Search Results (298)

Attention can be automatically captured by the distractor that matches the representation of working memory (WM) in search tasks, impairing visual search efficiency and resulting in attentional capture effects. The resource hypothesis of visual search predicts that resource allocation affects attentional capture. However, previous studies have shown partly opposing results inconsistent with this prediction. The purpose of this study is to assess the connection between attentional capture and WM resource allocation. Two experiments were conducted to combine the attentional capture paradigm with continuous delayed-estimation tasks. In Experiment 1, we manipulated the number of memory items between one and two and measured the WM representation precision as well as the magnitude of attentional capture. In Experiment 2, we manipulated resource allocation using a retro-cue task with the presentation of two memory items. In Experiment 1, the results show that when remembering one item, a single-item representation had higher precision compared to the scenario for remembering two items, and it also involved a greater allocation of WM resources. However, there was no significant difference in the magnitude of attentional capture effects between the two conditions. In Experiment 2, the results show that memory precision was higher when the cue pointed to the item compared to when it did not, but there was no significant difference in the magnitude of attentional capture effects between the cued-match and non-cued-match conditions. The findings show that the size of attentional capture effects based on WM is unaffected by the distribution of WM resources. Attentional capture effects may reflect the attention bias of WM representation that occurs in preparation stage of memory-based attentional guidance. Full article

The growing global environmental crisis calls for fundamental transformations in production and consumption systems, but the understanding of how circular economy strategies translate into quantifiable environmental benefits remains fragmented across sectors and geographies. The objective of this study is to synthesize current scientific knowledge on the circular economy as an environmental mitigation strategy, identifying conceptual convergences, methodological patterns, geographic distributions, and critical knowledge gaps. A systematic review combined with a bibliometric analysis of 62 peer-reviewed articles published between 2018 and 2024, retrieved from Scopus, Web of Science, ScienceDirect, Springer Link and Wiley Online Library, was conducted following the PRISMA 2020 guidelines. The results reveal a marked methodological convergence around life cycle assessment, with Europe dominating the scientific output (58% of the corpus). Four complementary conceptual frameworks emerged, emphasizing closed-loop material flows, environmental performance, integration of economic sustainability and business model innovation. The thematic analysis identified bioenergy and waste valorization as the most mature implementation pathways, constituting 23% of the research emphasis. However, critical gaps remain: geographic concentration limits the transferability of knowledge to diverse socioeconomic contexts; social, cultural and behavioral dimensions remain underexplored (12% of publications); and environmental justice considerations receive negligible attention. Crucially, the evidence reveals nonlinear relationships between circularity metrics and environmental outcomes, calling into question automatic benefits assumptions. This review contributes to an integrative synthesis that advances theoretical understanding of circularity-environment relationships while providing evidence-based guidance for researchers, practitioners, and policy makers involved in transitions to the circular economy. Full article

Shipborne UAV-assisted dock is an important way to recover unmanned systems for remote water surface low-altitude detection. The lack of resisting deck disturbances capability for UAV autonomous landing in dynamic dock stations has led to the inability of traditional hovering recovery methods for single UAV guidance and flight attitude control systems to meet the growing demand for landing assistance. In this work, we present a shipborne manipulator arm designed to grasp drones that use low-altitude visual servo technology for landing on the water surface. The shipborne manipulator arm is fabricated as a key component of a seaplane drone dock comprising a ship-type embedded drone storage, a packaged helistop for power transfer and UAV recovery, and a multi-degree-of-freedom arm integrated with multi-source information sensors for the treatment of air-to-water-related airplane crashes. Dynamic model tests have demonstrated that the end-effector of the shipborne manipulator arm stabilizes and performs optimally for water surface disturbances. A down-to-top grasp docking paradigm for a UAV-assisted perching on a shipborne helistop that enables the charging components of the station system to be equipped automatically to ensure that the drone performs its mission in the best condition is also presented. The surface grasp experiments have verified the efficacy of this grasp paradigm when compared to the traditional autonomous landing method. Full article

The Segment Anything Model (SAM), a foundational vision model, struggles with fully automatic segmentation of specific objects. Its “segment everything” mode, reliant on a grid-based prompt strategy, suffers from localization blindness and computational redundancy, leading to poor performance on tasks like Dichotomous Image Segmentation (DIS). To address this, we propose PMG-SAM, a framework that introduces a Pre-Mask Guided paradigm for automatic targeted segmentation. Our method employs a dual-branch encoder to generate a coarse global Pre-Mask, which then acts as a dense internal prompt to guide the segmentation decoder. A key component, our proposed Dense Residual Fusion Module (DRFM), iteratively co-refines multi-scale features to significantly enhance the Pre-Mask’s quality. Extensive experiments on challenging DIS and Camouflaged Object Segmentation (COS) tasks validate our approach. On the DIS-TE2 benchmark, PMG-SAM boosts the maximal F-measure from SAM’s $0.283$ to $0.815$. Notably, our fully automatic model’s performance surpasses even the ground-truth bounding box prompted modes of SAM and SAM2, while using only $22.9$ M trainable parameters ($58.8$% of SAM2-Tiny). PMG-SAM thus presents an efficient and accurate paradigm for resolving the localization bottleneck of large vision models in prompt-free scenarios. Full article

This article presents the design, fabrication, and experimental validation of a centimeter-scale autonomous robot that achieves bidirectional locomotion and trajectory control through 3D-printed resonators actuated by piezoelectricity and integrated with miniature legs. Building on previous works that employed piezoelectric bimorphs, the proposed system replaces them with custom-designed 3D-printed resonant plates that exploit the excitation of standing waves (SW) to generate motion. Each resonator is equipped with strategically positioned passive legs that convert vibratory energy into effective thrust, enabling both linear and rotational movement. A differential drive configuration, implemented through two independently actuated resonators, allows precise guidance and the execution of complex trajectories. The robot integrates onboard control electronics consisting of a microcontroller and inertial sensors, which enable closed-loop trajectory correction via a PD controller and allow autonomous navigation. The experimental results demonstrate high-precision motion control, achieving linear displacement speeds of 8.87 mm/s and a maximum angular velocity of 37.88°/s, while maintaining low power consumption and a compact form factor. Furthermore, the evaluation using the mean absolute error (MAE) yielded a value of 0.83° in trajectory tracking. This work advances the field of robotics and automatic control at the insect scale by integrating efficient piezoelectric actuation, additive manufacturing, and embedded sensing into a single autonomous platform capable of agile and programmable locomotion. Full article

Efficient and intuitive programming strategies are essential for enabling robots to adapt to small-batch, high-mix production scenarios. Mixed reality (MR) and programming by demonstration (PbD) have shown great potential to lower the programming barrier and enhance human–robot interaction by leveraging natural human guidance. However, traditional offline programming methods, while capable of generating industrial-grade trajectories, remain time-consuming, costly to debug, and heavily dependent on expert knowledge. Conversely, existing MR-based PbD approaches primarily focus on improving intuitiveness but often suffer from low trajectory quality due to hand jitter and the lack of refinement mechanisms. To address these limitations, this paper introduces a coarse-to-fine human–robot collaborative programming paradigm. In this paradigm, the operator’s role is elevated from a low-level “trajectory drawer” to a high-level “task guider”. By leveraging sparse key points as guidance, the paradigm decouples high-level human task intent from machine-level trajectory planning, enabling their effective integration. The feasibility of the proposed system is validated through two industrial case studies and comparative quantitative experiments against conventional programming methods. The results demonstrate that the coarse-to-fine paradigm significantly improves programming efficiency and usability while reducing operator cognitive load. Crucially, it achieves this without compromising the final output, automatically generating smooth, high-fidelity trajectories from simple user inputs. This work provides an effective pathway toward reconciling programming intuitiveness with final trajectory quality. Full article

As a critical parameter for describing oil–water two-phase flow behavior, relative permeability curves are widely applied in field development, dynamic forecasting, and reservoir numerical simulation. This study addresses the issue of relative permeability anisotropy, focusing on the seepage characteristics of two typical bedding structures in sandstone reservoirs—tabular cross-bedding and parallel bedding—through multi-directional displacement experiments. A novel anisotropic relative permeability testing apparatus was employed to conduct displacement experiments on cubic core samples, comparing the performance of the explicit Johnson–Bossler–Naumann (JBN) method, based on Buckley–Leverett theory, with the implicit Automatic History Matching (AHM) method, which demonstrated superior accuracy. The results indicate that displacement direction significantly influences seepage efficiency. For cross-bedded cores, displacement perpendicular to bedding (Z-direction) achieved the highest displacement efficiency (75.09%) and the lowest residual oil saturation (22%), primarily due to uniform fluid distribution and efficient pore utilization. In contrast, horizontal displacement exhibited lower efficiency and higher residual oil saturation due to preferential flow path effects. In parallel-bedded cores, vertical displacement improved efficiency by 18.06%, approaching ideal piston-like displacement. Microscale analysis using Nuclear Magnetic Resonance (NMR) and Computed Tomography (CT) scanning further revealed that vertical displacement effectively reduces capillary resistance and promotes uniform fluid distribution, thereby minimizing residual oil formation. This study underscores the strong interplay between displacement direction and bedding structure, validating AHM’s advantages in characterizing anisotropic reservoirs. By integrating experimental innovation with advanced computational techniques, this work provides critical theoretical insights and practical guidance for optimizing reservoir development strategies and enhancing the accuracy of numerical simulations in complex sandstone reservoirs. Full article

In modern high-demand applications, such as real-time video communication, cloud gaming, and high-definition live streaming, achieving both superior transmission speed and high visual fidelity is paramount. However, unstable networks and packet loss remain major bottlenecks, making accurate and low-latency video error concealment a critical challenge. Traditional error control strategies, such as Forward Error Correction (FEC) and Automatic Repeat Request (ARQ), often introduce excessive latency or bandwidth overhead. Meanwhile, receiver-side concealment methods struggle under high motion or significant packet loss, motivating the exploration of predictive models. SimVP-v2, with its efficient convolutional architecture and Gated Spatiotemporal Attention (GSTA) mechanism, provides a strong baseline by reducing complexity and achieving competitive prediction performance. Despite its merits, SimVP-v2’s reliance on 2D convolutions for implicit temporal aggregation limits its capacity to capture complex motion trajectories and long-term dependencies. This often results in artifacts such as motion blur, detail loss, and accumulated errors. Furthermore, its single-modality design ignores the complementary contextual cues embedded in the audio stream. To overcome these issues, we propose A3DSimVP (Audio- and 3D-Enhanced SimVP-v2), which integrates explicit spatio-temporal modeling with multimodal feature fusion. Architecturally, we replace the 2D depthwise separable convolutions within the GSTA module with their 3D counterparts, introducing a redesigned GSTA-3D module that significantly improves motion coherence across frames. Additionally, an efficient audio–visual fusion strategy supplements visual features with contextual audio guidance, thereby enhancing the model’s robustness and perceptual realism. We validate the effectiveness of A3DSimVP’s improvements through extensive experiments on the KTH dataset. Our model achieves a PSNR of 27.35 dB, surpassing the 27.04 of the SimVP-v2 baseline. Concurrently, our improved A3DSimVP model reduces the loss metrics on the KTH dataset, achieving an MSE of 43.82 and an MAE of 385.73, both lower than the baseline. Crucially, our LPIPS metric is substantially lowered to 0.22. These data tangibly confirm that A3DSimVP significantly enhances both structural fidelity and perceptual quality while maintaining high predictive accuracy. Notably, A3DSimVP attains faster inference speeds than the baseline with only a marginal increase in computational overhead. These results establish A3DSimVP as an efficient and robust solution for latency-critical video applications. Full article

Landslide disasters are complex spatiotemporal phenomena. Existing deep learning (DL) models for remote sensing (RS) image analysis primarily exploit shallow visual features, inadequately incorporating critical geological, geographical, and environmental knowledge. This limitation impairs detection accuracy and generalization, especially in complex terrains and diverse vegetation conditions. We propose Knowledge Graph Embedding in Swin Feature Pyramid Networks (KGE–SwinFpn), a novel RS landslide segmentation framework that integrates explicit domain knowledge with deep features. First, a comprehensive landslide knowledge graph is constructed, organizing multi-source factors (e.g., lithology, topography, hydrology, rainfall, land cover, etc.) into entities and relations that characterize controlling, inducing, and indicative patterns. A dedicated KGE Block learns embeddings for these entities and discretized factor levels from the landslide knowledge graph, enabling their fusion with multi-scale RS features in SwinFpn. This approach preserves the efficiency of automatic feature learning while embedding prior knowledge guidance, enhancing data–knowledge–model coupling. Experiments demonstrate significant outperformance over classic segmentation networks: on the Yuan-yang dataset, KGE–SwinFpn achieved 96.85% pixel accuracy (PA), 88.46% mean pixel accuracy (MPA), and 82.01% mean intersection over union (MIoU); on the Bijie dataset, it attained 96.28% PA, 90.72% MPA, and 84.47% MIoU. Ablation studies confirm the complementary roles of different knowledge features and the KGE Block’s contribution to robustness in complex terrains. Notably, the KGE Block is architecture-agnostic, suggesting broad applicability for knowledge-guided RS landslide detection and promising enhanced technical support for disaster monitoring and risk assessment. Full article

The rising global burden of chronic diseases highlights the limitations of traditional dietary guidelines. Precision Nutrition (PN) aims to deliver personalized dietary advice to optimize individual health, and the effective implementation of PN fundamentally relies on comprehensive and accurate dietary data. However, conventional dietary assessment methods often suffer from quantification errors and poor adaptability to dynamic changes, leading to inaccurate data and ineffective guidance. Machine learning (ML) offers a powerful suite of tools to address these limitations, enabling a paradigm shift across the nutritional management pipeline. Using dietary data as a thematic thread, this article outlines this transformation and synthesizes recent advances across dietary assessment, in-depth mining, and nutritional intervention. Additionally, current challenges and future trends in this domain are also further discussed. ML is driving a critical shift from a subjective, static mode to an objective, dynamic, and personalized paradigm, enabling a loop nutrition management framework. Precise food recognition and nutrient estimation can be implemented automatically with ML techniques like computer vision (CV) and natural language processing (NLP). Integrating with multiple data sources, ML is conducive to uncovering dietary patterns, assessing nutritional status, and deciphering intricate nutritional mechanisms. It also facilitates the development of personalized dietary intervention strategies tailored to individual needs, while enabling adaptive optimization based on users’ feedback and intervention effectiveness. Although challenges regarding data privacy and model interpretability persist, ML undeniably constitutes the vital technical support for advancing PN into practical reality. Full article

Background/Objectives: This study systematically compared the performance of U-Net and variants for automatic analysis of carotid intima-media thickness (CIMT) in ultrasound images, focusing on segmentation accuracy and real-time efficiency. Methods: Ten models were trained and evaluated using a publicly available Carotid Ultrasound Boundary Study (CUBS) dataset (2176 images from 1088 subjects). Images were preprocessed using histogram-based smoothing and resized to a resolution of 256 × 256 pixels. Model training was conducted using identical hyperparameters (50 epochs, batch size 8, Adam optimizer with a learning rate of 1 × 10⁻⁴, and binary cross-entropy loss). Segmentation accuracy was assessed using Dice, Intersection over Union (IoU), Precision, Recall, and Accuracy metrics, while real-time performance was evaluated based on training/inference times and the model parameter counts. Results: All models achieved high accuracy, with Dice/IoU scores above 0.80/0.67. Attention U-Net achieved the highest segmentation accuracy, while UNeXt demonstrated the fastest training/inference speeds (approximately 420,000 parameters). Qualitatively, UNet++ produced smooth and natural boundaries, highlighting its strength in boundary reconstruction. Additionally, the relationship between the model parameter count and Dice performance was visualized to illustrate the tradeoff between accuracy and efficiency. Conclusions: This study provides a quantitative/qualitative evaluation of the accuracy, efficiency, and boundary reconstruction characteristics of U-Net-based models for CIMT segmentation, offering guidance for model selection according to clinical requirements (accuracy vs. real-time performance). Full article

Background/Objectives: Occlusal splints are widely used for managing sleep bruxism (SB), providing uniform contact across the entire dentition in the centric relation. Nonetheless, different guidance schemes, such as bilateral balanced occlusion (BBO) and canine guidance (CG), are used during eccentric movements, and the optimal design remains unclear. This study compared the effects of BBO and CG on masticatory muscle activity, sleep architecture, and subjective outcomes during sleep. Methods: This non-blinded randomized crossover trial enrolled 24 healthy adults diagnosed with SB (16 men and 8 women; mean age, 26.1 years) who were randomly assigned to either a BBO-first or CG-first sequence. Individual splints of both types were milled from the polymethyl methacrylate discs. After a 5-night baseline period, each splint was worn for 33 nights in a home environment, and data from nights 29 to 33 were analyzed. Masseter muscle activity was assessed using single-channel electromyography (EMG), yielding EMG parameters, including integrated EMG per hour, number of episodes and bursts per hour, mean episode duration, and total episode duration per hour. Sleep architecture was assessed using portable polysomnography with automatic scoring, and subjective outcomes were assessed for sleep disturbance, morning symptoms, and splint comfort. Differences between splints were analyzed using Wilcoxon signed-rank tests (α = 0.05). Results: Twenty-three participants completed the study. No statistically significant differences were found between the BBO and CG splints for any EMG parameters, sleep variables, or subjective measures. Conclusions: Splint guidance design differences showed no significant effects; however, smaller, potentially clinically relevant effects cannot be excluded. Full article

Co-design in healthcare settings requires teams to utilize each other’s knowledge effectively, but practical guidance and simple methods for observing collaboration are often lacking. We tested whether a lightweight AI assistant that guides the process—and automatically logs who speaks, when, and how work progresses—can make teamwork easier to manage and easier to track. Six four-person teams completed the same five-phase session. The assistant nudged timing, turn-taking, and artifact hand-offs; all interactions were recorded in a shared workspace. We assessed usability and acceptance, expert-rated product quality (technical performance), perceived team performance, and self-rated technical contribution, and we summarized basic log signals of participation and pacing (e.g., turn-taking balance, average turn duration). Analyses were descriptive. All teams finished the protocol with complete logs. Outcomes were favorable (expert ratings averaged 4.18/5; perceived performance 6.14/7; self-rated contribution 4.08/5). Teams with more balanced participation and clearer pacing tended to report better performance, whereas simply having more turns did not. A process-guiding AI assistant can quantify teamwork behaviors as markers of collective intelligence and support reflection in everyday clinical co-design; future work will examine the generalizability of these findings across different sites. Full article

MCSGP (Multicolumn Countercurrent Solvent Gradient Purification) with AutoPeak control is increasingly used for production of synthetic peptides and oligonucleotides at scale, requiring guidance on how to perform regulatory-compliant Process Validation. This work, for the first time, presents a Process Characterization and Process Performance Qualification approach to support regulatory filings of therapeutics produced using MCSGP, based on the relevant Process Validation guidelines. The approach was demonstrated for the purification of synthetic Bivalirudin. During Process Characterization, MCSGP process parameter criticality was investigated, and the gradient slope was classified as a critical process parameter to be controlled within tighter limits. As a further outcome of Process Characterization, a supervision strategy was developed and verified in four Process Performance Qualification MCSGP runs. The strategy was backed by AutoPeak, a UV-based Process Analytical Technology. The Process Validation/Process Performance Qualification (PPQ) runs not only confirmed the selected control and supervision strategy but also the advantages of MCSGP/AutoPeak as a continuous manufacturing technology, including the fully automatic operation and the reduction in in-process control sampling and Process Mass Intensity (PMI). In the presented case, the PMI was reduced from around 5200 to 1400 kg/kg, the number of in-process controls (IPCs) was reduced from 81 IPCs (60 cm i.D. column batch) per kg to 3.2 IPCs per kg (2 × 30 cm i.D. column MCSGP), while yield (gross-to-gross) increased from 57% to 62%, comparing MCSGP/AutoPeak to a process with extensive side-cut recycling. Full article

In modern industrial systems, diagnosing faults in the rolling bearings of high-speed rotating machinery remains a considerable challenge due to the scarcity of reliable fault samples and the inherent complexity of the diagnostic task. To address these limitations, this study proposes an intelligent fault diagnosis method that integrates a generative adversarial network (GAN) with a convolutional block attention mechanism (CBAM). First, after systematically evaluating several loss functions, a GAN based on the Wasserstein distance loss function was adopted to generate high-quality synthetic vibration samples, effectively augmenting the training dataset. Subsequently, a convolutional block attention mechanism-based convolutional neural network (CBAM-CNN) was developed. By adaptively emphasizing salient features through channel and spatial attention modules, the CBAM-CNN improves feature extraction and recognition performance under limited-sample conditions. To validate the proposed method, an experimental platform for a two-speed automatic mechanical transmission (2AMT) of an electric vehicle was developed, and diagnostic experiments were conducted on high-speed rolling bearings. The results indicate that, under extremely severe conditions, CBAM-CNN achieves a diagnostic accuracy of 96.64% for rolling element pitting defects using only 10% of authentic samples. For composite faults, the model maintains an average accuracy above 97%, demonstrating strong generalization capability. These findings provide solid theoretical support and practical engineering guidance for rolling bearing fault diagnosis under few-shot conditions. Full article