic guidelines, and real-world photographs. Through expert screening and standardized processing of 596 schemes, a dataset of 333 high-quality, finely annotated structured samples is constructed. Furthermore, a human-guided, machine-segmented workflow assisted by Segment Anything Model 2 (SAM 2) is employed to establish a semantic label system comprising 4 major categories and 13 subcategories of components, thereby achieving the structural deconstruction of architectural prior knowledge. At the generation level, a two-stage model is trained based on Stable Diffusion and ControlNet: Stage I utilizes contour conditions and “layout prompts” to generate semantic label maps, aiming to strengthen component topology and layout consistency; Stage II employs the semantic label maps and “style prompts” as conditions to generate photorealistic facade images. By utilizing explicit semantic constraints to guide the model from pixel synthesis to logical generation, it achieves the controllable rendering of stylistic details and material expressions. At the evaluation level, an automated verification system featuring “clause translation–metric calculation–comprehensive scoring” is proposed. It conducts scoring, re-ranking, and diagnostic feedback on the generated variants across three dimensions: Design Rationality (Q), General Compliance (G), and Jiangnan water-town Regional Characteristics (P-J), forming a closed-loop “Generation-Evaluation-Feedback” workflow. Overall, this framework provides a “visualizable, evaluable, and explainable” pathway for scheme generation and pre-screening in the digital governance of rural architectural appearance. Full article

Offshore wind turbine operation and maintenance in complex sea states is influenced by the coupled effects of low-frequency vessel drift and high-frequency wave-induced disturbances. In practical operations, the ship dynamic positioning system primarily regulates low-frequency motion through vessel position control, whereas a boarding compensation system is required to attenuate high-frequency six-degrees-of-freedom motions to ensure safe personnel transfer. This study establishes coupled kinematic mapping among the ship dynamic positioning system, the Stewart platform, and a three-degrees-of-freedom gangway and proposes a hierarchical cooperative control architecture. At the upper layer, an extended Kalman filter and an exponential moving average low-pass filter are employed for online state estimation and for separating low-frequency and high-frequency components. A Kalman filter lookahead predictor is then used to generate a short-horizon prediction of the high-frequency component and to construct a feedforward reference signal. At the middle layer, the feedforward reference and the gangway end error feedback are coordinated at the velocity level, and a quadratic programming-based allocation strategy distributes compensation tasks between the Stewart platform and the gangway under safety-related constraints, including actuator stroke limits and singularity avoidance. At the lower layer, a robust feedback controller is designed for the gangway to mitigate modeling uncertainties and environmental disturbances and to ensure stable tracking. MATLAB R2024a-based simulations under representative wave conditions demonstrate that the proposed architecture improves end effector tracking accuracy and closed-loop stability compared with baseline strategies, providing a feasible engineering solution for shipboard boarding operations in complex sea states. Full article

Water quality prediction in non-stationary environmental systems requires not only high predictive accuracy but also structural robustness under physical, ecological, and operational constraints. This study reframes multi-model fusion as a constraint-governed inference architecture and synthesizes advances in rule-based adjudication, reliability-aware aggregation, post-fusion projection, dual-track adaptation, and hierarchical backoff control. By establishing a taxonomy of boundary constraints—specifically mass conservation, reaction kinetics, hydraulic transport, and ecological tipping points—an admissible prediction manifold identifies key structural limitations in existing paradigms, particularly their vulnerability to physical inconsistency and diminished reliability during non-stationary distribution shifts. A unified end-to-end robust framework is proposed in which candidate predictions are separated from admissibility validation, uncertainty is directly coupled to aggregation logic, and degradation pathways are explicitly defined under distribution shift. Furthermore, a multidimensional robustness evaluation matrix is introduced, incorporating structural consistency, ecological compliance, calibration quality, and adaptive stability alongside conventional accuracy metrics. The study advances water quality forecasting from model-centric optimization toward architecture-level governance, demonstrating that constraint-aware designs improve structural consistency, robustness under distribution shifts, and early warning reliability, providing a systematic reference for developing resilient, transparent, and operationally deployable environmental prediction systems. Full article

Autonomous obstacle avoidance is a critical capability for Autonomous Underwater Vehicles (AUVs) to operate safely in dynamic and uncertain marine environments. Traditional AUV control methods rely on precise physical modeling and preset rules, yet they struggle to adapt to multiple sources of uncertainty, such as random initial states, dynamic obstacles, and varying currents. In recent years, deep reinforcement learning has provided a new avenue for data-driven adaptive policy learning. However, it remains insufficient for handling long-horizon tasks with sparse rewards. While hierarchical reinforcement learning can mitigate reward sparsity through temporal abstraction, it often faces challenges including exploration–exploitation imbalance, slow global convergence, and insufficient safety guarantees. Furthermore, most existing studies neglect dynamic environmental disturbances and task continuity, which further limits the practical application of these algorithms. To address these challenges, this paper proposes a hierarchical curiosity-driven AUV obstacle avoidance algorithm (HDAO), designed for autonomous obstacle avoidance in dynamic and uncertain underwater environments. The core design of HDAO incorporates several key innovations. Firstly, it introduces a Collision Threat Index for dynamic obstacles, which enables explicit risk perception and quantifies collision threats, thereby enhancing the policy’s generalization and robustness. Secondly, a task-decoupled hierarchical architecture is employed to synergistically optimize global path planning and local obstacle avoidance behaviors. This approach effectively manages long-horizon navigation tasks while alleviating high-dimensional training pressure. Finally, a novel reward mechanism is designed by integrating hierarchical active exploration with curiosity-driven passive exploration. This mechanism effectively incentivizes the agent to explore unvisited areas under sparse reward conditions and dynamically balances exploration and exploitation. Experimental results demonstrate that HDAO significantly outperforms existing methods in terms of obstacle avoidance success rate, training convergence speed and robustness against external disturbances. Full article

Infectious disease outbreaks continue to pose a significant threat to global health, underscoring the importance of timely detection and reliable reporting for effective interventions. Traditional reporting systems often rely on hierarchical data flows, which introduce delays, inconsistencies, and vulnerabilities, as highlighted during the COVID-19 pandemic. Blockchain, a disruptive technology, offers a promising solution. This study proposes a blockchain-based infectious disease reporting system built on Hyperledger Fabric that supports multi-level reporting and governance across national health systems. The architecture preserves hierarchical structures while enabling real-time reporting across authorized health stakeholders. It separates public test results from sensitive patient information, with private data secured via Private Data Collections and anchored using cryptographic hashes. Smart contracts enforce role-based access and validation, ensuring data integrity and controlled oversight. The system prototype was deployed within Docker containers and evaluated using illustrative COVID-19 case data. Network performance was benchmarked using Hyperledger Caliper, measuring throughput, latency, and resource utilization. The results demonstrate proper system functioning and stable transaction processing under the tested experimental conditions, supporting the feasibility of the proposed architecture for privacy-preserving multi-level infectious disease reporting systems. Full article

In this article, we propose a non-archimedean framework for information processing based on the arithmetic and geometry of p-adic numbers. Exploiting the ultrametric structure of ${\mathbb{Q}}_p$, we introduce norm-based p-adic operations inspired by classical logical connectives that exhibit strong stability under perturbations. The non-archimedean nature of the p-adic norm induces natural scale separation and clustering properties, leading to robust information encoding and spectral localization. Using p-adic Fourier analysis, we show that hierarchical signals decompose into disjoint frequency bands, enabling multiscale information processing with reduced interference. These results provide a mathematical foundation for robust and hierarchical information architectures, with potential applications in signal processing, error-tolerant computation, and non-classical information systems. Full article

Accurate cloud detection remains a significant challenge due to the spectral ambiguity between clouds and bright or heterogeneous surfaces (e.g., snow, desert). While multi-angle and polarization data offer rich information, the discriminative power of joint spectral analysis for resolving these ambiguities has been underexploited. In this work, we demonstrate that physically motivated spectral band ratios and differences can robustly enhance cloud signatures. Motivated by this insight, we propose a novel deep learning framework, the Multi-angle Polarization Feature Pyramid Structure (MP-FPS), that explicitly leverages joint spectral features as discriminative priors. Our architecture employs a dual-branch network to disentangle and adaptively fuse spectral and multi-angle polarization modalities. Within this framework, a hierarchical, multi-scale cross-channel multi-angle fusion module dynamically captures spatial–spectral–angular dependencies, enriching the structural representation of clouds. Furthermore, a channel-space dual-path attention mechanism refines sub-pixel responses, significantly improving detection accuracy in challenging regions such as cloud edges and thin cirrus. Evaluated on the global POLDER-3 dataset, MP-FPS achieves a mean Intersection over Union (mIoU) of 0.8662 across diverse surface types, surpassing the official baseline by 12.4%. This study establishes joint spectral analysis as a critical enabler for high-precision cloud masking, and demonstrates its synergistic value when integrated with multi-angle polarimetric information in a unified deep architecture. Full article

Sensitivity and effective sensing range are core performance metrics of flexible pressure sensors, directly dictating their practical applicability. A key challenge in sensor design is sensitivity degradation with elevated pressure, hindering synergistic optimization of high sensitivity and broad sensing range, while cumbersome electrode fabrication further impedes facile preparation and large-scale deployment of high-performance devices. Herein, this work proposes a novel fabrication strategy for flexible iontronic pressure sensors via direct laser writing (DLW) technology. A controllable ultraviolet laser patterns polyimide substrates to fabricate hierarchical stepped conoid-like microstructural templates, which are transferred to ion gels through reverse molding. The DLW-enabled precise geometric control and hierarchical conical architectures efficiently amplify interfacial contact area variation under pressure, significantly boosting sensitivity. The resultant sensor achieves a high sensitivity of 118.4 kPa⁻¹ and a broad detection range up to 2000 kPa, with fast response/recovery times of 38.4 ms and 47 ms and excellent mechanical stability enduring 2000 loading–unloading cycles at 850 kPa. Multi-scenario physiological signal monitoring validates its accurate capture of laryngeal vibrations and joint movements. This work establishes a straightforward, efficient microfabrication route for high-performance flexible iontronic sensors, accelerating their practical application in wearable health monitoring and related fields. Full article

Traditional villages in ecologically fragile and multi-ethnic frontier regions are increasingly threatened by rapid urbanization and socio-economic transformation. Northwestern Yunnan, located in the longitudinal valleys of the Hengduan Mountains, represents a key cultural landscape of plateau agropastoral civilization and ethnic interaction, yet its spatial organization and clustering mechanisms remain insufficiently understood. This study develops a four-dimensional analytical framework integrating four dimensions—spatial morphology (village distribution patterns and density), geomorphological conditions (elevation, slope, and terrain features), cultural attributes (ethnic composition and historical-cultural corridors), and architectural typologies (dominant residential structure types) to examine 246 officially recognized traditional villages. Using GIS-based spatial statistics, kernel density estimation (KDE), spatial autocorrelation, and a hierarchical overlay model, the study identifies the spatial structure (distribution patterns and density gradients), environmental adaptability (relationships with elevation, slope, and hydrological conditions), and multidimensional clustering characteristics (integrated clustering intensity across four analytical dimensions) of settlements. The results reveal a highly uneven and a statistically significant clustered spatial pattern (R = 0.606, Moran’s I = 0.251, p < 0.05) characterized by a “two corridors–six clusters–multiple nodes” structure. Settlement distribution demonstrates strong coupling with mid-elevation plateau basins, river valley systems, and trade-cultural corridors shaped by the Ancient Tea Horse Road. Multidimensional integration further classifies villages into three typologies—comprehensive, specialized, and general clusters—reflecting different levels of coordination among spatial, environmental, cultural, and architectural dimensions. These findings reveal the spatial regularities and multidimensional clustering characteristics of officially recognized traditional villages in Northwestern Yunnan, and suggest that environmental setting, historical corridors, and cultural-architectural features jointly shape the current recognized heritage landscape. The proposed framework provides a context-sensitive basis for differentiated heritage conservation and rural management in mountainous multi-ethnic regions. Full article

This study illuminates fundamental questions in behavioral science through advanced machine learning methodologies applied to large-scale public opinion data. Drawing on Kahneman and Tversky’s dual-process theory and Sunstein’s nudge architecture, we employ hierarchical unsupervised clustering and supervised predictive models to detect cognitive biases—loss aversion, availability heuristic, and partisan motivated reasoning—embedded within a nationally representative survey of 5022 American respondents. Our primary methodological contribution is a hierarchical two-stage clustering framework that uncovers latent opinion structures without imposing a priori partisan categories, permitting discovery of cross-cutting cleavages invisible to conventional survey analysis. Three principal findings emerge: (1) loss aversion is empirically confirmed in prospective economic perception, with pessimists outnumbering optimists at a 1.14:1 ratio even among respondents rating current conditions positively; (2) partisan motivated reasoning produces a 13.15 percentage-point perception gap among individuals with identical financial circumstances; and (3) multi-platform digital engagement is associated with reduced partisan bias, providing evidence that challenges simple echo chamber assumptions. Crime safety perception emerges as the strongest predictor of economic bias, surpassing party affiliation, and substantiating availability heuristic dominance in political cognition. These findings carry implications for democratic accountability, platform governance, and the ethics of AI-augmented behavioral analysis in an era of affective polarization. Full article

The rapid electrification of transportation and proliferation of distributed energy resources (DERs) are transforming distribution grids into highly dynamic, data-intensive, and cyber-physical systems. While reinforcement learning (RL), multi-agent coordination, and edge computing offer powerful tools for adaptive control, their deployment in safety-critical utility environments raises concerns regarding stability, certification compatibility, cyber-resilience, and regulatory acceptance. This paper presents an architecture-centric framework for edge-intelligent and cyber-resilient coordination of electric vehicles (EVs) and DERs that reconciles adaptive learning with deterministic safety guarantees. The proposed hierarchical edge–cloud architecture integrates multi-agent system (MAS) coordination, constraint-invariant reinforcement learning, and embedded cybersecurity mechanisms within a structured control hierarchy. Learning-enabled edge agents operate exclusively within standards-compliant safety envelopes enforced through supervisory constraint projection, control barrier functions, and Lyapunov-consistent stability safeguards. Protection-critical functions remain deterministic and isolated from adaptive layers, preserving compatibility with IEEE 1547 and existing utility protection schemes. The framework further incorporates anomaly triggered policy freezing, fail-safe fallback modes, and communication-aware resilience mechanisms to prevent unsafe transient behavior in non-stationary, distributed environments. Unlike simulation-only learning approaches, the architecture embeds progressive validation through software-in-the-loop (SIL), hardware-in-the-loop (HIL), and power hardware-in-the-loop (PHIL) testing to empirically verify transient stability, constraint compliance, and cyber-resilience under realistic timing and disturbance conditions. Beyond technical performance, the paper situates edge intelligence within standards evolution, governance structures, workforce transformation, techno-economic assessment, and equitable deployment pathways. By framing adaptive control as a bounded, auditable augmentation layer rather than a disruptive replacement for certified infrastructure, the proposed architecture provides a pragmatic roadmap for evolutionary modernization of distribution systems. Full article

This paper presents a distributed V2G-enabled multiport DC charging system with a hierarchical charging management strategy. Unlike conventional architectures based on centralized power converter cabinets, the proposed system distributes bidirectional power converters within individual multiport dispensers, each equipped with a local charging power management device. This architecture improves system scalability, fault tolerance, and operational flexibility while enabling vehicle-level charging and V2G services. A hierarchical control framework is introduced, consisting of high-level optimal charging scheduling, mid-level power coordination among distributed dispensers, and low-level converter control. Key elements include modular power units that can be dynamically configured and expanded, providing a cost-effective and adaptable solution for growing EV markets. Experimental results obtained from a 45 kW modular DC charging prototype demonstrate an efficiency improvement of up to 2% at rated power compared to a non-modular charger. In contrast, the optimized charging strategy achieves an overall charging cost reduction of approximately 11% and a peak load demand reduction of up to 31%. Furthermore, stable bidirectional power flow, effective power sharing, and total harmonic distortion within regulatory limits are experimentally validated during both charging and V2G operation. The prototype is implemented to validate the proposed charging system in the laboratory environment. Full article

Hybrid supramolecular–nanocomposite hydrogels based on polyethylene glycol (PEG), β-cyclodextrin–adamantane host–guest interactions, and silica nanoparticles represent an important class of hierarchical soft materials with tunable viscoelastic and transport properties. This review critically analyzes recent progress in cyclodextrin–silica hybrid PEG hydrogels, focusing on the mechanistic coupling between stiffness, stress relaxation, and molecular transport arising from the interplay between reversible supramolecular crosslinks and nanoparticle-induced confinement effects. Particular attention is given to how host–guest exchange kinetics regulate dynamic bond rearrangement and affinity-mediated retention of hydrophobic cargo, while silica nanoparticles enhance mechanical reinforcement and modify diffusion pathways through tortuosity and interfacial polymer–particle interactions. The analysis highlights how nanoparticle size, loading level, and surface functionalization influence relaxation spectra and network topology, as well as how environmental stimuli may affect supramolecular bond stability and overall material performance. Comparison with alternative inorganic fillers and mesoporous silica architectures further clarifies the specific advantages of silica in achieving balanced mechanical stability and controlled transport behavior. Overall, current evidence indicates that hybrid CD–silica networks enable partial decoupling of stiffness, relaxation dynamics, and diffusion, although complete independence remains constrained by fundamental polymer physics relationships. These insights support the development of predictive structure–property frameworks for advanced biomedical and controlled release applications. Full article

Electrospinning is a versatile technique for producing polymer nanofibers with high ratios of surface area to volume and tunable porosity. Conventional approach to the optimization of processing parameters such as voltage and flow rate frequently encounters limitations in reproducibility and scalability. This review proposes a comprehensive framework that integrates macromolecular design principles with established electrohydrodynamic theories. We analyze how intrinsic molecular traits, specifically chain entanglement density, molecular weight distribution (MWD), topological architecture, and polymer–solvent thermodynamic interactions, define the boundaries of jet stability and solidification. Key findings highlight that while molecular weight establishes a baseline for spinnability, the MWD dictates the dynamic response under extreme deformation. Notably, high-molecular-weight fractions act as elastic load-bearers that suppress capillary breakup. Furthermore, we discuss here how molecular architecture and solvent-mediated segmental mobility determine whether molecular orientation is kinetically trapped or relaxed during the nanosecond timescales of jet flight. By establishing a hierarchical design logic prioritizing molecular and formulation variables over processing parameters, this framework provides a robust strategy to overcome challenges in scalability and reproducibility, positioning electrospinning as a sensitive probe for macromolecular dynamics under extreme elongation. Full article

Image inpainting aims to recover missing regions in damaged images while preserving structural coherence and textural authenticity. Although deep learning methods based on generative adversarial networks (GAN) have made significant progress, they still face challenges in modeling long-range dependencies and maintaining semantic consistency, especially when large areas are missing. To address these issues, we propose an innovative multi-stage restoration framework. The coarse restoration stage incorporates attention via a transformer architecture, while the refinement stage introduces a plug-and-play channel-frequency encoder (CF-Encoder). This encoder effectively models both global structure and local details by hierarchically extracting and enhancing features through frequency-domain decomposition combined with an adaptive spatial-channel attention mechanism. Furthermore, we employ a bi-discriminator fusion mechanism to stabilize training and enhance perceptual quality. Experiments across multiple benchmark datasets demonstrate our method’s superior performance in both quantitative metrics and visual fidelity, with particularly notable advantages in high-missing-value scenarios. Full article