Search Results (64)

In frame structures, connections play a vital role in governing both serviceability and ultimate strength. For pultruded fiber-reinforced polymer (PFRP) frames, connection design is even more critical due to the anisotropic and viscoelastic nature of the composite materials used in the primary elements (e.g., beams and columns) and their joints. This study presents a finite element model (FEM) to evaluate the influence of several connection parameters—namely, connection stiffening, bolt diameter, washer diameter, and clamping force—on the elastic behavior of beam-column joints composed of PFRP elements. The results demonstrate that stiffening the upper and lower connection angles significantly enhances joint performance. Increasing the bolt diameter improves moment capacity, reduces rotational deformation, decreases stress concentrations around bolt-hole edges, and increases both minor principal and compressive stresses beneath the bolt shank. Similarly, a larger washer diameter contributes to higher connection stiffness and reduces stress concentrations at bolt holes. Although the clamping force has a relatively modest effect on global connection behavior, it positively influences the through-thickness stress distribution in the angle beneath the bolt shank. Finally, regression equations were developed to quantify the relationship between rotation, moment, bolt diameter, washer diameter, and clamping force, providing a valuable tool for the design and optimization of PFRP connections in structural applications. Full article

Structure-from-motion (SfM) is a foundational technology that facilitates 3D scene understanding and visual localization. However, bundle adjustment (BA)-based SfM is usually very time-consuming, especially when dealing with numerous unknown focal length cameras. To address these limitations, we proposed a novel SfM system based on pose-only adjustment (PA) for intrinsic and extrinsic joint optimization to accelerate computing. Firstly, we propose a base frame selection method based on depth uncertainty, which integrates the focal length and parallax angle under a multi-camera system to provide more stable depth estimation for subsequent optimization. We explicitly derive a global PA of joint intrinsic and extrinsic parameters to reduce the high dimensionality of the parameter space and deal with cameras with unknown focal lengths, improving the efficiency of optimization. Finally, a novel pose-only re-triangulation (PORT) mechanism is proposed for enhanced reconstruction completeness by recovering failed triangulations from incomplete point tracks. The proposed framework has been demonstrated to be both faster and comparable in accuracy to state-of-the-art SfM systems, as evidenced by public benchmarking and analysis of the visitor photo dataset. Full article

Background/Objectives: Prolonged sitting is linked to musculoskeletal discomfort, yet optimal sitting posture remains poorly defined, and the consistency with which individuals reproduce specific sitting strategies is unclear. This study examined postural variability across three sitting strategies—comfortable, habitual, and correct—using three common chair types: a stool, computer chair, and ergonomic chair. Methods: Thirty healthy young adults (fifteen men, fifteen women) participated. Global sagittal joint angles—head inclination (HI), trunk angle (TA), and knee angle (KA)—were measured using a motion analysis system across five repetitions per condition. Results: The chair type significantly influenced HI and TA (p < 0.001), with ergonomic chairs encouraging more upright trunk postures. The sitting strategy significantly affected TA and KA (p < 0.01), with comfortable sitting associated with more extended angles. Women exhibited greater TA (114.8° vs. 109.0°, p < 0.001) and lower within-subject variability. Substantial postural variability was observed across all conditions, with mean ranges exceeding minimum detectable change thresholds for HI (10.3°), TA (6.9°), and KA (11.3°). Notably, correct sitting reduced KA variability compared to other strategies (p < 0.01). Conclusions: These findings highlight the individualized and variable nature of sitting posture, even under controlled instructions. The results question the reliability of memory-based seat adjustments and emphasize the need for dynamic, user-centered ergonomic design and personalized clinical guidance to support musculoskeletal health. Full article

Global navigation satellite system (GNSS) has been widely used in many fields due to their low cost and high positioning accuracy. Because of the open frequency of navigation signals, the low power of navigation signals, and the growing reliance of many modern wireless systems on satellite-based navigation, GNSS performance may be easily affected by interference signals. Monitoring and troubleshooting of interference sources are important means to guarantee the normal use of satellite navigation applications and are an important part of GNSS operation in complex electromagnetic environments; however, traditional angle of arrival (AOA) algorithms cannot efficiently operate with coherent signals, so a decoherence-based orientation scheme is proposed to optimize the AOA algorithm. Furthermore, a joint AOA and time difference of arrival (TDOA) interference localization algorithm is proposed for problems such as the lack of accuracy in a single interference source localization algorithm. Numerical simulation results show that decoherence-based AOA localization can be well applied to various interference signals, and the accuracy of the joint AOA and TDOA interference localization algorithm is higher than that of single-method interference localization. In addition, the physical verification further verifies the usability and reliability of the GNSS interference source positioning algorithm proposed in this paper. Full article

Autism Spectrum Disorder (ASD) poses significant challenges in diagnosis due to its diverse symptomatology and the complexity of early detection. Atypical gait and gesture patterns, prominent behavioural markers of ASD, hold immense potential for facilitating early intervention and optimising treatment outcomes. These patterns can be efficiently and non-intrusively captured using modern computational techniques, making them valuable for ASD recognition. Various types of research have been conducted to detect ASD through deep learning, including facial feature analysis, eye gaze analysis, and movement and gesture analysis. In this study, we optimise a dual-stream architecture that combines image classification and skeleton recognition models to analyse video data for body motion analysis. The first stream processes Skepxels—spatial representations derived from skeleton data—using ConvNeXt-Base, a robust image recognition model that efficiently captures aggregated spatial embeddings. The second stream encodes angular features, embedding relative joint angles into the skeleton sequence and extracting spatiotemporal dynamics using Multi-Scale Graph 3D Convolutional Network(MSG3D), a combination of Graph Convolutional Networks (GCNs) and Temporal Convolutional Networks (TCNs). We replace the ViT model from the original architecture with ConvNeXt-Base to evaluate the efficacy of CNN-based models in capturing gesture-related features for ASD detection. Additionally, we experimented with a Stack Transformer in the second stream instead of MSG3D but found it to result in lower performance accuracy, thus highlighting the importance of GCN-based models for motion analysis. The integration of these two streams ensures comprehensive feature extraction, capturing both global and detailed motion patterns. A pairwise Euclidean distance loss is employed during training to enhance the consistency and robustness of feature representations. The results from our experiments demonstrate that the two-stream approach, combining ConvNeXt-Base and MSG3D, offers a promising method for effective autism detection. This approach not only enhances accuracy but also contributes valuable insights into optimising deep learning models for gesture-based recognition. By integrating image classification and skeleton recognition, we can better capture both global and detailed motion patterns, which are crucial for improving early ASD diagnosis and intervention strategies. Full article

The dynamic response of multi-story steel frames under impact loading exhibits a complex nonlinear behavior. This study develops a three-story, multi-scale spatial steel frame finite element model using ABAQUS 2023 software, and the contact algorithm and material parameters were validated through published drop-weight impact beam tests. A total of 48 impact parameter combinations were defined, covering rational mass–velocity ranges while accounting for column position variations at the first story. Systematic comparisons were conducted on the influence of varying impact parameters on structural dynamic responses. This study investigates deformation damage and progressive collapse mechanisms in spatial steel frames under impact loading. Structural dynamic responses show significant enhancement with increasing impact mass and velocity. As impact kinetic energy increases, the steel frame transitions from localized denting at impact zones to global bending deformation, inducing structural tilting. The steel frame exhibits potential collapse risk under severe impact conditions. Under identical impact energy, corner column impact displacements differ by <1% from edge-middle column displacements, with vertical displacement variations ranging 0–17.6%. The displacement of the first-floor joints of the structure with three spans in the impact direction was reduced by about 50% compared to that with two spans. When designing the structure, it is necessary to increase the number of frame spans in the impact direction to improve the overall stability of the structure. Based on the development of the rotation angle of the beam members during the impact process, the steel frame collapse process was divided into three stages, the elastic stage, the plastic and catenary stage, and the column member failure stage; the steel frame finally collapsed due to an excessive beam rotation angle and column failure. Full article

Currently, deep learning networks based on architectures such as CNN and Transformer have achieved significant advances in remote sensing image change detection, effectively addressing the issue of false changes due to spectral and radiometric discrepancies. However, when handling remote sensing image data from multiple sensors, different viewing angles, and extended periods, these models show limitations in modelling dynamic interactions and feature representations in change regions, restricting their ability to model the integrity and precision of irregular change areas. We propose the Context-Aware Global-Local Subspace Attention Change Detection Network (CGLCS-Net) to resolve these issues and introduce the Global-Local Context-Aware Selector (GLCAS) and the Subspace-based Self-Attention Fusion (SSAF) module. GLCAS dynamically selects receptive fields at different feature extraction stages through a joint pooling attention mechanism and depthwise separable convolution, enhancing global context and local feature extraction capabilities and improving feature representation for multi-scale and irregular change regions. The SSAF module establishes dynamic interactions between dual-temporal features via feature decomposition and self-attention mechanisms, focusing on semantic change areas to address challenges such as sensor viewpoint variations and the texture and spectral inconsistencies caused by long periods. Compared to ChangeFormer, CGLCS-Net achieved improvements in the IoU metric of 0.95%, 9.23%, and 13.16% on the three public datasets, i.e., LEVIR-CD, SYSU-CD, and S2Looking, respectively. Additionally, it reduced model parameters by 70.05%, floating-point operations by 7.5%, and inference time by 11.5%. These improvements enhance its applicability for continuous land use and land cover change monitoring. Full article

Background/Objectives: Several studies have described the changes in tibiofemoral rotation (TFRA) and patellar tilt angle (PTA) following total hip arthroplasty (THA). However, no studies have applied three-dimensional measurements to evaluate leg lengthening, changes in global femoral offset (GFO) or TFRA, or PTA during THA. Accordingly, this study employs three-dimensional measurements to test our hypothesis that increased leg lengthening is associated with an increase in TFRA, which increases PTA and worsens postoperative m-Harris Hip Score (mHHS). Methods: A total of 111 consecutive patients who underwent THA were enrolled. THA-related changes in GFO, femoral version, TFRA, PTA, and leg lengthening. Relationships between each parameter and the m-Harris Hip Sc were also assessed using the intraclass correlation coefficient. Results: Leg lengthening was significantly positively correlated with changes in TFRA and PTA. However, changes in GFO negatively correlated with changes in TFRA and PTA. Moreover, changes in GFO and leg lengthening were the only factors affecting changes in TFRA and PTA, respectively. Conclusions: Direct relationships exist between changes in GFO and changes in TFRA and PTA. This may be related to increased tension of the adductor muscles and medial soft tissue around the knee, ultimately reducing strain on the patellofemoral joint and improving knee pain. Full article

The Modified Mallet Score (MMS) is widely used to assess upper limb function but requires evaluation by experienced clinicians. This study automated MMS assessments using smartphone videos, artificial intelligence (AI), and new algorithms. A total of 125 videos covering all MMS grades were recorded from four neurotypical participants. For all recordings, an expert physician provided manual scores as the ground truth. The OpenPose BODY25 model extracted body keypoint data, which were used to calculate joint angles for an automated scoring algorithm. The algorithm’s scores were compared to the ground truth and expert manual scoring. High accuracy was achieved for the global abduction, hand-to-neck, hand-on-spine, and hand-to-mouth movements, with Pearson correlation coefficients (PCCs) > 0.9 and a low root mean square error (RMSE). Although slightly less accurate for global external rotation, the algorithm still showed strong agreement. This study demonstrates the potential of using AI and smartphone videos for reliable, remote upper limb assessments. Full article

Background/Objectives: Few studies report on hip rotation after total hip arthroplasty (THA); however, details of the factors affecting the hip rotation angle are unknown. We aimed to investigate the factors related to hip rotation after THA. Methods: This study included 124 consecutive patients who underwent THA. We retrospectively analyzed the correlation between changes in the rotation angle of the femur relative to the pelvis, global femoral offset, and femoral version and leg lengthening. Moreover, we performed a multivariate regression analysis of these parameters to calculate the efficacy of the change in the rotation angle of the femur relative to the pelvis. Results: Leg lengthening and femoral version change were negatively correlated, whereas change in global femoral offset was positively correlated with leg lengthening, with correlation coefficients of 0.376, 0354, and 0.334, respectively. Regarding the multiple regression analysis, only leg lengthening was correlated with the change in rotation angle of the femur relative to the pelvis, with a coefficient of −0.336. Conclusions: The change in the rotation angle of the femur relative to the pelvis is only associated with leg lengthening in multivariate analysis. In actual planning, in cases where the hip is internally rotated, it may be better not to excessively increase leg length, decrease anterior stem anteversion, or increase global femoral offset. Thus, physicians should avoid large leg lengthening for patients with highly external rotation in their hip joint as it may lead to increased internal rotation of the hip, consequently resulting in relative malpositioning and subsequent implant impingement and/or dislocation following THA. Full article

Facing the global energy crisis and increasingly stringent environmental protection regulations, automotive lightweighting has become a core issue for the sustainable development of the automotive industry. In particular, the qualified combination of steel and aluminum alloy has become a promising development direction to achieve the aim of lightweight design. As an innovative solid-phase welding technique, magnetic pulse welding (MPW) exhibits unique advantages in joining these dissimilar metals. The 6061 Al alloy and 20# steel tubes were joined by the MPW technique in this study. The microstructure and interface morphology of the MPW steel/Al tube were characterized using optical microscopy (OM), scanning electron microscopy (SEM), and an electro-probe microanalyzer (EPMA). The microstructure in the region adjacent to the interface was similar to that of the base metals (BMs). The element transition zone could be observed at the interface. The thickness of the transition layer was approximately 6 μm. The transition layer did not possess high hardness and brittleness like the Fe–Al binary IMC layer. Therefore, the interface bonding quality and long-term stability of the MPW steel/Al joint were relatively good. The welded joint interface could be divided into three zones: the bonded zone in the center and unbonded zones on both sides. In particular, an obvious wavy interface with gradually increased amplitude was detected in the bonded zone. The interaction between the reflected wave and the welding collision point could promote the initiation of the wavy interface. In addition, the formation of the wavy interface depended on the impact velocity and angle of the MPW process. The qualified mechanical properties of the joint could be attributed to the formation of the wavy interface. The microhardness at the interface was higher than that on both sides, owing to work hardening, at approximately 226 HV. Full article

An accurate estimation of Differential Code Bias (DCB) is essential for high-precision applications of the Global Navigation Satellite System (GNSS) and for the precise determination of GNSS-derived total electron content (TEC). This study leverages BeiDou Navigation Satellite System (BDS) and Global Positioning System (GPS) dual-frequency observations of the China Seismo-electromagnetic Satellite (CSES) from day of the year (DOY) 201 to DOY 232 in 2018, we evaluate the quality of CSES onboard GNSS observations, improve the data preprocessing method, and use the least-squares to estimate DCBs for both GNSS satellites and CSES receivers. A comprehensive analysis of the estimation accuracy is presented, revealing that DCBs for BDS satellites, derived from joint BDS and GPS observations, exhibit superior consistency compared to those from single BDS observations. Notably, the stability of DCBs for the CSES BDS receiver as well as for BDS GEO, IGSO, and MEO satellites has been significantly enhanced by 70%, 14%, 22%, and 23%, respectively. Conversely, the consistency of GPS satellite DCBs estimated from joint observations shows a decline when compared to the DCB products from the Center for Orbit Determination in Europe (CODE) and the Chinese Academy of Sciences (CAS). When fewer than nine satellites are tracked daily and nighttime observations are under 25%, estimation errors increase. The optimal DCB estimation is achieved with a cutoff elevation angle set at 10°, with monthly mean DCB values for CSES GPS and BDS receivers determined to be −2.193 ns and −1.099 ns, respectively, accompanied by root mean square errors (RMSEs) of 0.10 ns and 0.31 ns. The highest accuracy of DCBs estimated by the single-GPS scheme is corroborated by examining the occurrence of negative vertical total electron content (VTEC) percentages. Full article

Background: Falls and fall consequences in older adults are global health issues. Previous studies have compared postural sways or stepping strategies between older adults with and without fall histories to identify factors associated with falls. However, more in-depth neuromuscular/kinematic mechanisms have remained unclear. This study aimed to comprehensively investigate muscle activities and joint kinematics during reactive balance control in older adults with different fall histories. Methods: This pilot observational study recruited six community-dwelling older fallers (≥1 fall in past one year) and six older non-fallers, who received unpredictable translational balance perturbations in randomized directions and intensities during standing. The whole-body center-of-mass (COM) displacements, eight dominant-leg joint motions and muscle electrical activities were collected, and analyzed using the temporal and amplitude parameters. Results: Compared to non-fallers, fallers had significantly: (a) smaller activation rate of the ankle dorsiflexor, delayed activation of the hip flexor/extensor, larger activation rate of the knee flexor, and smaller agonist-antagonist co-contraction in lower-limb muscles; (b) larger knee/hip flexion angles, longer ankle dorsiflexion duration, and delayed timing of recovery in joint motions; and (c) earlier downward COM displacements and larger anteroposterior overshooting COM displacements following unpredictable perturbations (p < 0.05). Conclusions: Compared to non-fallers, fallers used more suspensory strategies for reactive standing balance, which compensated for inadequate ankle/hip strategies but resulted in prolonged recovery. A further longitudinal study with a larger sample is still needed to examine the diagnostic accuracies and training values of these identified neuromuscular/kinematic factors in differentiating fall risks and preventing future falls of older people, respectively. Full article

Aerosol optical and microphysical properties determine their radiative capabilities, climatic impacts, and health effects. Satellite remote sensing is a crucial tool for obtaining aerosol parameters on a global scale. However, traditional physical and statistical retrieval methods face bottlenecks in data mining capacity as the volume of satellite observation information increases rapidly. Artificial intelligence methods are increasingly applied to aerosol parameter retrieval, yet most current approaches focus on end-to-end single-parameter retrieval without considering the inherent relationships among multiple aerosol properties. In this study, we propose a sequence-to-sequence aerosol parameter joint retrieval algorithm based on the transformer model S2STM. Unlike conventional end-to-end single-parameter retrieval methods, this algorithm leverages the encoding–decoding capabilities of the transformer model, coupling multi-source data such as polarized satellite, meteorological, model, and surface characteristics, and incorporates a physically coherent consistency loss function. This approach transforms traditional single-parameter numerical regression into a sequence-to-sequence relationship mapping. We applied this algorithm to global observations from the Chinese polarimetric satellite (the Particulate Observing Scanning Polarimeter, POSP) and simultaneously retrieved multiple key aerosol optical and microphysical parameters. Event analyses, including dust and pollution episodes, demonstrate the method’s responsiveness in hotspot regions and events. The retrieval results show good agreement with ground-based observation products. This method is also adaptable to satellite instruments with various configurations (e.g., multi-wavelength, multi-angle, and multi-dimensional polarization) and can further improve its spatiotemporal generalization performance by enhancing the spatial balance of ground station training datasets. Full article

An approach to estimate the dynamic characteristic of multilayered three-dimensional cubic quasicrystal cylindrical shells, annular plates, and truncated conical shells with different boundary conditions is presented. These investigated structures can be in a vacuum, totally filled with quiescent fluid, and subjected to internal flowing fluid where the fluid is incompressible and inviscid. The velocity potential, Bernoulli’s equation, and the impermeability condition have been applied to the shell–fluid interface to obtain an explicit expression, from which the fluid pressure can be converted into the coupled differential equations in terms of displacement functions. The state-space method is formulated to quasicrystal linear elastic theory to derive the state equations for the three structures along the radial direction. The mixed supported boundary conditions are represented by means of the differential quadrature technique and Fourier series expansions. A global propagator matrix, which connects the field variables at the internal interface to those at the external interface for the whole structure, is further completed by joint coupling matrices to overcome the numerical instabilities. Numerical examples show the correctness of the proposed method and the influence of the semi-vertical angle, different boundary conditions, and the fluid debit on the natural frequencies and mode shapes for various geometries and boundary conditions. Full article