Search Results (258)

Modal tracking has evolved into a crucial research domain over the past decades. It synergizes with automatic modal parameter estimation (AMPE) to achieve automatic operational modal analysis (AOMA). Modal tracking aims to establish the dynamic correspondence between the currently identified mode and historical reference modes, thereby following the evolution of modal parameters over time. Established modal tracking methods involve two stages: reference mode determination and current mode linking. This paper reviews the developments of modal tracking from both methodological and application perspectives, emphasizing the pivotal challenges in existing results and suggestions for future research. Additionally, a detailed summary of the distinct strategies employed across two stages and the application of modal tracking in various types of structures is provided. Future research should focus on the adaptive updating of reference modes and linking thresholds while enhancing the capability to eliminate spurious modes and track rapidly changing physical modes. This review seeks to assist researchers in efficiently grasping the advancements in modal tracking and in developing more suitable and robust methods for civil engineering structures in the future. 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

Real-time computational capability for simultaneous grasping force and displacement determination constitutes a critical enabler for stable and reliable grasping performance in dexterous robotic grasping. To accelerate the computational efficiency of LCP in the dexterous grasping problem, as well as to ensure the stability and reproducibility of the algorithm’s output, the NSNMGK method, which incorporates sequential projection iterations across all greedy-selected active constraint rows within each NSNGRK framework iteration cycle, is developed. In each NSNMGK iteration, sequential projection operations are systematically applied to all active constraint rows, satisfying the greedy criterion. This processing strategy ensures the full utilization of qualifying constraints within the greedy subset through a same generalized Jacobian evaluation per iteration cycle. The methodology effectively mitigates inherent limitations of conventional randomized row selection, including unpredictable iteration counts and computational overhead from repeated Jacobian updates, while maintaining deterministic convergence behavior. The method’s convergence theory is rigorously established, with benchmark analyses demonstrating marked improvements in computational efficiency over the NSNGRK framework. Experimental validation in dexterous robotic grasping scenarios further confirms enhanced convergence rates through reduced iteration counts and shortened computational durations relative to existing approaches. Full article

TiAl intermetallic alloy is a crucial high-performance material, and its microstructure evolution at high temperatures is closely related to the process parameters. Observing the lamellar structure is key to exploring growth kinetics, and the feature extraction of precipitate phases can provide an effective basis for subsequent evolution studies and process parameter settings. Traditional observation methods struggle to promptly grasp the growth state of lamellar structures, and conventional object detection has certain limitations for clustered lamellar structures. This paper introduces a novel method for high-temperature precipitate phase feature extraction based on the YOLOv5-obb rotational object detection network, and a corresponding precipitate phase dataset was created. The improved YOLOv5-obb network was compared with other detection networks. The results show that the proposed YOLOv5-obb network model achieved a precision rate of 93.6% on the validation set for detecting and identifying lamellar structures, with a detection time of 0.02 s per image. It can effectively and accurately identify γ lamellar structures, providing a reference for intelligent morphology detection of alloy precipitate phases under high-temperature conditions. This method achieved good detection performance and high robustness. Additionally, the network can obtain precise positional information for target structures, thus determining the true length of the lamellar structure, which provides strong support for subsequent growth rate calculations. Full article

The origin and evolution of genes are central themes in evolutionary biology and genomics, shedding light on how molecular innovations shape biological complexity and adaptation. This review explores the principal mechanisms underlying gene emergence in eukaryotes, including gene duplication, de novo gene birth, horizontal gene transfer, viral gene domestication, and exon shuffling. We examine the population dynamics that govern the fixation of new genes, their functional integration, and the selective forces acting upon them—from purifying selection to adaptive innovation. Examples such as NOTCH2NL and SRGAP2C, which originated through recent segmental duplications followed by neofunctionalization, illustrate how duplicate-derived de novo genes can play a key role in human brain development. In addition, we highlight the emerging relevance of nuclear architecture in determining the evolutionary fate of new genes, offering a spatial dimension to gene innovation. We also discuss methodological approaches for detecting new genes and inferring selection, and finally, we highlight the emerging role of the human pangenome in revealing hidden gene diversity and its implications for evolutionary and biomedical research. Understanding gene innovation not only enhances our grasp of evolutionary processes but also informs clinical studies on disease susceptibility and human uniqueness. Full article

Pneumatic soft manipulators are one of the current development trends in the field of manipulators. The soft manipulator that has been developed at present still has problems with single function and poor load-bearing capacity. This paper designs a composite soft finger inspired by the human middle finger, featuring a dual-chamber pneumatic drive and embedded steel sheet structure. Utilizing the principles of moment equilibrium and virtual work, a theoretical model for the bending behavior of the soft finger is developed, and the correlation between the bending angle and driving air pressure is derived. The determination process of key parameters and their influence on bending deformation are explained in detail through simulation. The bending experiment confirmed the reliability of the theoretical model. The fingertip force test indicates that the composite finger exerts a greater force than the ordinary one, with the extra force equivalent to 42.57% of the composite finger’s own fingertip force. Subsequent tests on the soft robotic hand measured the hooking quality, gripping diameter, and gripping force. The hooking experiment confirmed that composite fingers have a stronger load-bearing capacity than ordinary fingers, with an extra capacity equivalent to 31.25% of the composite finger’s own load-bearing capacity. Finally, the grasping experiment demonstrates that the soft manipulator can grasp objects of varying shapes and weights, indicating its strong adaptability and promising applications. 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

This study aims to review research on voluntary IFRS application and future research directions. This study presents a bibliometric review of 185 studies on voluntary IFRS application using Web of Science data and R software. Four research clusters are identified: (i) determinants of IFRS voluntary application, (ii) corporate disclosure, (iii) voluntary disclosure, and (iv) economic consequences, with detailed analysis and future research question extraction. These research clusters enable us to extract future research questions, highlighting the avenues where further investigation is needed. This study provides practical insights for policymakers and practitioners. Researchers can leverage our work to grasp the current state of knowledge, identifying active and impactful research areas. This guidance can inform their investigations, though this study relies solely on data from the Web of Science database. This study provides a comprehensive overview of the existing literature on voluntary IFRS application, helping researchers, practitioners, and policymakers understand the current state of research in this field. Full article

This paper proposes a grasping method for prosthetic hands based on object geometry and symmetry axis. The method utilizes computer vision to extract the geometric shape, spatial position, and symmetry axis of target objects and selects appropriate grasping modes and postures through the extracted features. First, grasping patterns are classified based on the analysis of hand-grasping movements. A mapping relationship between object geometry and grasp patterns is established. Then, target object images are captured using binocular depth cameras, and the YOLO algorithm is employed for object detection. The SIFT algorithm is applied to extract the object’s symmetry axis, thereby determining the optimal grasp point and initial hand posture. An experimental platform is built based on a seven-degree-of-freedom (7-DoF) robotic arm and a multi-mode prosthetic hand to conduct grasping experiments on objects with different characteristics. Experimental results demonstrate that the proposed method achieves high accuracy and real-time performance in recognizing object geometric features. The system can automatically match appropriate grasp modes according to object features, improving grasp stability and success rate. 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

The disadvantages of traditional bolt support technology relying too much on engineering experience in slope engineering in China are becoming more and more obvious. Aiming at this problem, this paper establishes an intelligent bolt pull-out test system based on the Internet of Things, monitors the whole process of a bolt pull-out test, determines the ultimate pull-out bearing capacity, and grasps the friction of a bolt in real time. Based on the local common deformation theory, the force of the bolt is analyzed theoretically. The results show that the stress process of bolt rod end tension–rod end displacement is divided into quasi-elastic stage, strengthening stage and failure stage. The stress history of bolts with different anchorage lengths is the same, but the curve shape changes from steep to slow with the increase in anchorage length. Increasing the length of the long bolt can increase the ultimate pull-out bearing capacity of the bolt. Full article

Pneumatic force control has a broad application background in the automation field, such as in industrial polishing, robotic grasping, and humanoid robots. Nonlinear hysteresis characteristics are one of the major factors that affect the feedforward force control performance of a pneumatic system. The primary motivation of this paper is to develop an accurate feedforward actuating force control method for a single-acting pneumatic cylinder with a nonlinear hysteresis characteristic. A data-driven neural network modeling method is presented to achieve accurate actuating force modeling. The modeling accuracy of the neural network model under different configurations of the input layer is quantitatively analyzed to determine the essential modeling variables. The real-time execution speed of neural network models with different numbers of hidden neurons is evaluated to achieve a balance between the modeling accuracy and the real-time computing speed of the neural network model. Then, a single-acting pneumatic system is fabricated to experimentally verify the effectiveness of the proposed modeling and control method. The experimental results reveal that the actuating force can achieve ideal tracking of the target. In both the loading and the unloading process, the amplitude of the control error is less than 0.5 N. The overall RMS value of the control error is about 1 N. An instruction smoothing operation could reduce the percentage overshoot and steady-state error of the feedforward step actuating force control. 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

Flexible grippers based on magnetically sensitive rubber have garnered significant research attention due to their high gripping adaptability and ease of control. However, current research designs often separate the excitation device from the flexible finger, which can lead to potential interference or damage to other electronic components in the working environment and an inability to simultaneously ensure safety and gripping performance. In this paper, we propose an integrated magnetically controlled bionic flexible gripper that combines the excitation device and the flexible finger. We derive a formula for calculating the magnetic field generated by the excitation device, model and simulate the device, and find that the optimal magnetic field effect is achieved when the core-to-coil size ratio is 1:5. Additionally, we fabricated flexible fingers with different NdFeB volume ratios and experimentally determined that a volume ratio of 20% yields relatively better bending performance. The integrated magnetically controlled bionic flexible gripper described in this paper can adaptively grasp items such as rubber, column foam, and electrical tape, achieving maximum grasping energy efficiency of 0.524 g per millitesla (g/mT). These results highlight its potential advantages in applications such as robotic end-effectors and industrial automatic sorting. Full article

Nowadays, portrait drawing has become increasingly popular as a means of developing artistic skills and nurturing emotional expression. However, it is challenging for novices to start learning it, as they usually lack a solid grasp of proportions and structural foundations of the five senses. To address this problem, we have studied Portrait Drawing Learning Assistant System (PDLAS) for guiding novices by providing auxiliary lines of facial features, generated by utilizing OpenPose and OpenCV libraries. For PDLAS, we have also presented the exactness assessment method to evaluate drawing accuracy using the Normalized Cross-Correlation (NCC) algorithm. It calculates the similarity score between the drawing result and the initial portrait photo. Unfortunately, the current method does not assess the hair drawing, although it occupies a large part of a portrait and often determines its quality. In this paper, we present a hair drawing evaluation algorithm for the exactness assessment method to offer comprehensive feedback to users in PDLAS. To emphasize hair lines, this algorithm extracts the texture of the hair region by computing the eigenvalues and eigenvectors of the hair image. For evaluations, we applied the proposal to drawing results by seven students from Okayama University, Japan and confirmed the validity. In addition, we observed the NCC score improvement in PDLAS by modifying the face parts with low similarity scores from the exactness assessment method. Full article