Joint Model Research Articles

Dynamic Modeling and Analysis of Flexible-Joint Robots with Clearance.

The coupling effects of flexible joints and clearance on the dynamics of a robotic system were investigated. A numerical analysis was undertaken to reveal the coupling effects between flexible joints and clearance. The nonlinear spring-damping model and Coulomb model were applied to characterize the contact characteristics of the clearance, and a model for the flexible joint was formulated using the equivalent spring theory. An accurate robot model was established based on the clearance and joint flexibility characterization. The dynamic equation of a robot was obtained according to the Newton-Euler method. A comparative analysis was performed to assess the impacts of both the joint action of clearance and flexible joints and varying joint clearance values on the performance of the robot. The results showed that the coupling effects of flexible joints and clearance had a negative impact on the system dynamic performance. The amplitudes of the dynamic responses caused by the clearance are weakened by the flexible joint, but it leads to the lag of the system response. This study served as the theoretical foundation for exploring precise control techniques in robotics research.

Sea Surface Wind Speed Estimation From the Combination of Satellite Scatterometer and Radiometer Parameters

AbstractSatellite research on global sea surface winds is crucial for understanding and monitoring the dynamics of earth's oceans, providing valuable insights into weather patterns, climate changes, and oceanographic processes. This study investigates the fusion of active (microwave scatterometer) and passive (microwave radiometer) satellite data using Machine learning (ML) for sea surface wind speed estimation. Employing Random Forest Regression (RF), Convolutional Neural Network Regression (CNN), and Multiple Linear Regression (MLR). Evaluation against reference data sets (Advanced Scatterometer, ERA5, Cross‐Calibrated Multi‐Platform (CCMP), buoy wind speeds) highlights the robustness of the proposed models. The research findings indicate that both RF and CNN exhibit superior accuracy in the active, passive, and joint active‐passive models compared to the simplistic MLR model. The joint models of the three regression methods outperform the individual active or passive models. The root mean square deviation (RMSD) accuracy of RF and CNN joint models, when compared with ASCAT, is in the order of 0.7 m/s, and when compared with buoys, the RMSD accuracy is around 1.1 m/s. The ML models, especially RF and CNN, demonstrate superior performance, providing accurate and reliable estimations crucial for meteorological and oceanographic applications. These findings underscore the potential operational use of ML techniques in enhancing remote sensing applications.

Biomechanical finite element analysis of various tibial plateau posterior tilt angles in medial unicondylar knee arthroplasty.

The present study aimed to determine the optimal posterior tibial plateau inclination for fixed-platform unicondylar knee arthroplasty (UKA) using finite element analysis (FEA). These findings provided a theoretical basis for selecting an appropriate posterior inclination of the tibial plateau during surgery. The present study utilized the FEA method to create models of fixed-platform UKA with tibial plateau posterior inclinations of 3, 6 and 9˚. The stress changes in the internal structures of each model after knee flexion motion were then compared. During knee flexion from 0 to 90˚, the contact and Von Mises equivalent stresses of the femoral condyle prosthesis and tibial platform pad revealed consistent trends of 3˚ posterior inclination, >6˚ posterior inclination and >9˚ posterior inclination. The present study established the first quasi-dynamic fixed-platform UKA model of the knee joint under load-bearing conditions. From a theoretical perspective, it was found that controlling the posterior inclination of UKA between 6 and 9˚ may be more beneficial for the survival of the tibial platform pad than between 3 and 6˚. It is also more effective in reducing pad wear.

Investigation of the Defect Effects on the Load-Carrying Capacity of Butt Joints: A Numerical Study

Determining the behavior of joints under a specific load and estimating the damage potential is essential for ensuring the durability of joints in engineering structures. Defects might occur at the adhesive layer, which causes a reduction in the durability of joints. This work aims to examine the effects of the defect presence, defect volume fraction, defect position, and random distribution of defects at the adhesive layer on the durability of the butt joint. A finite element (FE) model of the butt joint was constructed using the commercially available FE software Abaqus/Standard. The validation of the FE model was conducted by comparing its results with experimental finding reported in existing literature. Numerical and experimental results showed strong agreement, with relative errors of 2.46% and 2.95% at peak force and displacement at peak force, respectively. Defect presence significantly influences the durability of the butt joint. Defect volume fraction and defect location are the dominant parameters affecting the durability of the butt joint. The square defects at the center of the bonding layer, with volume fractions of 0.05, 0.10, and 0.15, lowers the peak force by 5.08%, 10.56%, and 15.73%, respectively. When the defect is positioned at the center of the bonding layer, adhesive failure starts at the edges of the defects. However, relocating the defect from the center to the left or upper side of the bonding layer results in adhesive failure initiation at the corresponding edges of the adhesive. Random defect distribution in the adhesive layer doesn’t affect joint durability.

Joint modeling of choices and reaction times based on Bayesian contextual behavioral control.

In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models. However, typically these models do not provide an integrated account of both value-based choices and reaction times, but simply link two types of models. Here, we propose a novel integrative joint model of both choices and reaction times by combining a computational account of Bayesian sequential decision making with a sampling procedure. This allows us to describe how internal uncertainty in the planning process shapes reaction time distributions. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by a Markov chain Monte Carlo (MCMC) sampler to obtain both choices and reaction times. As we will show this makes the sampler an integral part of the decision making process and enables us to reproduce, using simulations, well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. Specifically, we use the proposed model to explain both choice behavior and reaction times in instrumental learning and automatized behavior, in the Eriksen flanker task and in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in these different decision making paradigms.

Relationship between prostate-specific antigen, alkaline phosphatase levels, and time-to-tumor shrinkage: understanding the progression of prostate cancer in a longitudinal study

BackgroundThis study delves into the complex interplay among prostate-specific antigen, alkaline phosphatase, and the temporal dynamics of tumor shrinkage in prostate cancer. By investigating the longitudinal trajectories and time-to-prostate cancer tumor shrinkage, we aim to untangle the intricate patterns of these biomarkers. This understanding is pivotal for gaining profound insights into the multifaceted aspects of prostate cancer progression. The joint model approach serves as a comprehensive framework, facilitating the elucidation of intricate interactions among these pivotal elements within the context of prostate cancer .MethodsA new joint model under a shared parameters strategy is proposed for mixed bivariate longitudinal biomarkers and event time data, for obtaining accurate estimates in the presence of missing covariate data. The primary innovation of our model resides in its effective management of covariates with missing observations. Built upon established frameworks, our joint model extends its capabilities by integrating mixed longitudinal responses and accounting for missingness in covariates, thus confronting this particular challenge. We posit that these enhancements bolster the model’s utility and dependability in real-world contexts characterized by prevalent missing data. The main objective of this research is to provide a model-based approach to get full information from prostate cancer data collected with patients’ baseline characteristics (Age\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Age$$\\end{document}, body mass index (BMI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$BMI$$\\end{document}), GleasonScore\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Gleason Score$$\\end{document}, Grade\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Grade$$\\end{document}, and Drug\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Drug$$\\end{document}) and two longitudinal endogenous covariates (Platelets\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Platelets$$\\end{document} and Bilirubin\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Bilirubin$$\\end{document}).ResultsThe results reveal a clear association between prostate-specific antigen and alkaline phosphatase biomarkers in the context of time-to-prostate cancer tumor shrinkage. This underscores the interconnected dynamics of these key indicators in gauging disease progression.ConclusionsThe analysis of the prostate cancer dataset, incorporating a joint evaluation of mixed longitudinal prostate-specific antigen and alkaline phosphatase biomarkers alongside tumor status, has provided valuable insights into disease progression. The results demonstrate the effectiveness of the proposed joint model, as evidenced by accurate estimates. The shared variables associated with both longitudinal biomarkers and event times consistently deviate from zero, highlighting the robustness and reliability of the model in capturing the complex dynamics of prostate cancer progression. This approach holds promise for enhancing our understanding and predictive capabilities in the clinical assessment of prostate cancer.

Bio-based connections and hybrid planar truss: A parallel genetic algorithm approach for model updating

Bolted steel to laminated bio-based material connections experience significant performance challenges due to the nonlinear response and high stress concentrations at their joints. This paper introduces an innovative 3D plasticity-fracture continuum Finite Element (FE) model that significantly advances the simulation of such truss joints by integrating Hill's yielding criteria with an element removal methodology for fracture simulation. This novel approach captures both plastic and fracture behaviors simultaneously, a capability not sufficiently addressed in existing models. We detail the theoretical framework for these models, including the derivation of constitutive equations and the algorithms necessary for their implementation in ABAQUS. Additionally, it is provided a low-fidelity modeling of truss joints, offering a comprehensive analysis of connector elements, joint models, and parametric modeling via Python scripting. The model's efficacy is demonstrated through identification of connection and of hybrid planar trusses under cyclic loading, which validates the practical applicability of the method. To optimize computational efficiency, we developed a Parallel Genetic Algorithm (PGA) that integrates seamlessly with ABAQUS and Python to facilitate parameter calibration. This integration not only enhances the model's accuracy but also reduces computational load, making it feasible for complex engineering applications. Our findings illustrate a significant improvement in modeling accuracy and efficiency, establishing a robust methodology for analyzing truss joints in bio-based construction materials.

Study on fire resistance of high performance concrete nonplanar beam-wall joints

The utilization of high performance concrete (HPC) in the core region of beam-wall joints can enhance the structural integrity and load-bearing capacity of prefabricated buildings. However, due to the high-temperature bursting characteristics of HPC, the mechanical properties of the joints degrade prematurely under fire. In this paper, the fire resistance of HPC nonplanar beam-wall joints was investigated using high-temperature experiments and numerical simulations. First, the prefabricated beam-wall joint and the cast-in-place beam-wall joint proposed in this study were subjected to high-temperature and post-fire static loading tests, enabling a comparative analysis of their fire resistance and load-bearing capacity. The results demonstrated that the utilization of HPC in the core region of the prefabricated beam-wall joints can enhance its load-bearing capacity and fulfill the fire resistance requirements. Subsequently, a finite element model of the nonplanar beam-wall joint was established based on test results to investigate the influence of different structural parameters on its post-fire mechanical properties. The results demonstrate that the diameter of vertical reinforcement in the wall can significantly influence the post-fire load-bearing capacity of the joints, while the concrete strength in the core area of the joints has little impact on the post-fire bearing performance of the joints. Compared to the conventional cast-in-place beam-wall joints with normal strength concrete, the prefabricated beam-wall joints have superior fire resistance and post-fire load-bearing performance.

TGIN: Translation-Based Graph Inference Network for Few-Shot Relational Triplet Extraction.

Extracting relational triplets aims at detecting entity pairs and their semantic relations. Compared with pipeline models, joint models can reduce error propagation and achieve better performance. However, all of these models require large amounts of training data, therefore performing poorly on many long-tail relations in reality with insufficient data. In this article, we propose a novel end-to-end model, called TGIN, for few-shot triplet extraction. The core of TGIN is a multilayer heterogeneous graph with two types of nodes (entity node and relation node) and three types of edges (relation-entity edge, entity-entity edge, and relation-relation edge). On the one hand, this heterogeneous graph with entities and relations as nodes can intuitively extract relational triplets jointly, thereby reducing error propagation. On the other hand, it enables the triplet information of limited labeled data to interact better, thus maximizing the advantage of this information for few-shot triplet extraction. Moreover, we devise a graph aggregation and update method that utilizes translation algebraic operations to mine semantic features while retaining structure features between entities and relations, thereby improving the robustness of the TGIN in a few-shot setting. After updating the node and edge features through layers, TGIN propagates the label information from a few labeled examples to unlabeled examples, thus inferring triplets from these unlabeled examples. Extensive experiments on three reconstructed datasets demonstrate that TGIN can significantly improve the accuracy of triplet extraction by 2.34%~10.74% compared with the state-of-the-art baselines. To the best of our knowledge, we are the first to introduce a heterogeneous graph for few-shot relational triplet extraction.

Population pharmacokinetics of flucloxacillin as intermittent bolus infusion in patients with Staphylococcus aureus bloodstream infection.

Optimal antibiotic dosing for Staphylococcus aureus bloodstream infections (BSI) is still controversial. One reason is inter-individual variation in pharmacokinetics, which may be influenced by various patient-related factors, particularly in critically ill patients. To describe the population pharmacokinetics (PopPK) of the antibiotic flucloxacillin in patients with S. aureus BSI. Subsequently, we sought to translate the model into a user-friendly app for generating a priori and a posteriori time-concentration curves and dose recommendations to optimize dosing regimens. Total and unbound flucloxacillin concentrations were included from 49 patients from a prospective cohort study conducted during clinical routine, including non-critically ill and critically ill individuals who received intermittent bolus applications. These data were analysed using non-linear mixed-effects modelling. Most patients (98%) were treated with 2 g of flucloxacillin every 4 h. We developed a joint model that simultaneously described total and unbound concentrations. The model included an allometric effect of glomerular filtration rate on clearance and albumin on the albumin dissociation constant. The latter was especially important, as in our population the unbound fraction was higher at 11.5% (16.7% for critically ill patients) compared with reported values of approximately 5%. Based on our joint model, we developed a web-based app for optimizing dosing regimens of flucloxacillin. By utilizing data from clinical routine, we were able to create a predictive PopPK model of flucloxacillin and identify influential covariates. The web-based app is currently being validated in a clinical trial.

Dynamics of Liver Stiffness Measurement and Clinical Course of Primary Biliary Cholangitis

Background & aimsIn primary biliary cholangitis (PBC), static liver stiffness measurement (LSM) has proven prognostic value. However, the added prognostic value of LSM time course in this disease remains uncertain. MethodsWe conducted an international retrospective cohort study among PBC patients treated with ursodeoxycholic acid (UDCA) and followed by vibration-controlled transient elastography (VCTE) between 2003 and 2022. Using joint modeling, the association of LSM trajectory and the incidence of serious clinical events (SCE), defined as cirrhosis complications, liver transplantation (LT) or death, was quantified using the hazard ratio (HR) and its confidence interval (CI). ResultsA total of 6,362 LSMs were performed in 3,078 patients (2,007 on UDCA alone; 13% with cirrhosis), in whom 316 SCE occurred over 14,445 person-years (median follow-up, 4.2 years; incidence rate, 21.9 per 1,000 person-years). LSM progressed in 59% of patients (mean 0.39 kPa/year). After adjusting for prognostic factors at baseline, including LSM, any relative change in LSM was associated with a significant variation in SCE risk (p<0.001). For example, the adjusted HRs (95% CI) associated with a 20% annual variation in LSM were 2.13 (1.89 – 2.45) for the increase and 0.40 (0.33 – 0.46) for the decrease. The association between LSM trajectory and SCE risk persisted regardless of treatment response or duration, when patients with cirrhosis were excluded, and when only death or LT was considered. ConclusionsTracking longitudinal changes in LSM using VCTE provides valuable insights into PBC prognosis, offering a robust predictive measure for the risk of SCE. LSM could be used as a clinically relevant surrogate endpoint in PBC clinical trials.

Individualized survival predictions using state space model with longitudinal and survival data.

Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model's effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.

Finite element analysis part 2 of 2: Glenohumeral bone stress distribution depends on implant configuration for anatomic and reverse stemless shoulder implants

AbstractPurposeOur purpose was to quantify stresses in the bone surrounding stemless implants in various configurations.MethodsA detailed finite element model of the glenohumeral joint was used to simulate abduction kinematics before and after arthroplasty and to measure bone stresses around the implants. Two digital patients were simulated: one healthy and one with supraspinatus muscle impairment (deficiency). Two anatomic total shoulder arthroplasty (TSA) configurations were placed in a 135° cutting plane. Two reverse shoulder arthroplasty (RSA) configurations with cutting angles of 135° and 145° were simulated with asymmetrical and symmetrical polyethylene cups, respectively, to obtain humeral neck‐shaft angles of 145°.ResultsCompared with preoperative models, TSA preserved and RSA restored abduction kinematics. The bone mechanical stresses were located mainly around the central stud of the TSA and were more peripheral to the RSA humeral components. The RSA configuration with the 145° cutting angle and symmetrical cup generated the lowest maximal bone stress and bone volume involvement. Stresses in the scapular cortical bone were highest in the supraspinatus fossa for TSA and the crest of the acromion for RSA.ConclusionEarly stability and glenohumeral bone stress change with implant configuration and should not be extrapolated from anatomic clinical data to reverse configurations.Level of EvidenceDiagnostic tests or criteria; Level IV.

Three-dimensional bone morphology is a risk factor for medial postmeniscectomy syndrome: A retrospective cohort study.

The study aims to identify differences in tibiofemoral joint morphology between responders (R group, no pain) to arthroscopic partial medial meniscectomy (APMM) versus medial postmeniscectomy syndrome patients (MPMS group, recurrent pain at 2 years postmeniscectomy) in a clinically neutrally aligned patient population. The second aim was to build a morphology-based predictive algorithm for response to treatment (RTT) in APMM. Two patient groups were identified from a large multicentre database of meniscectomy patients at 2 years of follow-up: the R group included 120 patients with a KOOS pain score > 75, and the MPMS group included 120 patients with a KOOS pain score ≤ 75. Statistical shape models (SSMs) of distal femur, proximal tibia and tibiofemoral joint were used to compare knee morphology. Finally, a predictive model was developed to predict RTT, with the SSM-derived morphologic variables as predictors. No differences were found between the R and MPMS groups for patient age, sex, height, weight or cartilage status. Knees in the MPMS group were significantly smaller, had a wider femoral notch and a smaller medial femoral condyle. A morphology-based predictive model was able to predict MPMS at 2 years follow-up with a sensitivity of 74.9% (95% confidence interval [CI]: 74.4%-75.4%) and a specificity of 81.0% (95% CI: 80.6%-81.5%). A smaller tibiofemoral joint, a wider intercondylar notch and smaller medial femoral condyle were observed shape variations related to medial postmeniscectomy syndrome. These promising results are a first step towards a knee morphology-based clinical decision support tool for meniscus treatment. Case-control study. Level IIIb.

Therapeutic Effects of Mesenchymal Stem Cells Derived from Bone Marrow and Adipose Tissue in a Rat Model of Temporomandibular Osteoarthritis

ObjectivesTo examine the potential of intra-articular administration of mesenchymal stem cells (MSCs) derived from bone marrow or adipose tissue to mitigate synovial inflammation in a rat model of temporomandibular joint (TMJ) osteoarthritis (OA). MethodsIn this experimental study, 40 rats were divided into 4 groups: (1) Control group; (2) Untreated TMJ-OA group; (3) TMJ-OA group treated with bone marrow-derived MSCs; (4) TMJ-OA group treated with adipose tissue-derived MSCs. The TMJ-OA model was established by inducing synovial inflammation through the intra-articular administration of complete Freund’s adjuvant (CFA). After 8 weeks of TMJ-OA establishment, the animals were sacrificed and each mandibular condyle was extracted for histological evaluation. ResultsThe untreated TMJ-OA group had significantly higher synovial inflammation, as indicated microscopically by higher grades of synovial membrane hyperplasia and adhesion, vascular vasodilation, and fibrin deposition than the control group (p < 0.001). Both TMJ-OA groups treated with MSCs had lower grades of synovial inflammation and less severe synovitis than the untreated TMJ-OA group (p < 0.001). The TMJ-OA group treated with adipose tissue-derived MSCs showed lower grades of synovial membrane hyperplasia and higher grades of fibrin deposition than the that treated with bone marrow-derived MSCs (p < 0.001). Other indicators of synovial inflammation and synovitis severity were comparable between the two treatment groups. ConclusionsAdministration of CFA to the TMJ-OA rat model augmented synovial inflammation. Intra-articular administration of MSCs derived from either bone marrow or adipose tissue attenuated the microscopic manifestations of this inflammation, indicating the therapeutic potential of this treatment for TMJ-OA.

Crack layer modeling of butt-fusion joints induced slow crack growth in high-density polyethylene pipes

The design and reliability of high-density polyethylene (HDPE) pipes can be dictated by the damage tolerance of their butt-fusion joints. A slow crack growth (SCG) model based on the crack layer theory for HDPE pipes internal and external circumferential and butt-fusion joints cracks is developed to investigate the discontinuous SCG behavior and lifetime tf variations. Using the developed model, the discontinuous SCG patterns, jump lengths and lifetime tf can be accurately simulated. In addition, the effects of pipes standard dimension ratio, internal pressure, and temperature on the SCG behavior and tf are investigated. Compared to pipe cracks, it was found that butt-fusion induced SCG can reduce the pipe tf > 60 %. Unlike longitudinal cracks, tf of external circumferential cracks, were found shorter than the internal ones, mandating an earlier evaluation. The new SCG model shows good accuracy with the experimental results. The same substitute geometry approach used can be followed for othercomplex designs, which aids in establishing a fundamental methodology for more realistic lifetime predictions.

An advanced heat source localization technology for intelligent warehousing: A multi-source fusion image segmentation approach leveraging infrared and visible light data

Infrared thermography technology, leveraging its unique ability to capture temperature features, has significantly improved the precision of high-temperature target localization. However, infrared imaging technology is limited by issues such as low image contrast, difficulty in distinguishing object categories, and limited image clarity. To enable intelligent detection of high-temperature objects that may cause fires in warehouses, this paper proposes an innovative method that integrates deep learning image segmentation with infrared and visible light image technology. We developed a new image segmentation model based on improved Fully Convolutional Networks and Deconvolutional Networks, introducing a batch normalization layer to accelerate convergence and employing the PReLU activation function to prevent neuron death, thereby enhancing convergence speed and accuracy. Through a feature dynamic image registration method combining a joint model and a cross-modulation strategy, we achieved efficient image fusion. In addition, a game theory-based strategy was adopted to correct localization results, ensuring accuracy. Experimental results demonstrate that the improved model achieves localization accuracy and precision rates of up to 89.30% and 88.00%, respectively, in real-world warehouse heat source scenarios, representing a significant improvement of 9.90% and 2.85% compared to the pre-improvement model, fully validating its advancement and effectiveness.

Multi-objective optimization of vehicle body B-pillar lower joints based on crashworthiness analysis

The joints in an automobile’s body structure are crucial in bearing loads and transmitting stresses, thereby significantly affecting the body’s rigidity. To effectively improve body rigidity and crashworthiness, this study employed a sensitivity analysis to identify the critical joints among the nine joints of a specific sport utility vehicle (SUV) body. Following regulatory requirements, collision simulations were performed, revealing that the joint below the B-pillar exhibited the most significant deformation. Thus, using the material and thickness of the B-pillar’s lower joint as design variables, experimental samples were generated by the design of experiment (DOE). A multi-objective optimization for the B-pillar’s lower joint model was conducted using the response surface method and the simulated annealing algorithm to determine the final optimized solution. The optimization results showed a 9.31% increase in body bending stiffness, an 11.37% increase in torsional stiffness, and reduced intrusion at various points on the B-pillar, effectively enhancing the body’s rigidity and crashworthiness.

An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data.

Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many non-spatial and spatial clustering approaches have been used to partition the entire region into mutually exclusive spatial domains based on the SRT high-dimensional molecular profile, most require an ad hoc selection of less interpretable dimensional-reduction techniques. To overcome this challenge, we propose a zero-inflated negative binomial mixture model to cluster spots or cells based on their molecular profiles. To increase interpretability, we employ a feature selection mechanism to provide a low-dimensional summary of the SRT molecular profile in terms of discriminating genes that shed light on the clustering result. We further incorporate the SRT geospatial profile via a Markov random field prior. We demonstrate how this joint modeling strategy improves clustering accuracy, compared with alternative state-of-the-art approaches, through simulation studies and 3 real data applications.

Factor-augmented transformation models for interval-censored failure time data.

Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This work proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates. We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event. Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation. We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study is provided. An R package ICTransCFA is also available for practitioners. Data used in preparation of this article were obtained from the ADNI database.