Joint Locations Research Articles

Predictive plasticity unveiled: XFEM modeling of cyclic failure in pressurized straight pipelines under three-point bending

This study presents an innovative numerical approach for predicting pressurized welded pipelines’ cyclic behavior and failure mechanisms under complex loading conditions. Focusing on X52 steel pipelines subjected to internal pressure and three-point bending, we investigate the interplay between structural integrity, weld joint positioning, and boundary conditions. Our methodology integrates advanced finite element analysis with a sophisticated material model capturing isotropic and kinematic hardening. The constitutive behavior is calibrated using the Voce model, accurately representing the material’s cyclic response. We employ the von Mises yield criterion to characterize the multiaxial stress state within the pipeline. A key innovation is our application of the eXtended Finite Element Method (XFEM) coupled with the calibrated hardening model, enabling high-fidelity simulation of crack initiation and propagation. Our model’s predictive capabilities are rigorously validated against experimental data, demonstrating excellent agreement across various loading scenarios. Through comprehensive parametric studies, we elucidate the critical influences of internal pressure, end-fixation conditions, and weld joint locations on the pipeline’s structural response. Force-displacement hysteresis curves reveal complex, nonlinear behaviors significantly impacting fatigue life and failure modes. This research advances the understanding of cyclic plasticity and fatigue in welded pressurized pipelines, providing a robust computational framework for predicting long-term performance. The insights gained have far-reaching implications for enhancing critical energy-transportation infrastructure’s safety, reliability, and longevity, potentially informing future design criteria and regulatory guidelines.

Response and seismic resilience of metro tunnels under normal fault dislocation evaluated based on the quantitative method of concrete damage state

The damage to tunnels crossing active fault zones caused by earthquakes can be of a severe and irreversible nature. The paper defines the quantification of the concrete damage state based on the uniaxial compression equation and the tensile-compression curve equation. Furthermore, the paper refines the concrete damage level in conjunction with the damage factor and crack width formulas, which is one of the most significant factors affecting the durability of concrete. A three-dimensional refinement model, constructed using ABAQUS software, was employed to analyse the mechanical response and seismic resilience of the tunnel structure under normal fault displacement with variable section mitigation measures. The model incorporated a number of key parameters, including fortification length, joint location and thickness. The following conclusions were obtained: the tunnel structure is susceptible to tensile damage and the damage is widely distributed under normal fault d; the use of variable section measures can help improve and reduce the overall damage of the cross-fault metro tunnel structure; the use of tensile damage state classification to assess the damage of the tunnel structure can determine the location and extent of the damage; an appropriate protection length is required, too long/short does not maximise the mitigation effect of setting variable section measures; an appropriate lining length is required; and the lining structure is not suitable for the tunnel structure. An appropriate location and thickness of the lining structure will help to reduce the overall damage to the structure. The application of localised reinforcement to the structure, in conjunction with variable section, serves to minimise the probability of collapse of the tunnel as a whole and to enhance the seismic resilience of the underground structure across the active fault zone.

3D Pose Nowcasting: Forecast the future to improve the present

Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of human and robot 3D poses using non-invasive systems. Therefore, in this paper, we propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. Specifically, we introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios.

Influence of the basalt fibre and fly ash on the structural properties of sustainable high-strength concrete

Abstract The present article describes the experimental research carried out to investigate the properties of concrete containing basalt fibre reinforcement and concrete containing basalt fibres and fly ash additives. Each test was done as per Indian standards (IS-516). The experiments sought to establish both fresh and hardened concrete properties. In respect of new concrete, slump, setting time, bleeding, temperature, and density were estimated to check its quality. Compressive, flexural and split tensile strength are the three main parameters for assessing those qualities in a solidified state. It was determined that the utilization of fly ash with 20%, 30% or 40% proportions of cement involved resulted in a stronger concrete mixture. To these samples, different contents of basalt fibres 0.25%, 0.5%, and 0.75% by volume were added respectively. Standard samples were cast, cured and tested accordingly. Thus, the studies show that it is possible to mix concrete with basalt fibres without any segregation or balling effect on it or even sticking together during the mixing processes. Modestly increased splitting tensile and bending strengths have been obtained due to the introduction of basalt fibres on this composite material using which improved workability can be achieved more rapidly than conventional hydration reactions based solely upon Portland Cement chemistry, moreover, thermal stability has also been considerably enhanced, besides that even basic physical-chemical properties should be very much similar to those of ordinary Portland cement. Based materials thus ensuring better compatibility during processing from raw constituents towards final composites up-to-date with known technological aspects related specifically towards production practices carried out in developing countries like India. Such situations usually result primarily due to poor engineering control exercised over construction activities. Resulting problems areas namely joint locations limited access space between steel reinforcement bars inside walls. So often appearing weak points in overall design yield inadequate structural performance compared to intended specifications. Another reason why sustainable building methods cannot be fully adopted has been found that they lack proper skilled manpower despite having all necessary resources that are easily renewable. By compared with traditional ones made up of non-renewable sources such as fossil fuels. However, fly ash used in place of concrete only makes it harder without improving workability and strength at the optimum percentage.

Design and Optimization of a Custom-Made Six-Bar Exoskeleton for Pulp Pinch Grasp Rehabilitation in Stroke Patients.

Stroke often causes neuromotor disabilities, impacting index finger function in daily activities. Due to the role of repetitive, even passive, finger movements in neuromuscular re-education and spasticity control, this study aims to design a rehabilitation exoskeleton based on the pulp pinch movement. The exoskeleton uses an underactuated RML topology with a single degree of mobility, customized from 3D scans of the patient's hand. It consists of eight links, incorporating two consecutive four-bar mechanisms and the third inversion of a crank-slider. A two-stage genetic optimization was applied, first to the location of the intermediate joint between the two four-bar mechanisms and later to the remaining dimensions. A targeted genetic optimization process monitored two quality metrics: average mechanical advantage from extension to flexion, and its variability. By analyzing the relationship between these metrics and key parameters at different synthesis stages, the population evaluated is reduced by up to 96.2%, compared to previous studies for the same problem. This custom-fit exoskeleton uses a small linear actuator to deliver a stable 12.45 N force to the fingertip with near-constant mechanical advantage during flexion. It enables repetitive pulp pinch movements in a flaccid finger, improving rehabilitation consistency and facilitating home-based therapy.

Computed tomography of the equine caudal spine and pelvis: Technique, image quality and anatomical variation in 56 clinical cases (2018-2023).

Cross-sectional imaging improves the diagnostic accuracy of complex anatomical regions. Computed tomography (CT) of the pelvis and caudal spine in a large group of live horses and ponies has not been previously reported. To describe the procedure for acquiring CT images of horses' caudal spine/pelvis under general anaesthesia (GA) and to detail the image quality, artefacts and anatomical variations in this region. Retrospective case series. Horses with CT of the caudal spine/pelvis were included. Horses under 6 months and CT examination performed post-mortem were excluded. Protocols, image quality, region of interest, anatomical features and morbidities were analysed. Fifty-six horses (8 months to 20 years, 85-680 kg) met the inclusion criteria. GA ranged from 10 to 60 min (mean: 30, median: 32). There were no adverse events recorded in any of the horses associated with the procedure. Images of all horses were considered of diagnostic quality. Anatomical variations were common and included the location of diverging (widest) interspinous space, the presence of spina bifida in the lumbar and sacral spine, the shape of the last lumbar vertebra and the location of intertransverse joints in terms of where they were present and the degree of fusion/modelling. Not all horses underwent CT examination of the same regions, the upper size limit of horses is unknown and will vary depending on bore size and table infrastructure. Image noise, particularly in large horses and beam hardening artefacts from hardware and pelvis degraded image quality. Images were of insufficient quality in large horses for soft tissue interpretation. CT of the caudal spine and pelvis in live horses with wide-bore CT machines and modified patient infrastructure was safe and produced diagnostic images.

Advancements in Subchondral Bone Biomechanics: Insights from Computed Tomography and Micro-Computed Tomography Imaging in Equine Models.

This review synthesizes recent advancements in understanding subchondral bone (SCB) biomechanics using computed tomography (CT) and micro-computed tomography (micro-CT) imaging in large animal models, particularly horses. Recent studies highlight the complexity of SCB biomechanics, revealing variability in density, microstructure, and biomechanical properties across the depth of SCB from the joint surface, as well as at different joint locations. Early SCB abnormalities have been identified as predictive markers for both osteoarthritis (OA) and stress fractures. The development of standing CT systems has improved the practicality and accuracy of live animal imaging, aiding early diagnosis of SCB pathologies. While imaging advancements have enhanced our understanding of SCB, further research is required to elucidate the underlying mechanisms of joint disease and articular surface failure. Combining imaging with mechanical testing, computational modelling, and artificial intelligence (AI) promises earlier detection and better management of joint disease. Future research should refine these modalities and integrate them into clinical practice to enhance joint health outcomes in veterinary and human medicine.

A distributed PD detection method for high voltage cables based on high precision clock synchronization

Partial discharge (PD) is a crucial cause of high voltage (HV) cable failures. Detecting and locating PD efficiently and effectively is paramount for HV cable maintenance. However, the attenuation of the PD signal after long-distance propagation makes it challenging to identify. This paper presents a distributed PD detection method that significantly reduces the propagation distance of PD signals before the sensor captures them. The technique uses the time-of-arrival to locate PD, whose localization accuracy is directly linked to the clock synchronization (CS) accuracy. CS between different sensors is achieved through three times interactions of the CS signal. The linear frequency modulation (LFM) signal is selected as the CS signal, which can generate a significant gain after pulse compression. It ensures that the CS signal can be effectively recognized after the attenuation of long-distance propagation. The paper also introduces a signal discrimination method based on the short-time Fourier transform that eliminates the influence of the LFM signal on PD identification. The cableless CS method makes it easier to set up before the onsite test. A prototype was implemented and tested. The CS accuracy was measured within 4 ns in the laboratory. An onsite test was carried out to evaluate the performance of the distributed PD detection system. A defect was found at a joint location, which was also confirmed by an additional ultrasonic PD test.

Joint Radar Sensing, Location, and Communication Resources Optimization in 6G Network

Joint Radar Sensing, Location, and Communication Resources Optimization in 6G Network

Noise-Robust 3D Pose Estimation Using Appearance Similarity Based on the Distributed Multiple Views.

In this paper, we present a noise-robust approach for the 3D pose estimation of multiple people using appearance similarity. The common methods identify the cross-view correspondences between the detected keypoints and determine their association with a specific person by measuring the distances between the epipolar lines and the joint locations of the 2D keypoints across all the views. Although existing methods achieve remarkable accuracy, they are still sensitive to camera calibration, making them unsuitable for noisy environments where any of the cameras slightly change angle or position. To address these limitations and fix camera calibration error in real-time, we propose a framework for 3D pose estimation which uses appearance similarity. In the proposed framework, we detect the 2D keypoints and extract the appearance feature and transfer it to the central server. The central server uses geometrical affinity and appearance similarity to match the detected 2D human poses to each person. Then, it compares these two groups to identify calibration errors. If a camera with the wrong calibration is identified, the central server fixes the calibration error, ensuring accuracy in the 3D reconstruction of skeletons. In the experimental environment, we verified that the proposed algorithm is robust against false geometrical errors. It achieves around 11.5% and 8% improvement in the accuracy of 3D pose estimation on the Campus and Shelf datasets, respectively.

Optimizing Defibrillator Deployment with Bus-Mounted Automated External Defibrillator

Objectives Early defibrillation with an automated external defibrillator (AED) can effectively improve the survival rate of patients with out-of-hospital cardiac arrest (OHCA). Placing AEDs in public locations can reduce the defibrillation response interval from collapse to defibrillation. Most public AEDs are currently placed in a stationary way (S-AED) with limited coverage area. Bus mounted AED (B-AED) can be delivered directly to the demand point. Although B-AEDs are only available during bus operating hours, they provide greater coverage area. When the number of available AEDs is insufficient, better coverage may be achieved by placing a portion of AEDs as B-AEDs. Our purpose is developing a model to determine the optimal locations of B-AEDs and S-AEDs with a predetermined number of available AEDs. The goal is to maximize the total coverage level of all demand points. Methods We proposed a joint location model to place B-AEDs and S-AEDs based on the p-median problem (JPMP). Using data from Chang’an District, Xi’an City, China, we determined the optimal AED deployment. The performance of JPMP was compared with several other models. The coverage results of JPMP are analyzed in details, including the quantity assignment, coverage level, and geographical location of B-AEDs and S-AEDs. The impact of the bus departure intervals on coverage was also discussed. Results The use of B-AEDs results in an average 98.43% increase in the number of covered demand points, and an average 74.05% increase in total coverage level. In optimal AED deployment, B-AEDs coverage follows an inverted U-shaped curve with increasing number of available AEDs. It begins to decrease when all demand points during the operating hours are covered. With a constant number of available AEDs, the total coverage level increases and then decreases as the bus departure interval increases. The larger the number of available AEDs, the smaller the optimal departure interval. Conclusions With a given number of available AEDs, combinational deployment of B-AEDs and S-AEDs significantly improves the coverage level. B-AEDs are recommended when AEDs are insufficient. If more AEDs are available, better coverage can be obtained with reasonable location of S-AEDs and B-AEDs.

Experimental Study on Heat Conduction and Water Migration of Composite Bentonite Samples.

The joints of buffer material composite blocks as potential weak parts in the engineering barrier system of a high-level radioactive waste (HLW) repository must be studied in depth. Therefore, a laboratory experiment device suitable for unsaturated composite bentonite samples was developed. The evolution of temperature and volumetric water content at different locations of Gaomiaozi (GMZ) composite bentonite samples with time before and after simulated water inflow was measured by the experiment device. According to the experimental results, the thermal conductivity and hydraulic conductivity of the joint location after healing of the composite bentonite samples were obtained. The experimental results show that the change in the internal temperature of the composite bentonite samples is mainly affected by the temperature boundary and that the change in the internal water has little effect on it. In a short period of time, the loading of hydraulic boundary conditions only makes the volumetric water content of the soil near the hydraulic boundary increase significantly but has little effect on other locations. And, affected by the temperature boundary, the volumetric water content of the soil near the temperature boundary gradually decreases with time. The process of hydration swelling of the composite bentonite sample is accompanied by the adjustment of stress. The composite bentonite samples are continuously squeezed to the joint area after hydration swelling, the whole composite samples are generally homogenized, and the joints between the composite bentonite samples tend to heal. The thermal conductivity and permeability of the joint location after healing can meet the requirements of the engineering barrier of the HLW repository.

Failure analysis of CFRP/Al single lap adhesive joint with enhancing porous metal foam insert

Cohesive failure is one of the main causes of adhesive bonding failure. The enhanced method and mechanism for adhesive signify a critical research issue for better adhesive joint design. This study proposes using a porous metal foam insert adhesive to enhance the carbon fiber reinforced polymer/aluminum single lap joint. In particular, stress distribution and failure locations of joints with and without insert were compared by simulation modeling. The insert of the Cu-foam, Fe-foam, and Ni-foam, compared to no insert, increased the shear strength of the bonded joint by 32.23 %, 36.66 %, and 66.82 %, respectively. Compared to no insert, the crack propagation of the joint with a metal foam insert was changed from the cross-section of the adhesive layer to the plane of the foam insert. The highest strength of the adhesive joint with the Ni-foam insert was further analyzed to determine fatigue behavior compared with pure adhesive joint under different stress levels. Compared to no insert, the fatigue life increased highly, and the crack path was prolonged.

Multi-Person Pose Regression With Distribution-Aware Single-Stage Models.

Understanding human posture is a challenging topic, which encompasses several tasks, e.g., pose estimation, body mesh recovery and pose tracking. In this article, we propose a novel Distribution-Aware Single-stage (DAS) model for the pose-related tasks. The proposed DAS model estimates human position and localizes joints simultaneously, which requires only a single pass. Meanwhile, we utilize normalizing flow to enable DAS to learn the true distribution of joint locations, rather than making simple Gaussian or Laplacian assumptions. This provides a pivotal prior and greatly boosts the accuracy of regression-based methods, thus making DAS achieve comparable performance to the volumetric-based methods. We also introduce a recursively update strategy to progressively approach the regression target, reducing the difficulty of regression and improving the regression performance. We further adapt DAS to multi-person mesh recovery and pose tracking tasks and achieve considerable performance on both tasks. Comprehensive experiments on CMU Panoptic and MuPoTS-3D demonstrate the superior efficiency of DAS, specifically 1.5 times speedup over previous best method, and its state-of-the-art accuracy for multi-person pose estimation. Extensive experiments on 3DPW and PoseTrack2018 indicate the effectiveness and efficiency of DAS for human body mesh recovery and pose tracking, respectively, which prove the generality of our proposed DAS model.

RESEARCH ON APPLICATION OF FINITE ELEMENT METHOD TO ANALYZE LOAD TRANSFER EFFICIENCY ACCORDING TO DEFLECTION OF AIRPORT RIGID PAVEMENT

The load transfer between adjacent cement concrete slabs at expansion joint locations is affected by many different factors. The load-bearing capacity of the pavement will be limited and even lead to destruction if the load transmission ability of the dowel bars does not meet the requirements. Especially, for airports that have just been put into operation or have been in operation for a long time. In this article, the authors have researched and evaluated the effectiveness of load transfer according to deflection through the application of the 3D finite element method. The model utilizes material parameters and actual airport hard pavement structures and calculates for many types of aircraft commonly operating in Vietnam. Thereby calculating and evaluating the load transfer efficiency (LTE) according to deflection for each specific type of aircraft. The model also visually shows the deformation of the transmission dowel bars and concrete slabs at the expansion joint location. The authors tested the model's reliability through experiments conducted at Tan Son Nhat International Airport which is the largest-scale and oldest airport in Vietnam. The results of calculating LTE from the simulation and experimental models have a small error (3.39%) which shows that the model is highly reliable and can be used for similar research directions without being easily done through traditional methods.

Detection and location of mount chip and solder joint based on machine vision

Detection and location of mount chip and solder joint based on machine vision

Topology Design of Compliant Mechanism Design With Multiple Component Modeling Connected by Various Joints

Abstract This study presents a novel framework for the optimal design of compliant mechanisms, specifically addressing the structural drawbacks of conventional single-point or de facto hinges. The hinges often lead to structural instability and stress concentration while deriving maximum motion. To overcome these issues, we introduce a new method that can design stable and elastic domains connected by either revolute or prismatic joints. The new method, called sequential analysis based on the reaction force, can successfully eliminate weak hinge points while optimizing joint locations. The efficiency of developed methodology is validated through several numerical examples, yielding compliant mechanisms with suppressed hinges.

The role of microstructure in the thermal fatigue of solder joints

Thermal fatigue is a common failure mode in electronic solder joints, yet the role of microstructure is incompletely understood. Here, we quantify the evolution of microstructure and damage in Sn-3Ag-0.5Cu joints throughout a ball grid array (BGA) package using EBSD mapping of localised subgrains, recrystallisation and heavily coarsened Ag3Sn. We then interpret the results with a multi-scale modelling approach that links from a continuum model at the package/board scale through to a crystal plasticity finite element model at the microstructure scale. We measure and explain the dependence of damage evolution on (i) the β-Sn crystal orientation(s) in single and multigrain joints, and (ii) the coefficient of thermal expansion (CTE) mismatch between tin grains in cyclic twinned multigrain joints. We further explore the relative importance of the solder microstructure versus the joint location in the array. The results provide a basis for designing optimum solder joint microstructures for thermal fatigue resistance.

Your body tells how you engage in collaboration: Machine‐detected body movements as indicators of engagement in collaborative math knowledge building

AbstractCollaborative learning, driven by knowledge co‐construction and meaning negotiation, is a pivotal aspect of educational contexts. While gesture's importance in conveying shared meaning is recognized, its role in collaborative group settings remains understudied. This gap hinders accurate and equitable assessment and instruction, particularly for linguistically diverse students. Advancements in multimodal learning analytics, leveraging sensor technologies, offer innovative solutions for capturing and analysing body movements. This study employs these novel approaches to demonstrate how learners' machine‐detected body movements during the learning process relate to their verbal and nonverbal contributions to the co‐construction of embodied math knowledge. These findings substantiate the feasibility of utilizing learners' machine‐detected body movements as a valid indicator for inferring their engagement with the collaborative knowledge construction process. In addition, we empirically validate that these inferred different levels of learner engagement indeed impact the desired learning outcomes of the intervention. This study contributes to our scientific understanding of multimodal approaches to knowledge expression and assessment in learning, teaching, and collaboration.Practitioner notesWhat is already known about this topic Previous research emphasizes the importance of gestures as essential tools for constructing common ground and reflecting shared meaning‐making in learning and teaching contexts. The prior studies in multimodal learning analytics (MMLA) suggest that certain forms of body movements and postures can be differentiated based on the automatic detection of upper body joint locations. Empirical observations indicate that co‐thought gestures typically involve smaller hand or arms movement that are closer to the gesturer's body than co‐speech gestures used in interpersonal communication. What this paper adds This paper fills the research gap by examining the use of gestures in collaborative learning, offering insights into how individuals contribute verbally and nonverbally to collaborative knowledge construction. This paper introduces the concept of using machine‐detected body movements as a viable proxy for inferring learners' engagement in collaborative knowledge‐building activities. Leverages sensor technologies for automatic detection of body movements, the innovative approach in this work seeks to overcome the time‐intensive and laborious process of manually coding gestures. Implications for practice and/or policy By recognizing the potential significance of learners' body movements in indicating engagement levels with collaborative knowledge‐building activities, instructors can set up computer‐supported collaborative learning (CSCL) environments to enable capturing these movements. Given the crucial role of gestures in learning, teaching, and collaboration, educators can create more equitable formative assessment practices for linguistically diverse students by developing strategies that align with multimodal forms of knowledge expression. Research can expand beyond mathematics to explore the transferability of these findings to other subjects, helping educators create comprehensive pedagogical approaches that leverage multimodal interactions across disciplines.

Detecting the symptoms of Parkinson’s disease with non-standard video

BackgroundNeurodegenerative diseases, such as Parkinson’s disease (PD), necessitate frequent clinical visits and monitoring to identify changes in motor symptoms and provide appropriate care. By applying machine learning techniques to video data, automated video analysis has emerged as a promising approach to track and analyze motor symptoms, which could facilitate more timely intervention. However, existing solutions often rely on specialized equipment and recording procedures, which limits their usability in unstructured settings like the home. In this study, we developed a method to detect PD symptoms from unstructured videos of clinical assessments, without the need for specialized equipment or recording procedures.MethodsTwenty-eight individuals with Parkinson’s disease completed a video-recorded motor examination that included the finger-to-nose and hand pronation-supination tasks. Clinical staff provided ground truth scores for the level of Parkinsonian symptoms present. For each video, we used a pre-existing model called PIXIE to measure the location of several joints on the person’s body and quantify how they were moving. Features derived from the joint angles and trajectories, designed to be robust to recording angle, were then used to train two types of machine-learning classifiers (random forests and support vector machines) to detect the presence of PD symptoms.ResultsThe support vector machine trained on the finger-to-nose task had an F1 score of 0.93 while the random forest trained on the same task yielded an F1 score of 0.85. The support vector machine and random forest trained on the hand pronation-supination task had F1 scores of 0.20 and 0.33, respectively.ConclusionThese results demonstrate the feasibility of developing video analysis tools to track motor symptoms across variable perspectives. These tools do not work equally well for all tasks, however. This technology has the potential to overcome barriers to access for many individuals with degenerative neurological diseases like PD, providing them with a more convenient and timely method to monitor symptom progression, without requiring a structured video recording procedure. Ultimately, more frequent and objective home assessments of motor function could enable more precise telehealth optimization of interventions to improve clinical outcomes inside and outside of the clinic.