Search Results (374)

Robot energy consumption is a prominent challenge in intelligent manufacturing and construction. Reducing energy consumption during robot trajectory execution is an urgent issue requiring immediate attention. In view of the shortcomings of traditional trajectory optimization methods, this paper proposes a multi-objective trajectory optimization method that combines energy consumption mapping with the NSGA-II, aiming to reduce robots’ trajectory energy consumption and optimize execution efficiency. By establishing a dynamic energy consumption model, energy consumption mapping is employed to constrain energy consumption within the robot’s workspace, thereby providing guidance for the optimization process. Simultaneously, with energy consumption minimization and time consumption as optimization objectives, the NSGA-II is utilized to obtain the Pareto-optimal solution set through non-dominated sorting and congestion distance calculation. Energy consumption mapping serves as a dynamic feedback mechanism during the optimization process, guiding the distribution of trajectory points towards low-energy-consumption regions, accelerating algorithm convergence, and enhancing the quality of the solution set. The experimental results demonstrate that the proposed method can significantly reduce robots’ trajectory energy consumption and achieve an effective balance between energy consumption and time consumption. Compared with the conventional NSGA-II normalized weighted function method in similar task scenarios, the robot can save 14.87% and 10.47% of its energy consumption, respectively. Compared with traditional methods, this method exhibits superior energy-saving performance and adaptability in complex task environments, providing a novel solution for the efficient trajectory planning of robots. Full article

With the increasing depth of research on built environments, theories of restorative environments and concepts of biophilic design have garnered widespread attention in the field of architecture. Based on Attention Restoration Theory (ART) and Flow Theory, this study systematically investigates how the architectural environment of aquatic centers influences users’ learning engagement in sports through psychological mechanisms. Analysis of cross-sectional data from 865 users revealed that all four core dimensions of restorative environments (being away, extent, fascination, and compatibility) significantly positively affect users’ learning engagement in aquatic centers. Psychological flow was found to mediate the relationship between these restorative dimensions and learning engagement. Building on previous research, this study constructs a theoretical framework of “restorative design–flow experience–learning behavior”, integrating the architectural features of aquatic centers with users’ psychological experiences. This approach addresses the gap in existing research where architectural elements and user psychological experiences have been studied in isolation, providing a theoretical basis for optimizing user experience through environmental interventions in sports architecture. The findings extend the application of environmental psychology in sports architecture and offer practical guidance for designing aquatic environments that promote learning engagement. Full article

To enhance the efficiency and reduce the operational costs of large-scale Unmanned Aerial Vehicle (UAV) inspection missions limited by endurance, this paper addresses the coupled problem of dynamically positioning landing/takeoff sites and routing the UAVs. A novel Hierarchical Reinforcement Learning (H-DRL) framework is proposed, which decouples the problem into a high-level strategic deployment policy and a low-level tactical routing policy. The primary contribution of this work lies in two architectural innovations that enable globally coordinated, end-to-end optimization. First, a coordinated credit assignment mechanism is introduced, where the high-level policy communicates its strategic guidance to the low-level policy via a learned “intent vector,” facilitating intelligent collaboration. Second, an Energy-Aware Graph Attention Network (Ea-GAT) is designed for the low-level policy. By endogenously embedding an energy feasibility model into its attention mechanism, the Ea-GAT guarantees the generation of dynamically feasible flight paths. Comprehensive simulations and a physical experiment validate the proposed framework. The results demonstrate a significant improvement in mission efficiency, with the makespan reduced by up to 16.3%. This work highlights the substantial benefits of joint optimization for dynamic robotic applications. Full article

The integration of nanofluids into solar collectors has gained increasing attention due to their potential to enhance heat transfer and support the transition toward low-carbon energy systems. However, a systematic understanding of their photothermal performance under the direct absorption mode remains lacking. This review addresses this gap by critically analyzing the role of nanofluids in solar energy harvesting, with a particular focus on the direct absorption mechanisms. Nanofluids enhance solar radiation absorption through improved light absorption by nanoparticles, surface plasmon resonance in metals, and enhanced heat conduction and scattering effects. The novelty of this work lies in its comparative evaluation of advanced nanofluids, including magnetic nanofluids, plasma nanofluids, and nanophase change slurries, highlighting their unique capabilities in flow manipulation, thermal storage, and optical energy capture. Future research directions are identified, such as the life cycle assessment (LCA) of nanofluids in solar systems, applications of hybrid nanofluids, development of predictive models for nanofluid properties, optimization of nanofluid performance, and integration of Direct Absorption Solar Collectors (DASCs). In addition, challenges related to the stability, production cost, and toxicity of nanofluids are critically analyzed and discussed for practical applications. This paper offers guidance for the design and application of high-performance nanofluids in next-generation solar energy systems. Full article

Effectively managing visual search tasks across multiple spatial regions during daily activities such as driving, cycling, and navigating complex environments often overwhelms visual processing capacity, increasing the risk of errors and missed critical information. This study investigates an integrated approach that combines an Ambient Display system utilizing peripheral vision cues with traditional Head-Mounted Displays (HMDs) to enhance spatial search efficiency while minimizing cognitive burden. We systematically evaluated this integrated HMD-Ambient Display system against standalone HMD configurations through comprehensive user studies involving target search scenarios across multiple spatial regions. Our findings demonstrate that the combined approach significantly improves user performance by establishing a complementary visual system where peripheral stimuli effectively capture initial attention while central HMD cues provide precise directional guidance. The integrated system showed substantial improvements in reaction time for rear visual region searches and higher user preference ratings compared with HMD-only conditions. This integrated approach represents an innovative solution that efficiently utilizes dual visual channels, reducing cognitive load while enhancing search efficiency across distributed spatial areas. Our contributions provide valuable design guidelines for developing assistive technologies that improve performance in multi-region visual search tasks by strategically leveraging the complementary strengths of peripheral and central visual processing mechanisms. Full article

In the unmanned aerial vehicles (UAVs) flight control and navigation guidance system, trajectory prediction serves as a critical foundational component, with its accuracy and reliability directly influencing the system performance of the UAVs. However, existing research has predominantly focused on optimizing algorithm efficiency, failing to fully consider the impact of the UAV’s flight status on its trajectory. This has resulted in significant discrepancies between predicted results and actual trajectories in complex scenarios. Therefore, this paper proposes a trajectory prediction algorithm that integrates the UAVs’ behavioral intentions. Firstly, a behavioral intention recognition model is constructed using the Support Vector Machine (SVM) to accurately discriminate the UAV’s motion patterns and output the probability distribution of its future actions, thereby integrating semantic-level intention information into the prediction process. Secondly, the Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to mine the spatial-temporal correlation features from trajectory data. Additionally, an attention mechanism is introduced to capture key information of sequence, enhancing the model’s ability to represent complex motion trends. The results of simulation experiments demonstrate that this algorithm exhibits significant advantages in terms of trajectory prediction accuracy and scene adaptability, providing more practical technical support for intelligent navigation and safety control of UAVs. Full article

Semantic segmentation of urban scenes from red–green–blue and thermal infrared imagery enables per-pixel categorization, delivering precise environmental understanding for autonomous driving and urban planning. However, existing methods suffer from inefficient fusion and insufficient boundary accuracy due to modal differences. To address these challenges, we propose a bidirectional dynamic adaptation framework with two complementary networks. The modality-aware network uses dual attention and multi-scale feature integration to balance modal contributions adaptively, improving intra-class semantic consistency and reducing modal disparities. The edge-texture guidance network applies pixel-level and feature-level weighting with Sobel and Gabor filters to enhance inter-class boundary discrimination, improving detail and boundary precision. Furthermore, the framework redefines multi-modal synergy using an adaptive cross-modal mutual learning mechanism. This mechanism employs information-driven dynamic alignment and probability-guided semantic consistency to overcome the fixed constraints of traditional mutual learning. This cohesive orchestration enhances multi-modal fusion efficiency and boundary delineation accuracy. Extensive experiments on the MFNet and PST900 datasets demonstrate the framework’s superior performance in urban road, vehicle, and pedestrian segmentation, surpassing state-of-the-art approaches. Full article

Proton exchange membrane fuel cells (PEMFCs) are considered promising solutions to address global energy and environmental challenges. This is largely due to their high efficiency in energy transformation, low emission of pollutants, quick responsiveness, and suitable operating conditions. However, their widespread application is limited by high cost, limited durability and system complexity. To maintain system reliability and optimize cost-effectiveness, it is essential to predict the remaining operational lifespan of PEMFC systems with precision. This study introduces a prediction framework integrating a dual-layer bidirectional LSTM architecture enhanced by an attention mechanism for accurately predicting the RUL of PEMFCs. Raw data is preprocessed, and important features are selected by the smoothing technique and random forest method to reduce manual intervention. To enhance model adaptability and predictive accuracy, the Optuna optimization framework is employed to automatically fine-tune hyperparameters. The proposed prediction model is benchmarked against several existing approaches using aging datasets from two separate PEMFC stacks. Experimental findings indicate that the proposed two-layer BiLSTM with attention mechanism surpasses other baseline models in performance. Notably, the designed prediction model demonstrates strong performance on both benchmark datasets and real-world data acquired through a custom-built experimental fuel cell platform. This research offers meaningful guidance for prolonging the service life of PEMFCs and enhancing the efficiency of maintenance planning. Full article

To address the limitations of low fusion efficiency and insufficient personalization in multimodal perception for educational intelligence, a novel deep learning framework is proposed that integrates image sensor data with textual and contextual information through a cross-modal attention mechanism. The architecture employs a cross-modal alignment module to achieve fine-grained semantic correspondence between visual features captured by image sensors and associated textual elements, followed by a personalized feedback generator that incorporates learner background and task context embeddings to produce adaptive educational guidance. A cognitive weakness highlighter is introduced to enhance the discriminability of task-relevant features, enabling explicit localization and interpretation of conceptual gaps. Experiments show the proposed method outperforms conventional fusion and unimodal baselines with 92.37% accuracy, 91.28% recall, and 90.84% precision. Cross-task and noise-robustness tests confirm its stability, while ablation studies highlight the fusion module’s +4.2% accuracy gain and the attention mechanism’s +3.8% recall and +3.5% precision improvements. These results establish the proposed method as a transferable, high-performance solution for next-generation adaptive learning systems, offering precise, explainable, and context-aware feedback grounded in advanced multimodal perception modeling. Full article

Accurate glomerular segmentation in renal pathological images is a key challenge for chronic kidney disease diagnosis and assessment. Due to the high visual similarity between pathological glomeruli and surrounding tissues in color, texture, and morphology, significant “camouflage phenomena” exist, leading to boundary identification difficulties. To address this problem, we propose BM-UNet, a novel segmentation framework that embeds boundary guidance mechanisms into a Mamba architecture with a symmetric encoder–decoder design. The framework enhances feature transmission through explicit boundary detection, incorporating four core modules designed for key challenges in pathological image segmentation. The Multi-scale Adaptive Fusion (MAF) module processes irregular tissue morphology, the Hybrid Boundary Detection (HBD) module handles boundary feature extraction, the Boundary-guided Attention (BGA) module achieves boundary-aware feature refinement, and the Mamba-based Fused Decoder Block (MFDB) completes boundary-preserving reconstruction. By introducing explicit boundary supervision mechanisms, the framework achieves significant segmentation accuracy improvements while maintaining linear computational complexity. Validation on the KPIs2024 glomerular dataset and HuBMAP renal tissue samples demonstrates that BM-UNet achieves a 92.4–95.3% mean Intersection over Union across different CKD pathological conditions, with a 4.57% improvement over the Mamba baseline and a processing speed of 113.7 FPS. Full article

Remote sensing image dehazing presents substantial challenges in balancing physical fidelity with generative flexibility, particularly under complex atmospheric conditions and sensor-specific degradation patterns. Traditional physics-based methods often struggle with nonlinear haze distributions, while purely data-driven approaches tend to lack interpretability and physical consistency. To bridge this gap, we propose the Atmospheric Scattering Prior embedded Diffusion Model (ASPDiff), a novel framework that seamlessly integrates atmospheric physics into the diffusion-based generative restoration process. ASPDiff establishes a closed-loop feedback mechanism by embedding the atmospheric scattering model as a physics-driven regularization throughout both the forward degradation simulation and the reverse denoising trajectory. The framework operates through the following three synergistic components: (1) an Atmospheric Prior Estimation Module that uses the Dark Channel Prior to generate initial estimates of the transmission map and global atmospheric light, which are then refined through learnable adjustment networks; (2) a Diffusion Process with Atmospheric Prior Embedding, where the refined priors serve as conditional guidance during the reverse diffusion sampling, ensuring physical plausibility; and (3) a Haze-Aware Refinement Module that adaptively enhances structural details and compensates for residual haze via frequency-aware decomposition and spatial attention. Extensive experiments on both synthetic and real-world remote sensing datasets demonstrate that ASPDiff significantly outperforms existing methods, achieving state-of-the-art performance while maintaining strong physical interpretability. Full article

Natural playgrounds have garnered growing attention as supportive environments for children’s mental health. This study develops an analytical framework grounded in affordance theory and incorporates the Pleasure–Arousal–Dominance (PAD) model to examine the relationships between physical environmental features—and their combinations—in natural playgrounds and children’s emotional perceptions. Using the Yunhu Natural Playground in Fuzhou, China, as a case study, we selected seven typical behavior setting units. Environmental features were assessed through UAV imagery and on-site observations, while PAD-based visual questionnaires were employed to collect emotional responses from 159 children. By applying correlation analysis, random forest, and regression tree models, this study identified key environmental predictors of children’s emotional responses and revealed heterogeneous mechanisms across the three emotional dimensions. The results indicated that seasonal flowering/fruiting plants, accessible lawns, and structured play facilities were critical in supporting children’s pleasure, arousal, and dominance. Specifically, pleasure was primarily associated with sensory enjoyment and contextual aesthetics, arousal favored open grassy areas, and dominance was linked to environments with clear structure and manipulability. Based on these findings, this study proposes a spatial configuration strategy characterized by “nature as foundation, play encouraged, and structure clarified” to promote the positive development of children’s multidimensional emotional experiences. This research contributes empirical evidence on the role of physical environmental features in supporting children’s play behaviors and expands the theoretical understanding of the “emotional effects” of green spaces. While the findings are exploratory and context-specific, they emphasize the critical role of the sensory–behavioral–emotional chain in shaping children’s well-being and provide theoretical and practical guidance for the design of emotionally supportive, child-friendly, natural play environments in schools, parks, and residential areas. Full article

Radionuclide tracer technology, as a state-of-the-art tool for quantifying and monitoring soil erosion processes, has attracted much attention in global sustainable land management research in recent years. However, existing studies are fragmented in methodological applications, lack systematic knowledge integration and interdisciplinary perspectives, and lack global research trends and dynamic evolution of key themes. This study integrates Bibliometrix, VOSviewer, and CiteSpace to conduct bibliometric and knowledge mapping analysis of 1692 documents (2000–2023) in the Web of Science Core Collection, focusing on the overall developmental trends, thematic evolution, and progress of convergence and innovation. The main findings of the study are as follows: (1) China, the United States, and the United Kingdom are in a “three-legged race” at the national level, with China focusing on technological application innovation, the United States on theoretical breakthroughs, and the United Kingdom contributing significantly to methodological research; (2) “soil erosion” and “¹³⁷Cs” continue to be the core themes, while “climate change” and “human impact” on soil erosion and its reflection in radionuclide tracing became the focus of attention; and (3) multi-scale radionuclide tracing (watershed, slope), multi-method synergy (radionuclide tracing combined with RS, GIS, AI), and the integration of advanced measurement and control technologies (PGS, ARS) have become cutting-edge trends in soil erosion monitoring and control. This study provides three prospective research directions—the construction of a global soil erosion database, the policy transformation mechanism of the SDG interface, and the iterative optimization of multi-radionuclide tracer technology, which will provide scientific guidance for the realization of the sustainable management of soil erosion and the goal of zero growth of land degradation globally. Full article

In today’s fierce market competition, enterprises must quickly attract consumers’ attention to products and prompt them to make purchases. Based on regulatory focus theory, this study examines the impact of the congruence between different types of goal framing in advertising (promotion vs. prevention) and product types (hedonic vs. utilitarian) on individual consumer decision-making, as well as the underlying psychological mechanisms. The findings are as follows: (1) A goal-framing effect was observed, such that individuals allocated more attention and exhibited higher purchase intentions toward products presented with promotion-framed advertising. (2) A matching effect between goal-framing type and product type was identified: promotion framing increased purchase intentions for hedonic products, whereas prevention framing increased purchase intentions for utilitarian products. (3) Processing fluency mediated the effect of goal–product matching on consumer decision-making. (4) The presence of time pressure amplified the goal-framing effect, leading to stronger preferences under promotion-framed advertisements, as reflected in both longer fixation durations and higher purchase intentions. By integrating regulatory focus theory with product type matching, this study leverages eye-tracking data to reveal the cognitive processes underlying consumer decision-making and the moderating role of time pressure on goal-framing effects. The findings enrich the motivational perspective in consumer behavior research and provide empirical guidance for designing differentiated advertising strategies and optimizing advertising copy. Full article

This study aims to guide the management and service of highways towards a more efficient and intelligent direction, and also provides intelligent and green data support for achieving sustainable development goals. The forecasting of traffic flow at highway stations serves as the cornerstone for spatiotemporal analysis and is vital for effective highway management and control. Despite considerable advancements in data-driven traffic flow prediction, the majority of existing models fail to differentiate between directions. Specifically, entrance flow prediction has applications in dynamic route guidance, disseminating real-time traffic conditions, and offering optimal entrance selection suggestions. Meanwhile, exit flow prediction is instrumental for congestion and accident alerts, as well as for road network optimization decisions. In light of these needs, this study introduces an enhanced heterogeneous spatiotemporal graph network model tailored for predicting highway station traffic flow. To accurately capture the dynamic impact of upstream toll stations on the target station’s flow, we devise an influence probability matrix. This matrix, in conjunction with the covariance matrix across toll stations, updated graph structure data, and integrated external weather conditions, allows the attention mechanism to assign varied combination weights to the target toll station from temporal, spatial, and external standpoints, thereby augmenting prediction accuracy. We undertook a case study utilizing traffic flow data from the Chengdu-Chengyu station on the Sichuan Highway to gauge the efficacy of our proposed model. The experimental outcomes indicate that our model surpasses other baseline models in performance metrics. This study provides valuable insights for highway management and control, as well as for reducing traffic congestion. Furthermore, this research highlights the importance of using data-driven approaches to reduce carbon emissions associated with transportation, enhance resource allocation at toll plazas, and promote sustainable highway transportation systems. Full article