Search Results (337)

Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborative robots can tackle that can also greatly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. We introduce a multi-agent multi-machine-tending learning framework using mobile robots based on multi-agent reinforcement learning (MARL) techniques, with the design of a suitable observation and reward. Moreover, we integrate an attention-based encoding mechanism into the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine-tending scenarios. Our model (AB-MAPPO) outperforms MAPPO in this new challenging scenario in terms of task success, safety, and resource utilization. Furthermore, we provided an extensive ablation study to support our design decisions. Full article

As one of the primary contributors to carbon emissions in China, the transportation sector plays a pivotal role in achieving green and low-carbon development. Considering the spatio-temporal dependency characteristics of transportation carbon emissions driven by economic interactions and population mobility among provinces, this study proposes a predictive framework for transportation carbon emissions based on a spatio-temporal attention mechanism from the perspective of multi-province spatio-temporal synergy. First, the study conducts transportation carbon emission accounting by considering both transportation fuel consumption and electricity usage, followed by feature selection using an enhanced STIRPAT model. Second, it integrates the spatio-temporal attention mechanism with graph convolutional neural networks to construct a multi-province transportation carbon emission collaborative prediction model. Comparative experiments highlight the superior performance of deep learning methods and spatio-temporal correlation modeling in multi-province transportation carbon emission collaborative prediction. Finally, three future development scenarios are designed to analyze the evolution paths of transportation carbon emissions. The results indicate that technological innovation can significantly improve the efficiency of transportation emission reduction. Moreover, given that the eastern region and the central and western regions are at distinct stages of development, it is essential to develop differentiated emission reduction strategies tailored to local conditions to facilitate a green and low-carbon transformation in the transportation sector. Full article

With the increasing diversity of remote sensing modalities, multimodal image fusion improves target recognition accuracy but also introduces new security risks. Adversaries can inject small, imperceptible perturbations into a single modality to mislead model predictions, which undermines system reliability. Most existing defences are designed for single-modal inputs and face two key challenges in multimodal settings: 1. vulnerability to perturbation propagation due to static fusion strategies, and 2. the lack of collaborative mechanisms that limit overall robustness according to the weakest modality. To address these issues, we propose CAGMC-Defence, a cross-attention-guided multimodal collaborative defence framework for multimodal remote sensing. It contains two main modules. The Multimodal Feature Enhancement and Fusion (MFEF) module adopts a pseudo-Siamese network and cross-attention to decouple features, capture intermodal dependencies, and suppress perturbation propagation through weighted regulation and consistency alignment. The Multimodal Adversarial Training (MAT) module jointly generates optical and SAR adversarial examples and optimizes network parameters under consistency loss, enhancing robustness and generalization. Experiments on the WHU-OPT-SAR dataset show that CAGMC-Defence maintains stable performance under various typical adversarial attacks, such as FGSM, PGD, and MIM, retaining 85.74% overall accuracy even under the strongest white-box MIM attack ($ϵ=0.05$), significantly outperforming existing multimodal defence baselines. Full article

Anthracnose on Illicium verum Hook.f (I. verum) significantly affects the yield and quality of I. verum, and timely detection methods are urgently needed for early control. However, early warning is difficult due to two major challenges, including the sparse availability of optical remote sensing observations due to frequent cloud and rain interference, and the weak spectral responses caused by infestation during early stages. In this article, a framework for early warning of anthracnose on I. verum that combines high-frequency environmental (meteorological and topographical) data and Sentinel-2 remote sensing time-series data, along with a Time-Aware Long Short-Term Memory (T-LSTM) network incorporating an attentional mechanism (At-T-LSTM) was proposed. First, all available environmental and remote sensing data during the study period were analyzed to characterize the early anthracnose outbreaks, and sensitive features were selected as the algorithm input. On this basis, to address the issue of unequal temporal lengths between environmental and remote sensing time series, the At-T-LSTM model incorporates a time-aware mechanism to capture intra-feature temporal dependencies, while a Self-Attention layer is used to quantify inter-feature interaction weights, enabling effective multi-source features time-series fusion. The results show that the proposed framework achieves a spatial accuracy (F1-score) of 0.86 and a temporal accuracy of 83% in early-stage detection, demonstrating high reliability. By integrating remote sensing features with environmental drivers, this approach enables multi-feature collaborative modeling for the risk assessment and monitoring of I. verum anthracnose. It effectively mitigates the impact of sparse observations and significantly improves the accuracy of early warnings. Full article

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) has emerged as a distinct autoimmune demyelinating disorder, characterised by clinical, radiological, and immunopathological features that differentiate it from Multiple Sclerosis (MS) and AQP4+ neuromyelitis optica spectrum disorder (AQP4+NMOSD). This review provides a comprehensive synthesis of the evolving landscape of MOGAD, from its immunopathogenesis and diagnostic criteria to treatment strategies and global epidemiological insights. We explore the role of MOG-IgG antibodies in disease mechanisms, the utility of emerging biomarkers, and the prognostic value of tools like clinical scores or longitudinal MOG-IgG assessment. Special attention is given to regional disparities, with a focus on Latin America, highlighting diagnostic delays, access inequities, and unique clinical phenotypes. We also examine the limitations of current evidence, including gaps in long-term longitudinal follow-up and variability in diagnostic testing. Finally, we discuss global collaborative efforts and clinical trials that are shaping the future of personalised care in MOGAD. As the field advances, integrating biomarker-driven monitoring, equitable access to therapies, and regionally adapted guidelines will be essential to improving outcomes for patients worldwide. Full article

Despite the immense potential in environmental remediation, the translation of magnetic nanomaterials (MNMs) from laboratory innovations to practical, field-scale applications remains hindered by significant technical and environmental challenges. This is particularly evident in soil environments—which are inherently more complex than aquatic systems and have received comparatively less research attention. Beginning with an outline of the fundamental properties that make iron-based MNMs effective as adsorbents and catalysts for heavy metals and organic pollutants, this review systematically examines their core contaminant removal mechanisms. These include adsorption, catalytic degradation (e.g., via Fenton-like reactions), and magnetic recovery. However, the practical implementation of MNMs is constrained by several key limitations, such as particle agglomeration, oxidative instability, and reduced efficacy in multi-pollutant systems. More critically, major uncertainties persist regarding their long-term environmental fate and biocompatibility. In light of these challenges, we propose that future efforts should prioritize the rational design of stable, selective, and intelligent MNMs through advanced surface engineering and interdisciplinary collaboration. Full article

Remote sensing scene image classification has extensive application scenarios in fields such as land use monitoring and environmental assessment. However, traditional methodologies based on convolutional neural networks (CNNs) face considerable challenges caused by uneven image quality, imbalanced sample distribution, intra-class similarities and limited computing resources. To address such issues, this study proposes a hierarchical lightweight attention-enhanced network (HLAE-Net), which employs a hierarchical feature collaborative extraction (HFCE) strategy. By considering the differences in resolution and receptive field as well as the varying effectiveness of attention mechanisms across different network layers, the network uses different attention modules to progressively extract features from the images. This approach forms a complementary and enhanced feature chain among different layers, forming an efficient collaboration between various attention modules. In addition, an improved lightweight attention module group is proposed, including a lightweight dual coordinate spatial attention module (DCSAM), which captures spatial and channel information, as well as the lightweight multiscale spatial and channel attention module. These improved modules are incorporated into the featured average sampling (FAS) bottleneck and basic bottlenecks. The experiments were studied on four public standard datasets, and the results show that the proposed model outperforms several mainstream models from recent years in overall accuracy (OA). Particularly in terms of small training ratios, the proposed model shows competitive performance. Maintaining the parameter scale, it possesses both good classification ability and computational efficiency, providing a strong solution for the task of image classification. Full article

Socialized services for rural characteristic industries are becoming a key support for promoting rural industries’ transformation and upgrading. They are permeating the development process of modern agricultural service technologies, achieving significant progress in specialized fields such as mechanized operations and plant-protection services. However, challenges remain, including low efficiency in matching service resources and limited spatiotemporal coordination capabilities. With the deep integration of spatiotemporal information technology and knowledge graph technology, the enormous potential of semantic-level feature spatial representation in intelligent scheduling of service resources has been fully demonstrated, providing a new technical pathway to solve the above problem. This paper systematically analyzes the technological evolution trends of socialized services for rural characteristic industries and proposes a collaborative scheduling framework based on semantic feature space and spatiotemporal maps for characteristic industry service resources. At the technical architecture level, the paper aims to construct a spatiotemporal graph model integrating geographic knowledge graphs and temporal tree technology to achieve semantic-level feature matching between service demand and supply. Regarding implementation pathways, the model significantly improves the spatiotemporal allocation efficiency of service resources through cloud service platforms that integrate spatial semantic matching algorithms and dynamic optimization technologies. This paper conducts in-depth discussions and analyses on technical details such as agricultural semantic feature extraction, dynamic updates of rural service resources, and the collaboration of semantic matching and spatio-temporal matching of supply and demand relationships. It also presents relevant implementation methods to enhance technical integrity and logic, which is conducive to the engineering implementation of the proposed methods. The effectiveness of the proposed collaborative scheduling framework for service resources is proved by the synthesis of principal analysis, logical deduction and case comparison. We have proposed a practical “three-step” implementation path conducive to realizing the proposed method. Regarding application paradigms, this technical system will promote the transformation of rural industry services from traditional mechanical operations to an intelligent service model of “demand perception–intelligent matching–precise scheduling”. In the field of socialized services for rural characteristic industries, it is suggested that relevant institutions promote this technical framework and pay attention to the development trends of new technologies such as knowledge services, spatio-temporal services, the Internet of Things, and unmanned farms so as to promote the sustainable development of rural characteristic industries. Full article

With the increasing demand for intelligent mobility assistance among the visually impaired, machine guide dogs based on computer vision have emerged as an effective alternative to traditional guide dogs, owing to their flexible deployment and scalability. To enhance their visual perception capabilities in complex urban environments, this paper proposes an improved YOLOv8-based detection algorithm, termed TFP-YOLO, designed to recognize traffic signs such as traffic lights and crosswalks, as well as small obstacle objects including pedestrians and bicycles, thereby improving the target detection performance of machine guide dogs in complex road scenarios. The proposed algorithm incorporates a Triplet Attention mechanism into the backbone network to strengthen the perception of key regions, and integrates a Triple Feature Encoding (TFE) module to achieve collaborative extraction of both local and global features. Additionally, a P2 detection head is introduced to improve the accuracy of small object detection, particularly for traffic lights. Furthermore, the WIoU loss function is adopted to enhance training stability and the model’s generalization capability. Experimental results demonstrate that the proposed algorithm achieves a detection accuracy of 93.9% and a precision of 90.2%, while reducing the number of parameters by 17.2%. These improvements significantly enhance the perception performance of machine guide dogs in identifying traffic information and obstacles, providing strong technical support for subsequent path planning and embedded deployment, and demonstrating considerable practical application value. Full article

In this study, we investigate a deep learning-based vehicle fault prediction model aimed at achieving accurate prediction of vehicle faults by analyzing the correlations among different faults and the impact of critical faults on future fault development. To this end, we propose a collaborative modeling approach utilizing multiple attention mechanisms. This approach incorporates a graph attention mechanism for the fusion representation of fault correlation information and employs a novel learning method that combines a Long Short-Term Memory (LSTM) network with an attention mechanism to capture the impact of key faults. Based on experimental validation using real-world vehicle fault record data, the model significantly outperforms existing prediction models in terms of fault prediction accuracy. 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

Leaves are the key organs in photosynthesis and nutrient production, and leaf counting is an important indicator of banana plant health and growth rate. However, in complex orchard environments, leaves often overlap, the background is cluttered, and illumination varies, making accurate segmentation and detection challenging. To address these issues, we propose a lightweight banana leaf detection and counting method deployable on embedded devices, which integrates a space–depth-collaborative reasoning strategy with multi-scale feature enhancement to achieve efficient and precise leaf identification and counting. For complex background interference and occlusion, we design a multi-scale attention guided feature enhancement mechanism that employs a Mixed Local Channel Attention (MLCA) module and a Self-Ensembling Attention Mechanism (SEAM) to strengthen local salient feature representation, suppress background noise, and improve discriminability under occlusion. To mitigate feature drift caused by environmental changes, we introduce a task-aware dynamic scale adaptive detection head (DyHead) combined with multi-rate depthwise separable dilated convolutions (DWR_Conv) to enhance multi-scale contextual awareness and adaptive feature recognition. Furthermore, to tackle instance differentiation and counting under occlusion and overlap, we develop a detection-guided space–depth position modeling method that, based on object detection, effectively models the distribution of occluded instances through space–depth feature description, outlier removal, and adaptive clustering analysis. Experimental results demonstrate that our YOLOv8n MDSD model outperforms the baseline by 2.08% in mAP50-95, and achieves a mean absolute error (MAE) of 0.67 and a root mean square error (RMSE) of 1.01 in leaf counting, exhibiting excellent accuracy and robustness for automated banana leaf statistics. Full article

The efficient coordination of multi-Unmanned Aerial Vehicle (UAV) systems in the increasingly complex domain of aerial tasks is hampered by significant challenges, including partial observability, low sample efficiency, and difficulties in inter-agent coordination. To address these issues, this paper introduces a novel Causal Spatio-Temporal Multi-Agent Soft Actor–Critic (CaST-MASAC) framework. At its core, CaST-MASAC integrates two key innovations: (1) a spatio-temporal attention (STa) module that extracts features from historical observations to enable accurate target trajectory prediction and dynamic task assignment, thereby enhancing situational awareness and collaborative decision-making in highly dynamic and partially observable environments; and (2) a Causal Inference Experience Replay (CIER) mechanism that significantly improves sample efficiency and convergence speed by identifying and prioritizing experiences with a high causal impact on the task success. Evaluated in 4v4 and 2v2 multi-UAV aerial coordination simulation environments, CaST-MASAC demonstrates superior performance over state-of-the-art baselines such as MAPPO and QMIX in terms of task success rate, cumulative reward, and decision efficiency. Furthermore, extensive ablation studies validate the critical contributions of both the STa and CIER modules to the framework’s overall performance. Consequently, CaST-MASAC offers a novel and effective approach for developing robust and efficient multi-agent coordination strategies in complex dynamic environments. Full article

To address the challenges of user behavior sparsity and insufficient utilization of course semantics on MOOC platforms, this paper proposes a personalized recommendation method that integrates user behavioral sequences with course textual semantic features. First, shallow word-level features from course titles are extracted using FastText, and deep contextual semantic representations from course descriptions are obtained via a fine-tuned BERT model. The two sets of semantic features are concatenated to form a multi-level semantic representation of course content. Next, the fused semantic features are mapped into the same vector space as course ID embeddings through a linear projection layer and combined with the original course ID embeddings via an additive fusion strategy, enhancing the model’s semantic perception of course content. Finally, the fused features are fed into an improved SASRec model, where a multi-head self-attention mechanism is employed to model the evolution of user interests, enabling collaborative recommendations across behavioral and semantic modalities. Experiments conducted on the MOOCCubeX dataset (1.26 million users, 632 courses) demonstrated that the proposed method achieved NDCG@10 and HR@10 scores of 0.524 and 0.818, respectively, outperforming SASRec and semantic single-modality baselines. This study offers an efficient yet semantically rich recommendation solution for MOOC scenarios. Full article

Hyperspectral images (HSIs), with their rich spectral information, are widely used in remote sensing; yet the inherent trade-off between spectral and spatial resolution in imaging systems often limits spatial details. Single-image hyperspectral super-resolution (HSI-SR) seeks to recover high-resolution HSIs from a single low-resolution input, but the high dimensionality and spectral redundancy of HSIs make this task challenging. In HSIs, spectral signatures and spatial textures often exhibit intrinsic symmetries, and preserving these symmetries provides additional physical constraints that enhance reconstruction fidelity and robustness. To address these challenges, we propose the Dynamic Spectral Collaborative Super-Resolution Network (DSCSRN), an end-to-end framework that integrates physical modeling with deep learning and explicitly embeds spatial–spectral symmetry priors into the network architecture. DSCSRN processes low-resolution HSIs with a Cascaded Residual Spectral Decomposition Network (CRSDN) to compress redundant channels while preserving spatial structures, generating accurate abundance maps. These maps are refined by two Synergistic Progressive Feature Refinement Modules (SPFRMs), which progressively enhance spatial textures and spectral details via a multi-scale dual-domain collaborative attention mechanism. The Dynamic Endmember Adjustment Module (DEAM) then adaptively updates spectral endmembers according to scene context, overcoming the limitations of fixed-endmember assumptions. Grounded in the Linear Mixture Model (LMM), this unmixing–recovery–reconstruction pipeline restores subtle spectral variations alongside improved spatial resolution. Experiments on the Chikusei, Pavia Center, and CAVE datasets show that DSCSRN outperforms state-of-the-art methods in both perceptual quality and quantitative performance, achieving an average PSNR of 43.42 and a SAM of 1.75 (×4 scale) on Chikusei. The integration of symmetry principles offers a unifying perspective aligned with the intrinsic structure of HSIs, producing reconstructions that are both accurate and structurally consistent. Full article