Search Results (172)

Background/Objectives: End-of-life care is a vital part of nursing education that has been overlooked until recent years. Advance care planning should be incorporated into the prelicensure nursing curriculum to build student nurses’ confidence in aiding patients and families with their preferred future care plans. Advance care planning tools, such as the Advance Health Care Directive (AHCD) and Five Wishes, provide experiential learning opportunities that bridge theoretical knowledge with real-world patient advocacy. In this study, students were asked to complete either the AHCD or Five Wishes document as though planning for their own end-of-life care, encouraging personal reflection and professional insight. Embedding these assignments into nursing education strengthens students’ confidence in facilitating end-of-life discussions. This study applied Kolb’s experiential learning theory, including concrete experience, reflective observation, abstract conceptualization, and active experimentation, to explore student nurses’ perspectives on the Advance Health Care Directive and Five Wishes assignments, as well as their understanding of end-of-life care. Methods: This study used an exploratory–descriptive qualitative design featuring one open-ended question to collect students’ views on the assignments. Results: The final sample comprised 67 prelicensure student nurses from Bachelor of Science and Entry-Level Master’s programs. The Advance Health Care Directive and/or Five Wishes assignment enhanced students’ understanding of end-of-life decision-making. Conclusions: It is essential to complete the assignment and immerse oneself in an end-of-life situation to grasp patients’ perspectives and concerns regarding when to engage in difficult conversations with their patients. Full article

A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a multifunctional mobile manipulation robot by integrating perception, mapping, navigation, object detection, and grasping functions into a seamless workflow to conduct search-and-fetch tasks. To realize navigation and collision avoidance in complex environments, a new hierarchical motion planning strategy is proposed by fusing global and local planners. Control Lyapunov Function (CLF) and Control Barrier Function (CBF) are employed to realize path tracking and to guarantee safety during navigation. The convolutional neural network and the gripper’s kinematic constraints are adopted to construct a learning-optimization hybrid grasping algorithm to generate precise grasping poses. The efficiency of the developed mobile manipulation robot is demonstrated by performing indoor fetching experiments, showcasing its promising capabilities in real-world applications. Full article

Remote sensing visual grounding, a pivotal technology bridging natural language and high-resolution remote sensing images, holds significant application value in disaster monitoring, urban planning, and related fields. However, it faces critical challenges due to the inherent scale heterogeneity, semantic complexity, and annotation scarcity of remote sensing data. This paper first reviews the development history of remote sensing visual grounding, providing an overview of the basic background knowledge, including fundamental concepts, datasets, and evaluation metrics. Then, it categorizes methods by whether they employ large language models as a pedestal, and provides in-depth analyses of the innovations and limitations of Transformer-based and multimodal large language model-based methods. Furthermore, focusing on remote sensing image characteristics, it discusses cutting-edge techniques such as cross-modal feature fusion, language-guided visual optimization, multi-scale, and hierarchical feature processing, open-set expansion and efficient fine-tuning. Finally, it outlines current bottlenecks and proposes valuable directions for future research. As the first comprehensive review dedicated to remote sensing visual grounding, this work is a reference resource for researchers to grasp domain-specific concepts and track the latest developments. Full article

In the rapid process of urbanization, urban agglomerations have become a key driving factor for regional development and spatial reorganization. The formation and development of urban agglomerations rely on communication between cities. However, the spatiotemporal characteristics of intercity travelers are not fully grasped throughout the entire trip chain. This study proposes a spatiotemporal analysis method for intercity travel in urban agglomerations by constructing origin-to-destination (OD) trip chains using smartphone data, with the Beijing–Tianjin–Hebei urban agglomeration as a case study. The study employed Cramer’s V and Spearman correlation coefficients for multivariate feature selection, identifying 12 key variables from an initial set of 20. Then, optimal cluster configuration was determined via silhouette analysis. Finally, the K-prototypes algorithm was applied to cluster 161,797 intercity trip chains across six transportation corridors in 2019 and 2021, facilitating a comparative spatiotemporal analysis of travel patterns. Results show the following: (1) Intercity travelers are predominantly males aged 19–35, with significantly higher weekday volumes; (2) Modal split exhibits significant spatial heterogeneity—the metro predominates in Beijing while road transport prevails elsewhere; (3) Departure hubs’ waiting times increased significantly in 2021 relative to 2019 baselines; (4) Increased metro mileage correlates positively with extended intra-city travel distances. The results substantially contribute to transportation planning, particularly in optimizing multimodal hub operations and infrastructure investment allocation. Full article

Background: Self-controlled motor imagery combined with assistive devices is promising for enhancing neurorehabilitation. This study developed a soft, Flexible Exoskeleton (flexEXO) for finger movements and investigated whether self-controlled motor tasks facilitate stronger cortical activation than externally controlled conditions. Methods: Twenty-one healthy participants performed grasping tasks under four conditions: Self-Controlled Motion (SCC), Other-Controlled Motion (OCC), Self-Controlled Imagery Only (SCIOC), and Other-Controlled Imagery Only (OCIOC). EEG data were recorded, focusing on event-related desynchronization (ERD) in the μ and β bands during imagery and motion and event-related synchronization (ERS) in the β band during feedback. Source localization was performed using eLORETA. Results: Higher μERD and βERD were observed during self-controlled tasks, particularly in the primary motor cortex and supplementary motor area. Externally controlled tasks showed enhanced activation in the inferior parietal lobule and secondary somatosensory cortex. βERS did not differ significantly across conditions. Source localization revealed that self-controlled tasks engaged motor planning and error-monitoring regions more robustly. Conclusions: The flexEXO device and the comparison of brain activity under different conditions provide insights into the neural mechanisms of motor control and have implications for neurorehabilitation. Full article

Understanding the complexity of urban systems remains a significant challenge for researchers and practitioners in urban planning and governance. Cities function as multifaceted systems composed of interconnected subsystems with nonlinear interactions, making the design of effective interventions to enhance sustainability and liveability particularly challenging. Spatial modelling has gained prominence in recent decades, fuelled by advances in digital technologies and the advent of digital twins as decision support tools. To fully harness these innovations, it is essential to grasp their underlying principles, strengths, and limitations, and to select the most suitable modelling approach for specific applications. This paper examines two contrasting spatial modelling paradigms: top-down and bottom-up. Specifically, it focuses on Space Syntax and Agent-Based Modelling as representative tools of each approach, analyzing their potential applications in urban planning. This discussion delves into the effectiveness of the proposed methodologies in analyzing crime dynamics—selected as a representative application field—at the micro-urban scale. It highlights the insights each approach offers, emphasizing their contributions to understanding the spatial and environmental factors influencing crime patterns. Finally, this paper explores the potential for integrating these methodologies to develop hybrid models that capture both spatial structure and emergent behaviours, offering enhanced support for sustainable urban policies and planning. Full article

To address the challenge of low operational efficiency in apple harvesting robots, this study proposes an adaptive grasping sequence planning methodology that combines Self-Organizing Maps (SOMs) and genetic algorithms (GAs). The proposed adaptive SOM—GA hybrid algorithm aims to minimize cycle time by optimizing the path planning between the fruit detection and grasping phases. First of all, we propose a density-aware adaptive mechanism that dynamically adjusts planning strategies based on fruit count thresholds. In addition, the proposed grasping sequence planning framework for high-density dwarf cultivation (HDDC) orchards is validated through threshold sensitivity analysis and empirical analysis of over 500 real-world fruit distribution samples. Finally, comparative experiments demonstrate that our proposed method reduces path length in high-density scenarios. Statistical analysis reveals a bimodal fruit distribution, which aligns the algorithm’s adaptive thresholds with real-world operational demands. These advancements improve theoretical research and enhance the commercial viability in agricultural robotics. Full article

This study proposes a D-optimality-based viewpoint selection method to improve visual assistance for a manipulator by optimizing camera placement. The approach maximizes the information gained from visual observations, reducing uncertainty in object recognition and localization. A mathematical framework utilizing D-optimality criteria is developed to determine the most informative camera viewpoint in real time. The proposed method is integrated into a robotic system where a mobile robot adjusts its viewpoint to support the manipulator in grasping and placing tasks. Experimental evaluations demonstrate that D-optimality-based viewpoint selection improves recognition accuracy and task efficiency. The results suggest that optimal viewpoint planning can enhance perception robustness, leading to better manipulation performance. Although tested in structured environments, the approach has the potential to be extended to dynamic or unstructured settings. This research contributes to the integration of viewpoint optimization in vision-based robotic cooperation, with promising applications in industrial automation, service robotics, and human–robot collaboration. Full article

Manipulators performing pick-and-place tasks with objects of complex shapes must consider not only how to grasp the objects but also how to maneuver them out of a bin. In this paper, we explore the industrial challenge of picking hook-shaped components, whose interlocking nature often leads to failed attempts at safely retrieving a single component at a time. Rather than explicitly modeling contact-rich interactions within optimization-based motion planners, we tackle this challenge by leveraging recent advances in sampling-based optimization and parallelizable physics simulators to predict the impact of motion on the separating subgoal, aimed at resolving interlocking. The proposed framework generates candidate trajectories initialized from a user-provided demonstration, which are then simulated and evaluated in a physics simulator to optimize robot trajectories in joint space while considering the entire planning horizon. We validate our approach through real-world experiments on a manipulator, demonstrating improved success rates in terms of separating interlocked objects compared to the industrial baseline. Full article

The legibility of residential space pertains to the clarity and intelligibility of spatial organisation, facilitating intuitive navigation and an immediate grasp of spatial structure. Despite its significance, legibility remains an underexplored factor in residential design, particularly regarding its cognitive impact on users. This study investigates key determinants of legibility, including spatial layout, circulation patterns, lighting, and colour schemes. Through theoretical and empirical approaches, a literature review and case studies identify fundamental principles shaping residential legibility. Unlike previous studies, this research integrates both objective spatial cognition tests and subjective user perceptions, offering a more comprehensive understanding of legibility. The experimental component examines cognitive navigation and subjective perception, assessed via the recognition of a 3D interior from a 2D plan and bipolar rating scales. Participants explored regular and irregular layouts through animated simulations. Findings confirm that experts exhibit greater spatial recognition accuracy and that legibility is enhanced by spatial regularity and distinct colour schemes. However, lighting had no significant impact on subjective assessments, indicating that its role in perceived legibility may be overstated. These findings provide new insights into how spatial legibility affects cognitive processing, distinguishing this study from prior research and advancing practical design strategies for optimising residential environments. Full article

Considerable losses are inflicted by plant-parasitic nematodes (PPNs) due to their obligate parasitism; serious damage occurs in many susceptible crops, and the parasites have a broad distribution worldwide. As most PPNs have a subterranean nature, the complexity of soils in the plant rhizosphere and the structures and functions of the soil food webs necessitate a grasp of the relevant biotic/abiotic factors in order to ensure their effective control. Such factors frequently lead to the inconsistent performance and untapped activity of applied bionematicides, hindering efforts to develop reliable ones. Research efforts that take these factors into account to back the usage of these bionematicides by combining the disease-suppressive activities of two or more agricultural inputs are highlighted herein. These combinations should be designed to boost useful colonization in the rhizosphere, persistent expression of desirable traits under a wide range of soil settings, and/or antagonism to a larger number of plant pests/pathogens relative to individual applications. Relevant ecological/biological bases with specific settings for effective PPN management are exemplified. Determining the relative sensitivity or incompatibility of some biologicals entails studying their combinations and reactions. Such studies, as suggested herein, should be conducted on a case-by-case basis to avoid unsatisfactory outputs. These studies will enable us to accurately define certain outputs, namely, the synergistic, additive, neutral, and antagonistic interactions among the inputs. In optimizing the efficiencies of these inputs, researchers should consider their multi-functionality and metabolic complementarity. Despite previous research, the market currently lacks these types of safe and effective products. Hence, further explorations of novel integrated pest management plans that boost synergy and coverage to control multiple pathogens/pests on a single crop are required. Also, setting economic incentives and utilizing a standardized regulation that examines the authentic risks of biopesticides are still called for in order to ease cost-effective formulation, registration, farmer awareness, and usage worldwide. On the other hand, tank mixing that ensures legality and avoids physical and chemical agro-input-based incompatibilities can also provide superior merits. The end in view is the unraveling of the complexities of interactions engaged with in applying multiple inputs to develop soundly formulated, safe, and effective pesticides. Sophisticated techniques should be incorporated to overcome such complexities/limitations. These techniques would engage microencapsulation, nanopesticides, volatile organic compounds as signals for soil inhabitants, bioinformatics, and RNA-Seq in pesticide development. Full article

The integration of vision–language models (VLMs) with robotic systems represents a transformative advancement in autonomous task planning and execution. However, traditional robotic arms relying on pre-programmed instructions exhibit limited adaptability in dynamic environments and face semantic gaps between perception and execution, hindering their ability to handle complex task demands. This paper introduces GPTArm, an environment-aware robotic arm system driven by GPT-4V, designed to overcome these challenges through hierarchical task decomposition, closed-loop error recovery, and multimodal interaction. The proposed robotic task processing framework (RTPF) integrates real-time visual perception, contextual reasoning, and autonomous strategy planning, enabling robotic arms to interpret natural language commands, decompose user-defined tasks into executable subtasks, and dynamically recover from errors. Experimental evaluations across ten manipulation tasks demonstrate GPTArm’s superior performance, achieving a success rate of up to 91.4% in standardized benchmarks and robust generalization to unseen objects. Leveraging GPT-4V’s reasoning and YOLOv10’s precise small-object localization, the system surpasses existing methods in accuracy and adaptability. Furthermore, GPTArm supports flexible natural language interaction via voice and text, significantly enhancing user experience in human–robot collaboration. Full article

The “Village Merger and Resettlement” policy, as an adjustment of rural living arrangements and spatial organization, addresses the rural population outflow against the backdrop of global urbanization and industrialization. It has profound impacts on agricultural resource allocation, technological innovation, and carbon emissions, playing a significant role in achieving green and low-carbon development alongside high-quality agricultural advancement. This paper conducts an empirical analysis based on panel data from 30 provincial regions in China from 2001 to 2022 (excluding Tibet, Hong Kong, Macau, and Taiwan) to examine the impact of the “Village Merger and Resettlement” policy on agricultural carbon emissions. It explores the mediating effects of agricultural informatization and the integration of agricultural industries and analyzes the moderating roles of government environmental regulations and public environmental participation. The findings indicate that the “Village Merger and Resettlement” policy significantly suppresses agricultural carbon emissions, with the effects being more pronounced in major grain-producing areas, regions with flat terrain, convenient transportation, and higher levels of technology and labor, as well as on the east side of the Hu Huanyong Line, where the degree of agricultural industrial restructuring is lower and government policy enforcement is stronger. The mediation analysis reveals that the processes of agricultural informatization and industry integration both play positive transmission roles in the policy’s impact on reducing agricultural carbon emissions. The moderation analysis shows that compulsory government environmental regulations have a negative moderating effect on the policy’s carbon emission suppression, while public environmental participation has a positive moderating effect. Therefore, in implementing the “Village Merger and Resettlement” policy, it is necessary to tailor strategies to local conditions, make full use of agricultural informatization resources, reasonably plan the integration of agricultural industries, and accurately grasp the roles of environmental regulations to promote the positive effects on green, low-carbon, and high-quality agricultural development. Full article

Grasping the effective carrying capacity of airport hub subway stations in real-time serves as the foundation for enhancing the safety assurance capability of the hub. Starting from the perspectives of multiple subsystems, including people, stations, and trains, and combining passenger flow, system structure, and multiple attributes of trains, a system dynamics (SD) model for passenger travel in airport hub subway stations is established. The model is simulated using Vensim PLE 5.9d to analyze the effective carrying capacity of the transfer system under the existing configuration and layout of transfer facilities and equipment in the hub. The model features a modular architecture and interface, enabling quick and easy model establishment, and adapts to various configurations and operational characteristics of airport hub subway stations in a user-friendly manner. Multiple sensitivity simulation analysis experiments are designed to analyze changes in passenger flow density from multiple perspectives. This method can calculate the effective carrying capacity of airport hub subway stations, providing a scientific basis for planning, construction, and operational management. The effectiveness of the model is verified by analyzing the Pudong International Airport terminal subway station. Full article

Automated depalletizing systems aim to offer continuous and efficient operation in warehouse logistics, reducing cycle times and contributing to worker safety. However, most commercially available depalletizing solutions are designed primarily for highly homogeneous cargo arranged in orthogonal configurations. This paper presents a real-time approach for depalletizing heterogeneous pallets with boxes of varying sizes and arbitrary orientations, including configurations where the topmost surfaces of boxes are not necessarily parallel to each other. To accomplish this, we propose an algorithm that leverages deep learning-based machine vision to determine the size, position, and orientation of boxes relative to the horizontal plane of a robot arm from sparse depth data. Using this information, we implement a path planning method that generates collision-free trajectories to enable precise box grasping and placement onto a production line. Validation through both simulated and real-world experiments demonstrates the feasibility and accuracy of this approach in complex industrial settings, highlighting potential improvements in the efficiency and adaptability of automated depalletizing systems. Full article