Search Results (3,944)

Wave Energy Converter (WEC) buoys operate in aggressive marine environments that impose demanding requirements on structural materials, particularly in terms of moisture resistance, mechanical reliability, and long-term durability. Conventional glass fiber reinforced composites meet these performance requirements but raise sustainability concerns due to their high environmental footprint and limited recyclability. This study addresses this challenge by introducing a systematic, application-driven multi-criteria decision-making (MCDM) framework specifically tailored for material selection in marine renewable energy devices. The novelty of this work lies in the integration of marine durability-dominated criteria weighting with sustainability metrics, moving beyond cost-driven selection approaches commonly reported in the literature. Four cellulosic natural fibers, flax, hemp, kenaf, and sisal, are evaluated as reinforcements for polymer composites intended for point-absorber WEC buoy structures, using conventional E-glass as a baseline reference. Ten performance criteria covering mechanical properties, environmental durability, manufacturing feasibility, and sustainability are defined and objectively weighted using the entropy method to minimize subjective bias. Moisture resistance emerges as the most influential criterion with a weight of 0.142, underscoring its role as a primary degradation mechanism in marine environments, while material cost receives the lowest weight of 0.057, reflecting the prioritization of long-term performance over initial cost. The results identify flax as optimal reinforcement, achieving the highest aggregated score of 4.022 by effectively balancing mechanical performance, resistance to marine exposure, and environmental sustainability. This work introduces a novel decision-support tool for the sustainable design of buoy structures using natural fiber-reinforced composites and establishes a foundation for future optimization of such composites in wave energy applications. Full article

In the context of the rapid transformation of global agricultural production towards intensification and intelligence, the precise and intelligent management of horticultural crop production processes is key to enhancing resource utilization efficiency and industry profitability. Crop growth and development models, as digital representations of the interactions between environment, crops, and management, are core tools for achieving intelligent decision-making in facility production. This paper provides a comprehensive review of the advancements in intelligent management models and systems for horticultural crop growth and development. It introduces the developmental stages of horticultural crop growth models and the integration of multi-source data, systematically organizing and analyzing the modeling mechanisms of crop growth and development process models centered on developmental stages, photosynthesis and respiration, dry matter accumulation and allocation, and yield and quality formation. Furthermore, it summarizes the current status of expert decision-support system software development and application based on crop models, achieving comprehensive functionalities such as data and document management, model parameter management and optimization, growth process and environmental simulation, management plan design and effect evaluation, and result visualization and decision product dissemination. This illustrates the pathway from theoretical research to practical application of models. Addressing the current challenges related to the universality of mechanisms, multi-source data assimilation, and intelligent decision-making, the paper looks forward to future research directions, aiming to provide theoretical references and technological insights for the future development and system integration of intelligent management models for horticultural crop growth and development. Full article

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon. Full article

Intrinsic motivation provides a principled mechanism for driving exploration in reinforcement learning when external rewards are sparse or delayed. A central challenge, however, lies in defining meaningful novelty signals in high-dimensional and combinatorial state spaces, where observation-level density estimation and prediction-error heuristics often become unreliable. In this work, we propose an information-theoretic framework for intrinsically motivated reinforcement learning grounded in the Information Bottleneck principle. Our approach learns compact latent state representations by explicitly balancing the compression of observations and the preservation of predictive information about future state transitions. Within this bottlenecked latent space, intrinsic rewards are defined through information-theoretic quantities that characterize the novelty of state–action transitions in terms of mutual information, rather than raw observation dissimilarity. To enable scalable estimation in continuous and high-dimensional settings, we employ neural mutual information estimators that avoid explicit density modeling and contrastive objectives based on the construction of positive–negative pairs. We evaluate the proposed method on two representative combinatorial routing problems, the Travelling Salesman Problem and the Split Delivery Vehicle Routing Problem, formulated as Markov decision processes with sparse terminal rewards. These problems serve as controlled testbeds for studying exploration and representation learning under long-horizon decision making. Experimental results demonstrate that the proposed information bottleneck-driven intrinsic motivation improves exploration efficiency, training stability, and solution quality compared to standard reinforcement learning baselines. Full article

In digital marketplaces, trust in e-commerce platforms has evolved from a protective heuristic into a powerful mechanism of behavioral conditioning. This review interrogates how trust cues such as star ratings, fulfillment badges, and platform reputation shape consumer cognition, systematically displace critical evaluation, and create asymmetries in perceived quality. Drawing on over 47 high-quality studies across experimental, survey, and modeling methodologies, we identify seven interlocking dynamics: (1) cognitive outsourcing via platform trust, (2) reputational arbitrage by low-quality sellers, (3) consumer loyalty despite disappointment, (4) heuristic conditioning through trust signals, (5) trust inflation through ratings saturation, (6) false security masking structural risks, and (7) the shift in consumer trust from brands to platforms. Anchored in dual process theory, this synthesis positions trust not merely as a transactional enabler but as a socio-technical artifact engineered by platforms to guide attention, reduce scrutiny, and manage decision-making at scale. Eventually, platform trust functions as both lubricant and leash: streamlining choice while subtly constraining agency, with profound implications for digital commerce, platform governance, and consumer autonomy. Full article

Polymetallic nodules enriched in Mn, Ni, Cu, Co, and other metals may be one of the first seabed mineral resources to be exploited. Although optical imagery is crucial for resource evaluation, semi-buried nodules are frequently overlooked. To address this, we propose a framework that integrates the elliptic approximation method (EAM) and the contour interweaving method (CIM) to reconstruct three types of semi-buried nodules segmented by U-Net: edge-buried, partition-buried, and almost-completely-buried. This strategy introduced a decision-making mechanism based on category fusion, which significantly enhanced the robustness and practicality of the reconstruction. Performance was assessed using four metrics: area ratio, absolute percentage change, intersection-over-union, and Chamfer distance. Among 1785 samples, the EAM recovered up to 41.8% of lost area, which substantially improved the minimum values of area ratio and intersection-over-union, and it performed well on almost-completely-buried nodules. The CIM achieved median area ratio and intersection-over-union values of 99.37% and 93.36%, respectively, and excelled in edge-buried and partition-buried types. Fusion experiments demonstrated the complementary strengths of both approaches: 23.96% of buried area was recovered in large-scale imagery recognized by U-Net. The proposed framework balances accuracy, adaptability, and computational efficiency, which enables real-time nodule identification on platforms with limited resources such as autonomous underwater vehicles. This could provide more direct support for resource evaluation and mining applications. Full article

The rapid integration of unmanned aerial vehicles (UAVs) into next-generation wireless systems demands seamless and reliable handover (HO) mechanisms to ensure continuous connectivity. However, frequent topology changes, high mobility, and dynamic channel variations make traditional HO schemes inadequate for UAV-assisted 6G networks. This paper presents a comprehensive review of existing HO optimization studies, emphasizing artificial intelligence (AI) and machine learning (ML) approaches as enablers of intelligent mobility management. The surveyed works are categorized into three main scenarios: non-UAV HOs, UAVs acting as aerial base stations, and UAVs operating as user equipment, each examined under traditional rule-based and AI/ML-based paradigms. Comparative insights reveal that while conventional methods remain effective for static or low-mobility environments, AI- and ML-driven approaches significantly enhance adaptability, prediction accuracy, and overall network robustness. Emerging techniques such as deep reinforcement learning and federated learning (FL) demonstrate strong potential for proactive, scalable, and energy-efficient HO decisions in future 6G ecosystems. The paper concludes by outlining key open issues and identifying future directions toward hybrid, distributed, and context-aware learning frameworks for resilient UAV-enabled HO management. Full article

Autonomous navigation in unknown environments demands policies that can jointly perceive semantic context and geometric safety. Existing Transformer-enabled deep reinforcement learning (DRL) frameworks, such as the Goal-guided Transformer Soft Actor–Critic (GoT-SAC), rely on temporal stacking of multiple RGB frames, which encodes short-term motion cues but lacks explicit spatial understanding. This study introduces a geometry-aware RGB-D early fusion modality that replaces temporal redundancy with cross-modal alignment between appearance and depth. Within the GoT-SAC framework, we integrate a pixel-aligned RGB-D input into the Transformer encoder, enabling the attention mechanism to simultaneously capture semantic textures and obstacle geometry. A comprehensive systematic ablation study was conducted across five modality variants (4RGB, RGB-D, G-D, 4G-D, and 4RGB-D) and three fusion strategies (early, parallel, and late) under identical hyperparameter settings in a controlled simulation environment. The proposed RGB-D early fusion achieved a 40.0% success rate and +94.1 average reward, surpassing the canonical 4RGB baseline (28.0% success, +35.2 reward), while a tuned configuration further improved performance to 54.0% success and +146.8 reward. These results establish early pixel-level multimodal fusion (RGB-D) as a principled and efficient successor to temporal stacking, yielding higher stability, sample efficiency, and geometry-aware decision-making. This work provides the first controlled evidence that spatially aligned multimodal fusion within Transformer-based DRL significantly enhances mapless navigation performance and offers a reproducible foundation for sim-to-real transfer in autonomous mobile robots. Full article

This paper proposes a novel method, the Convergent Radiation Algorithm (CRA), aimed at multi-attribute group decision-making (MAGDM) in circular intuitionistic fuzzy settings. The approach is aimed at reaching geometric consensus among experts, with uncertainties and hesitancies expressed via circular intuitionistic fuzzy numbers (CIFNs). First, the qualitative judgment in professionals is converted into a geometric space where experts’ assessments are represented as spatial points that reflect the differences between the opinions. All these points are gradually combined with the help of a radiation–reflection–convergence mechanism, which iteratively finds the Optimal Consensus Point (OCP) to minimize the overall weighted divergence over the evaluations. After that, a projection-based scoring method is used to locate good and bad optimal solutions, and the alternatives are ranked based on a comparison of their projection distance. It presents a numerical example with data supplied by the Hubei agro-ecological zone to demonstrate that the offered method helps to capture collective agreement and convergence behavior that is consistent, and makes the decision results readable and reliable. Full article

Cross-border e-commerce, as an emerging trade format, offers new chances for optimizing industrial chains’ layout, enhancing economic resilience, and attaining high-quality development at the city level. In this context, treating the execution of the cross-border e-commerce comprehensive pilot zone (CBEC) as a quasi-natural experiment, this study subtly attests to how the CBEC affects urban entrepreneurship by using a difference-in-differences (DID) technique. The results exhibit that the CBEC greatly promotes urban entrepreneurship, which is supported by some robustness tests, including instrumental variable testing and placebo testing. Heterogeneity analysis reveals that in cities with more developed economies, stronger digitalization, richer cultures, sounder law rules, and better business environments, the benefit for the CBEC on entrepreneurship is more significant. Mechanism testing argues that the CBEC promotes urban entrepreneurship through talent aggregation and industrial upgrading. Precisely, the more concentrated high-quality talents are and the more advanced the industrial structure is, the higher the urban entrepreneurship. More importantly, the CBEC exhibits a spatial spillover effect on entrepreneurship, promoting local entrepreneurship while stimulating the motivation to imitate and learn in neighboring areas, thereby driving their entrepreneurship. The findings offer a viable decision-making guide for building a unified factor market and achieving regional coordinated development. Full article

Queuing is almost inevitable in tourist service experiences, but most tourists are reluctant to wait. Drawing on prospect theory, this study examined how comparative framing influences tourists’ waiting intention. Across three scenario-based experiments, the research found that, compared with non-comparative framing, comparative framing can effectively enhance tourists’ waiting intention. Perceived waiting costs play a mediating role in the impact of the comparative framing on waiting intention. Additionally, the queuing settings play a moderating role, and the mediating effect is stronger in physical queues than in virtual queues. This research shifts the analytical focus from objective waiting time to the framing of waiting-time information, reveals a psychological cost assessment mechanism based on reference points, and enriches the theoretical explanation of tourists’ immediate decision-making in tourism services. It also provides practical references for optimizing service information and queue management during peak hours. Full article

By addressing the challenges of management difficulties, insufficient integration of driver analysis, and single-dimensional analysis in the governance of illegal land use and illegal construction (collectively referred to as the “Two Illegalities”) under rapid urbanization, this study designs and implements a GIS-based governance system using Xiamen City as the study area. First, we propose a standardized data-processing workflow and construct a comprehensive management platform integrating multi-source data fusion, spatiotemporal visualization, intelligent analysis, and customized report generation, effectively lowering the barrier for non-professional users. Second, utilizing methods integrated into the platform, such as Moran’s I and centroid trajectory analysis, we deeply analyze the spatiotemporal evolution and driving mechanisms of “Two Illegalities” activities in Xiamen from 2018 to 2023. The results indicate that the distribution of “Two Illegalities” exhibits significant spatial clustering, with hotspots concentrated in urban–rural transition zones. The spatial morphology evolved from multi-core diffusion to the contraction of agglomeration belts. This evolution is essentially the result of the dynamic adaptation between regional economic development gradients, urbanization processes, and policy-enforcement synergy mechanisms. Through a modular, open technical architecture and a “Data-Technology-Enforcement” collaborative mechanism, the system significantly improves information management efficiency and the scientific basis of decision-making. It provides a replicable and scalable technical framework and practical paradigm for similar cities to transform “Two Illegalities” governance from passive disposal to active prevention and control. Full article

Reverse engineering (RE) has been increasingly adopted in metal manufacturing to digitize legacy parts, connect “as-is” geometry to mechanical performance, and enable agile repair and remanufacturing. This review consolidates scan-to-simulation workflows that transform 3D measurement data (optical/laser scanning and X-ray computed tomography) into simulation-ready models for structural assessment and manufacturing decisions, with an explicit focus on sustainability. Key steps are reviewed, from acquisition planning and metrological error sources to point-cloud/mesh processing, CAD/feature reconstruction, and geometry preparation for finite-element analysis (watertightness, defeaturing, meshing strategies, and boundary condition transfer). Special attention is given to uncertainty quantification and the propagation of geometric deviations into stress, stiffness, and fatigue predictions, enabling robust accept/reject and repair/replace choices. Sustainability is addressed through a lightweight reporting framework covering material losses, energy use, rework, and lead time across the scan–model–simulate–manufacture chain, clarifying when digitalization reduces scrap and over-processing. Industrial use cases are discussed for high-value metal components (e.g., molds, turbine blades, and marine/energy parts) where scan-informed simulation supports faster and more reliable decision making. Open challenges are summarized, including benchmark datasets, standardized reporting, automation of feature recognition, and integration with repair process simulation (DED/WAAM) and life-cycle metrics. A checklist is proposed to improve reproducibility and comparability across RE studies. Full article

In recent years, the rapid development of commercial satellite projects, such as low-Earth orbit (LEO) communication and remote sensing constellations, has driven the satellite industry toward low-cost, rapid development, and large-scale deployment. Commercial off-the-shelf (COTS) components have been widely adopted across various commercial satellite platforms due to their advantages of low cost, high performance, and plug-and-play availability. However, the space environment is complex and hostile. COTS components were not originally designed for such conditions, and they often lack systematically flight-verified protective frameworks, making their reliability issues a core bottleneck limiting their extensive application in critical missions. This paper focuses on COTS solid-state drives (SSDs) onboard the Jilin-1 KF satellite and presents a full-lifecycle reliability practice covering component selection, system design, on-orbit operation, and failure feedback. The core contribution lies in proposing a full-lifecycle methodology that integrates proactive design—including multi-module redundancy architecture and targeted environmental stress screening—with on-orbit data monitoring and failure cause analysis. Through fault tree analysis, on-orbit data mining, and statistical analysis, it was found that SSD failures show a significant correlation with high-energy particle radiation in the South Atlantic Anomaly region. Building on this key spatial correlation, the on-orbit failure mode was successfully reproduced via proton irradiation experiments, confirming the mechanism of radiation-induced SSD damage and providing a basis for subsequent model development and management decisions. The study demonstrates that although individual COTS SSDs exhibit a certain failure rate, reasonable design, protection, and testing can enhance the on-orbit survivability of storage systems using COTS components. More broadly, by providing a validated closed-loop paradigm—encompassing design, flight verification and feedback, and iterative improvement—we enable the reliable use of COTS components in future cost-sensitive, high-performance satellite missions, adopting system-level solutions to balance cost and reliability without being confined to expensive radiation-hardened products. Full article

The subgrade is a critical component of highway infrastructure that directly affects pavement performance and traffic safety. With the rapid expansion of highway networks and increasing heavy-truck traffic, latent subgrade distresses, such as insufficient base strength, uneven settlement, and base cracking, have become key factors limiting pavement serviceability. These distresses are often difficult to detect at early stages and may evolve into sudden structural failures if not properly identified. This study investigates the evolution mechanisms and spatial characteristics of representative subgrade distresses through an integrated framework combining FWD screening, GPR imaging, core sampling, and Abaqus-based finite element simulation. Field data were collected from the Changshen Expressway. Potential weak zones were first identified using FWD testing and further localized by GPR, while multilayer constitutive parameters were obtained from core sample analyses. The field-derived material parameters were then incorporated into an FE model to simulate pavement responses under loading and to interpret the underlying distress mechanisms. The proposed framework enables identification of dominant distress types, quantification of stiffness degradation, and clarification of deterioration pathways within the subgrade system. The results provide practical support for condition assessment, health monitoring, and maintenance decision-making in highway infrastructure. Full article