Search Results (470)

Wind turbine blades feature complex geometries and operate under harsh conditions, including high curvature gradients, nonlinear deformations, elevated humidity, and particulate contamination. This study presents the design and kinematic analysis of a novel climbing robot based on a 10R folding metamorphic mechanism. The robot employs a hybrid wheel-leg drive and adaptively reconfigures between rectangular and hexagonal topologies to ensure precise adhesion and efficient locomotion along blade leading edges and windward surfaces. A high-order kinematic model, derived from a modified Grubler–Kutzbach criterion augmented by rotor theory, captures the mechanism’s intricate motion characteristics. We analyze the degrees of freedom (DOF) and motion branch transitions for three representative singular configurations, elucidating their evolution and constraint conditions. A scaled-down prototype, integrating servo actuators, vacuum adhesion, and multi-modal sensing on an MDOF control platform, was fabricated and tested. Experimental results demonstrate a configuration switching time of 6.3 s, a single joint response time of 0.4 s, and a maximum crawling speed of 125 mm/s, thereby validating stable adhesion and surface tracking performance. This work provides both theoretical insights and practical validation for the intelligent maintenance of wind turbine blades. Full article

Soft robot motion performance has long been a core focus in scientific research. This study investigates the motion capabilities of soft robots constructed using poly(N-isopropylacrylamide) (PNIPAM) hydrogels, with key innovations in material design and functional enhancement. By optimizing the hydrogel formulation and incorporating molybdenum disulfide (MoS₂) to endow it with photothermal response properties, the material achieves muscle-like controllable contraction and expansion deformation—a critical breakthrough in mimicking biological motion mechanics. Building on this material advancement, the research team developed a series of soft robotic prototypes to systematically explore the hydrogel’s motion characteristics. A flytrap-inspired soft robot demonstrates rapid opening–closing movements, replicating the swift responsiveness of natural carnivorous plants. For terrestrial locomotion, a hexapod crawling robot utilizes the photo-induced stretch-recovery mechanism of both horizontally configured and pre-bent feet to achieve stable directional propulsion. Most notably, a magnetically driven rolling robot integrates magnetic units to realize versatile multimodal movement: it achieves a stable rolling speed of 1.8 cm/s across flat surfaces and can surmount obstacles up to 1.5 times its own body size. This work not only validates the strong potential of PNIPAM hydrogel-based soft robots in executing complex motion tasks but also provides valuable new insights for the development of multimodal soft robotic systems, paving the way for future innovations in adaptive and bio-inspired robotics. Full article

A pervasive cognitive asymmetry exists between designers and users, and contemporary furniture form design often struggles to accommodate and balance multi-dimensional user affective requirements. To address these challenges, this study proposes an intelligent optimization design method for furniture form that enhances the universality of user research and the balance of design decision-making. First, representative URs are extracted from online user review texts collected through web crawling. These URs are then classified into three-dimensional quality attributes using the refined Kano’s model, thereby identifying the key URs. Second, a decomposition table of furniture design characteristics (DCs) is constructed. Third, the multi-objective red-billed blue magpie optimizer (MORBMO) is employed to automatically generate a Pareto solution set that satisfies the multi-dimensional key URs, from which the final optimal solution is determined. The proposed method improves the objectivity and granularity of user research, assists furniture enterprises in prioritizing product development, and enhances user satisfaction across multiple affective dimensions. Furthermore, it provides enterprises with flexible choices among diverse alternatives, thereby mitigating the asymmetry inherent in furniture form design. Full article

Detecting internal defects in narrow and curved pipelines remains a significant challenge, due to the difficulty of achieving reliable defect perception under low-light conditions and generating collision-free motion trajectories. To address these challenges, this article proposes an event-aware ES-YOLO framework, and develops a pipeline defect inspection experimental environment that utilizes a hyper-redundant manipulator (HRM) to insert an event camera into the pipeline in a collision-free manner for defect inspection. First, to address the lack of datasets for event-based pipeline inspection, the ES-YOLO framework is proposed. This framework converts RGB data into an event dataset, N-neudet, which is subsequently used to train and evaluate the detection model. Concurrently, comparative experiments are conducted on steel and acrylic pipelines under three different illumination conditions. The experimental results demonstrate that, under low-light conditions, the event-based detection model significantly outperforms the RGB detection model in defect recognition rates for both types of pipelines. Second, a pipeline defect detection physical system is developed, integrating a visual perception module based on the ES-YOLO framework and a control module for the snake-like HRM. The system controls the HRM using a combination of Nonlinear Model Predictive Control (NMPC) and the Serpentine Crawling Algorithm (SCA), enabling the event camera to perform collision-free inspection within the pipeline. Finally, extensive pipeline insertion experiments are conducted to validate the feasibility and effectiveness of the proposed framework. The results demonstrate that the framework can effectively identify steel pipeline defects in a 2 Lux low-light environment, achieving a detection accuracy of 84%. Full article

Efficient and explainable vehicle damage inspection is essential due to the increasing complexity and volume of vehicular incidents. Traditional manual inspection approaches are not time-effective, prone to human error, and lead to inefficiencies in insurance claims and repair workflows. Existing deep learning methods, such as CNNs, often struggle with generalization, require large annotated datasets, and lack interpretability. This study presents a robust and interpretable deep learning framework for vehicle damage classification, integrating Vision Transformers (ViTs) and ensemble detection strategies. The proposed architecture employs a RetinaNet backbone with a ViT-enhanced detection head, implemented in PyTorch using the Detectron2 object detection technique. It is pretrained on COCO weights and fine-tuned through focal loss and aggressive augmentation techniques to improve generalization under real-world damage variability. The proposed system applies the Weighted Box Fusion (WBF) ensemble strategy to refine detection outputs from multiple models, offering improved spatial precision. To ensure interpretability and transparency, we adopt numerous explainability techniques—Grad-CAM, Grad-CAM++, and SHAP—offering semantic and visual insights into model decisions. A custom vehicle damage dataset with 4500 images has been built, consisting of approximately 60% curated images collected through targeted web scraping and crawling covering various damage types (such as bumper dents, panel scratches, and frontal impacts), along with 40% COCO dataset images to support model generalization. Comparative evaluations show that Hybrid ViT-RetinaNet achieves superior performance with an F1-score of 84.6%, mAP of 87.2%, and 22 FPS inference speed. In an ablation analysis, WBF, augmentation, transfer learning, and focal loss significantly improve performance, with focal loss increasing F1 by 6.3% for underrepresented classes and COCO pretraining boosting mAP by 8.7%. Additional architectural comparisons demonstrate that our full hybrid configuration not only maintains competitive accuracy but also achieves up to 150 FPS, making it well suited for real-time use cases. Robustness tests under challenging conditions, including real-world visual disturbances (smoke, fire, motion blur, varying lighting, and occlusions) and artificial noise (Gaussian; salt-and-pepper), confirm the model’s generalization ability. This work contributes a scalable, explainable, and high-performance solution for real-world vehicle damage diagnostics. Full article

Existing research predominantly focuses on the preservation or renewal models of the physical forms of historic cultural districts, with limited exploration of their roles in stimulating tourists’ cognitive, affective resonance, and behavioral interactions. This study addresses historic cultural districts by evaluating the space quality and its impact on tourist experiences through the “cognition-affect-behavior” framework, integrating GIS, street view semantic segmentation, VR eye-tracking, and web crawling technologies. The findings reveal significant multidimensional differences in how space quality influences tourist experiences: the impact intensities of functional diversity, sky visibility, road network accessibility, green visibility, interface openness, and public facility convenience decrease sequentially, with path coefficients of 0.261, 0.206, 0.205, 0.204, 0.201, and 0.155, respectively. Additionally, space quality exerts an indirect effect on tourist experiences through the mediating roles of cognitive, affective, and behavioral dimensions, with a path coefficient of 0.143. This research provides theoretical support and practical insights for empowering cultural heritage space governance with digital technologies in the context of cultural and tourism integration. Full article

Falkland Islands English (FIE) began its development in the first half of the 19th century. In part, as a consequence of its youth, FIE is an understudied variety. It shares some morphosyntactic features with other anglophone countries in the Southern Hemisphere, but it also shares lexical features with regional varieties of Spanish, including Rioplatense Spanish. Che is one of many South American words that have entered FIE through Spanish, with its spelling ranging from “chay” and “chey” to “ché”. The word has received some marginal attention in terms of its meaning. It is said to be used in a similar way to the British dear or love and the Australian mate, and it has been compared to chum or pal, and is taken as an equivalent of the River Plate, hey!, hi!, or I say!. In this work, we explore the hypothesis that che entered FIE through historical contact with Rioplatense Spanish, drawing on both linguistic and sociohistorical evidence, and presenting survey, corpus, and ethnographic data that illustrate its current vitality, usage, and social meanings among FIE speakers. In situ observations, fieldwork, and an online survey were used to look into the vitality of che. Concomitantly, by crawling social media and the local press, enough data was gathered to build a small corpus to further study its vitality. A thorough literature review was conducted to hypothesise about the borrowing process involving its entry into FIE. The findings confirm that the word is primarily a vocative, it is commonly used, and it is indicative of a sense of belonging to the Falklands community. Although there is no consensus on the origin of che in the River Plate region, it seems to be the case that it entered FIE during the intense Spanish–English contact that took place during the second half of the 19th century. Full article

This work presents a novel type of soft reconfigurable mobile robot with multimodal locomotion, which is created using a controllable magneto-elastica-reinforced composite elastomer. The rope motor-driven method is employed to modulate magnetics–mechanics coupling effects and enable the magneto-elastica-reinforced elastomer actuator to produce controllable deformations. Furthermore, the 3D-printed magneto-elastica-reinforced elastomer actuators are assembled into several typical robotic patterns: linear configuration, parallel configuration, and triangular configuration. As a proof of concept, a few of the basic locomotive modes are demonstrated including squirming-type crawling at a speed of 1.11 $\mathrm{m}\mathrm{m}/\mathrm{s}$, crawling with turning functions at a speed of 1.11 $\mathrm{m}\mathrm{m}/\mathrm{s}$, and omnidirectional crawling at a speed of 1.25 $\mathrm{m}\mathrm{m}/\mathrm{s}$. Notably, the embedded magnetic balls produce magnetic adhesion on the ferromagnetic surfaces, which enables the soft mobile robot to climb upside-down on ferromagnetic curved surfaces. In the experiment, the inverted ceiling-based inverted crawling speed is 2.17 $\mathrm{m}\mathrm{m}/\mathrm{s}$, and the inverted freeform surface-based inverted crawling speed is 3.40 $\mathrm{m}\mathrm{m}/\mathrm{s}$. As indicated by the experimental results, the proposed robot has the advantages of a simple structure, low cost, reconfigurable multimodal motion ability, and so on, and has potential application in the inspection of high-value assets and operations in confined environments. Full article

In the freshwater snail L. stagnalis, two hours of shallow water crawling exercise are accompanied by the formation of memory, metabolic, neuronal, and behavioral changes, such as faster orientation in a novel environment. Interestingly, rest following exercise enhances serotonin and dopamine metabolism linked to the formation of memory and adaptation to novel conditions. However, the underlying transcriptional responses are not characterized. In this paper, we show that, while two hours of forced crawling exercise in L. stagnalis produce significant changes in nervous system gene expression, the subsequent rest induces a completely distinct transcriptional program. Chromatin-modifying, vesicle transport, and cell cycle genes were induced, whereas neurodevelopmental, behavioral, synaptic, and hormone response genes were preferentially repressed immediately after two hours of exercise. These changes were normalized after two hours of the subsequent rest. In turn, rest induced the expression of genes functioning in neuron differentiation and synapse structure/activity, while mitotic, translational, and protein degradation genes were repressed. Our findings are likely relevant to the physiology of exercise, rest, and learning in other species. For example, chronic voluntary exercise training in mice affects the expression of many homologous genes in the hippocampus. Moreover, in humans, homologous genes are pivotal for normal development and complex neurological functions, and their mutations are associated with behavioral, learning, and neurodevelopmental abnormalities. Full article

Objectives: The aim of this study was to indicate which variables are the most important determinants of swimming results in the 50 m front crawl among non-elite pre-pubertal female swimmers. Methods: The study group consisted of 14 female swimmers (at the time of the research commencement—biological age: 10.52 ± 0.37 years; body mass: 34.99 ± 2.77 kg; height: 146.00 ± 3.05 cm). The study was conducted over three years. The swimmers performed capacity training recommended by the British Swimming Federation. Every 6 months, in the participants the following parameters were measured: percentage of body fat; anthropometric measurements; aerobic and anaerobic capacity; and respiratory parameters: vital capacity—VC, forced expiratory volume—FEV1, and forced vital capacity—FVC. Additionally, a 50 m front crawl swim test was performed. Results: After adjusting for multicollinearity, the most influential determinants of swimming performance were anthropometric measures: shoulder width was the most influential predictor, with a regression coefficient of −0.66, followed by foot length (with a beta of −0.15) and chest depth (with a beta of 0.008). The remaining anthropometric and physical predictors did not contribute to the prediction of 50 m freestyle performance. Conclusions: These research results suggest to coaches and trainers that sports performance in sprint distances in pre-pubertal girls is not determined by aerobic and anaerobic capacity or body fat but is based on the somatic build of the swimmer. Full article

Kirigami-inspired geometries offer a lightweight, bioinspired strategy for friction enhancement and anchoring in soft robotics. This study presents a bioinspired kirigami structure designed to enhance the anchoring performance of soft robotic systems through systematic geometric and actuation parameter optimization. Drawing inspiration from the anisotropic friction mechanisms observed in reptilian scales, we integrated linear, triangular, trapezoidal, and hybrid kirigami cuts onto flexible plastic sheets. A compact 12 V linear actuator enabled cyclic actuation via a custom firmware loop, generating controlled buckling and directional friction for effective locomotion. Through experimental trials, we quantified anchoring efficiency using crawling distance and stride metrics across multiple cut densities and actuation conditions. Among the tested configurations, the triangular kirigami with a 4 × 20 unit density on 100 µm PET exhibited the most effective performance, achieving a stride efficiency of approximately 63% and an average crawling speed of ~47 cm/min under optimized autonomous operation. A theoretical framework combining buckling mechanics and directional friction validated the observed trends. This study establishes a compact, tunable anchoring mechanism for soft robotics, offering strong potential for autonomous exploration in constrained environments. Full article

The summarized content of one or more extensive text documents helps users extract only the most important key information, instead of reviewing and reading hundreds of pages of text. This study uses extractive and abstractive mechanisms to automatically extract and summarize information retrieved from various web documents on the same topic. The research aims to develop a methodology for designing and developing an information system for pre- and post-processing natural language obtained through web content search and web scraping, and for the automatic generation of a summary of the retrieved text. The research outlines two subtasks. As a first step, the system is designed to collect and process up-to-date information based on specific criteria from diverse web resources related to project funding, initiated by various organizations such as startups, sustainable companies, municipalities, government bodies, schools, the NGO sector, and others. As a second step, the collected extensive textual information about current projects and programs, which is typically intended for financial professionals, is to be summarized into a shorter version and transformed into a suitable format for a wide range of non-specialist users. The automated AI software tool, which will be developed using the proposed methodology, will be able to crawl and read project funding information from various web documents, select, process, and prepare a shortened version containing only the most important key information for its clients. Full article

Nowadays, a pedestrian navigation system using a smartphone has become popular as a useful tool to reach an unknown destination. When the destination is the office of a person, a detailed map information is necessary on the target area such as the room number and location inside the building. The information can be collected from various sources including Google maps, websites for the building, and images of signs. In this paper, we propose a map information collection tool for a pedestrian navigation system. To improve the accuracy and completeness of information, it works with the four steps: (1) a user captures building and room images manually, (2) an OCR software using Google ML Kit v2 processes them to extract the sign information from images, (3) web scraping using Scrapy (v2.11.0) and crawling with Apache Nutch (v1.19) software collects additional details such as room numbers, facilities, and occupants from relevant websites, and (4) the collected data is stored in the database to be integrated with a pedestrian navigation system. For evaluations of the proposed tool, the map information was collected for 10 buildings at Okayama University, Japan, a representative environment combining complex indoor layouts (e.g., interconnected corridors, multi-floor facilities) and high pedestrian traffic, which are critical for testing real-world navigation challenges. The collected data is assessed in completeness and effectiveness. A university campus was selected as it presents a complex indoor and outdoor environment that can be ideal for testing pedestrian navigations in real-world scenarios. With the obtained map information, 10 users used the navigation system to successfully reach destinations. The System Usability Scale (SUS) results through a questionnaire confirms the high usability. Full article

This paper presents the design and development of a new modular wall-climbing robot—Modular Wall Climbing-1 (MC-1)—for solving the problem of autonomous wall switching observed in wall-climbing robots. Each modular robot is capable of independently adhering to vertical surfaces and maneuvering, making it a fully autonomous robotic system. Multiple modules of MC-1 are connected by an electromagnet-based magnetic attachment method, and wall transitions are achieved using a servo motor mechanism. Moreover, an ultrasonic sensor is employed to measure the unknown wall-inclination angle. Mechanical analysis is conducted for MC-1 at rest individually and in combination to determine the required suction force. Experimental investigations are performed to assess the robot’s crawling ability, loading capacity, and wall-transition performance. The results demonstrate that the MC-1 robot is capable of multi-angle wall transitions for executing multiple tasks. It provides a new approach for wall-climbing robots to collaborate during wall transitions through a quick attachment-and-disassembly device and an efficient wall detection method. Full article

Pneumatic artificial muscles (PAMs) consist of an elastomeric bladder wrapped in a Kevlar braid. When inflated, PAMs expand radially and contract axially, producing large axial forces. PAMs are often utilized for their high specific work and specific power, as well as their ability to produce large axial displacements. Although the axial behavior of PAMs is well studied, the radial behavior has remained underutilized and is poorly understood. Modeling was performed using a force balance approach to capture the effects that bladder strain and applied axial load have on the anchoring force. Radial expansion testing was performed to validate the model. Force due to anchoring was recorded using force transducers attached to sections of aluminum pipe using an MTS servo-hydraulic testing machine. Data from the test were compared to the predicted anchoring force. Radial expansion in large-diameter (over 50.8 mm) PAMs was then used in worm-like robots to create anchoring forces that allow for a peristaltic wave, which creates locomotion through acrylic pipes. By radially expanding, the PAM presses itself into the pipe, creating an anchor point. The previously anchored PAM then deflates, which propels the robot forward. Modeling of the radial expansion forces and anchoring was necessary to determine the pressurization required for proper anchoring before slipping occurs due to the combined robot and payload weight. Full article