Parametric Model Research Articles

Diagnosis of Voltage Losses of the Proton Exchange Membrane Water Electrolyzer with Nonlinear Frequency Response Method

Proton exchange membrane water electrolyzer (PEMWE) takes a central place in the hydrogen economy as a crucial technology for green hydrogen production. However, this technology still faces challenges, such as the cost of the materials, performance losses at high current densities, and durability. To overcome these obstacles, better comprehension of the phenomena occurring in the PEMWE is necessary. Typical electrochemical methods, such as polarization curves and high-frequency response measurements, lack detailed information about the individual contributions to the overall voltage losses and cannot point out the causes of the performance shortcomings. Therefore, the development of advanced diagnosis techniques for PEMWE is of importance both for the state of health analysis during the operation, as well as for the testing of new materials.In this work, a nonlinear frequency response (NFR) method was applied for the diagnosis of a lab-scale PEMWE. The NFR method represents an extension of the electrochemical impedance spectroscopy to the nonlinear domain by investigating the second-order frequency response function (FRF) in addition to the first-order FRF (equivalent to the impedance) [1]. Typical features corresponding to processes occurring in the PEMWE were observed in both the first- and the second-order FRFs. The experimental observations were interpreted based on a simple nonlinear model. The features observed at frequencies higher than 0.1 Hz were attributed to the electrochemical half-reactions, while a high current density feature observed at lower frequencies, could be attributed to the mass transport. During the analysis, the first-order FRF (impedance) was found not to be sensitive enough for the discrimination of the different sets of parameters and the second-order FRF had to be included for better parameter identification. The obtained parametrized model is dynamic and nonlinear and, thus, it can be utilized for model predictive control of the PEMWE and optimization of the overall green hydrogen production system.[1] Vidaković-Koch, T., Miličić, T., Živković, L.A., Chan, H.S., Krewer, U., Petkovska, M., Cur. Opin. Electrochem., 2021, 30, 100851

An analytic method to investigate hemodynamics of the cardiovascular system: Biventricular system.

Previously, we found analytic solutions for single ventricular system based on the lumped parameter model (LPM). In this study, we generalized the method to biventricular system and derived its analytic solutions. LPM is just a set of differential equations, but it is difficult to solve due to time-varying ventricular elastance and high order. Mathematically, there exist no elementary solutions for time-varying equations. It turns out that instead of differential equations, according to volume conservation, a set of algebraic equations can be carried out. The solutions of the set of equations are just physiological states at end of systolic and diastolic phases such as end systolic/diastolic pressure/volume of left ventricle. As a preliminary application, the method is utilized to deduce the hemodynamic effects of VA ECMO. Left ventricular (LV) distension, a serious complication of VA ECMO, is usually attributed to factors such as increased afterload, inadequate LV unloading, reduced myocardial contractility or aortic valve regurgitation (AR), bronchial and Thebesian return in the absence of aortic valve (AoV) opening. Among these, reduced contractility and AR are strongly associated with LV distension. However, in the absence of reduced contractility or AR, it is less clear whether increased afterload or inadequate LV unloading alone can cause LV distension. This leads to the critical question: under what conditions does LV distension occur in the absence of reduced contractility or AR? The analytic formulas derived in this study give conditions for LV distension. Furthermore, the results show that the analytic hemodynamics are coincident with simulated results.

Research on Automated Modeling Technology of Sluice Gate Project Based on BIM Technology.

In order to improve the efficiency and accuracy of the modeling and design work of the sluice gate project, this paper proposes an automatic generation template of the sluice gate project with customized semantics and project layout scheme, aiming at realizing the rapid assembling of all kinds of components of the sluice gate project. In the construction process, this paper first starts from basic parametric modeling and proposes constraints as the basis of modeling. Subsequently, a template library framework is developed based on the constraints to ensure that the generated templates have a high degree of standardization and consistency. Finally, an efficient and flexible template library is successfully constructed by using the customized classes and functions of Revit API, which provides powerful technical support for the modeling and design work of sluice gate engineering. This achievement helps to promote the informationization and intelligent development of the water conservancy engineering industry, and its versatility and scalability also make it have a wide range of application prospects in other water conservancy engineering fields.

High-latitude coronal mass ejections on the young solar-like star AB Dor

ABSTRACT AB Dor is a young solar-type star with a surface large-scale magnetic field $10^2$ to $10^3$ times stronger than the that of the Sun. Although strong magnetic fields are thought to inhibit coronal mass ejections (CMEs), dimming signatures typically associated with an eruptive CME were recently observed in AB Dor. The uninterrupted, long-duration dimming signal suggests that a CME took place at a high latitude, where it remained in view as the star rotates. A high-latitude CME is also consistent with observations that indicate that AB Dor hosts polar active regions. To investigate magnetic confinement in AB Dor, we conduct a parametric modelling study of 21 CMEs at latitudes ${\sim} 60^\circ$, varying the location, mass, and magnetic field strength of an injected flux rope. 12 models had the flux rope located in an open magnetic field region, while the remaining nine were in a closed region. Results show that CMEs in open-field regions are in general more likely to erupt. The four eruptive CMEs from closed regions had high free magnetic energies ${\gtrsim} 3\times 10^{35}$ erg, and 10 CMEs predominantly from the closed-field regions (8/10) were confined. CMEs in closed-field regions exhibited lower kinetic energies, since part of the CME energy was expended to overcome magnetic tension and break open the overlying field. In conclusion our work suggests that eruptive CMEs in AB Dor may occur in high-latitude regions of open magnetic field, as the magnetic tension in such regions does not significantly inhibit the eruption.

Measuring the individualization potential of treatment individualization rules: Application to rules built with a new parametric interaction model for parallel-group clinical trials.

For personalized medicine, we propose a general method of evaluating the potential performance of an individualized treatment rule in future clinical applications with new patients. We focus on rules that choose the most beneficial treatment for the patient out of two active (nonplacebo) treatments, which the clinician will prescribe regularly to the patient after the decision. We develop a measure of the individualization potential (IP) of a rule. The IP compares the expected effectiveness of the rule in a future clinical individualization setting versus the effectiveness of not trying individualization. We illustrate our evaluation method by explaining how to measure the IP of a useful type of individualized rules calculated through a new parametric interaction model of data from parallel-group clinical trials with continuous responses. Our interaction model implies a structural equation model we use to estimate the rule and its IP. We examine the IP both theoretically and with simulations when the estimated individualized rule is put into practice in new patients. Our individualization approach was superior to outcome-weighted machine learning according to simulations. We also show connections with crossover and N-of-1 trials. As a real data application, we estimate a rule for the individualization of treatments for diabetic macular edema and evaluate its IP.

Grasshopper platform-assisted design optimization of fujian rural earthen buildings considering low-carbon emissions reduction

This work aims to explore optimization methods for the design of earthen buildings in rural Fujian to achieve low-carbon emissions and improve the structural stability of earthen buildings. First, parametric modeling and optimization algorithms are employed through the Grasshopper platform. An intelligent earthen building design is created by combining the optimization of factors such as the structure of earthen buildings, building materials, and orientation. Then, a comparison is made with the unoptimized, energy-efficient, and carbon emission reduction designs. Finally, the work concludes that the proposed design significantly optimizes the total carbon emissions, energy consumption, structural stability, and economic aspects. The proposed design scheme achieves the highest carbon emission reduction effect, with a reduction rate of 34.64%. The proposed design exhibits lower maximum stress and higher minimum safety factor in terms of structural stability compared to other scenarios, along with smaller structural displacement. It also performs well in terms of initial investment, annual operating costs, and construction period. The significance of this work lies in providing scientific guidance for the design and construction of rural earthen buildings, promoting the organic integration of rural development with low-carbon initiatives. This indicates that the use of intelligent optimization methods for earthen building design is feasible and can yield positive results in practice.

Spatial Prediction of Organic Matter Quality in German Agricultural Topsoils

Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial. Three ensemble machine learning models were trained to obtain spatial predictions of the C/N ratio, MAOM, and POM in German agricultural topsoil (0–10 cm). Parameter optimization and model evaluation were performed using nested cross-validation. Additionally, a modification to the regressor chain was applied to capture and interpret the interactions among the C/N ratio, MAOM, and POM. The ensemble models yielded mean absolute percent errors (MAPEs) of 8.2% for the C/N ratio, 14.8% for MAOM, and 28.6% for POM. Soil type, pedo-climatic region, hydrological unit, and soilscapes were found to explain 75% of the variance in MAOM and POM, and 50% in the C/N ratio. The modified regressor chain indicated a nonlinear relationship between the C/N ratio and SOM due to the different decomposition rates of SOM as a result of variety in its nutrient quality. These spatial predictions enhance the understanding of soil properties’ distribution in Germany.

Applying machine learning approach in predicting short-term rockburst risks using microseismic information: a comparison of parametric and non-parametric models

Applying machine learning approach in predicting short-term rockburst risks using microseismic information: a comparison of parametric and non-parametric models

In-silico Evaluation of the effects of blood flow rate change in ductus arteriosus on cardiac function and cerebral blood flow rate in term and preterm infants

The transition of cardiovascular system is vital for newborn infants. It is expected that ductus arteriosus which is vessel between aorta and pulmonary arteries closes in the first day of life. However, ductus arteriosus may not close within 24 h in preterm infants. In this study, a lumped parameter model was used to evaluate the effects of the change in blood flow rate through ductus arteriosus on cardiac function and cerebral blood flow rate in term and preterm neonatal infants. Mean arterial pressure increased from 41 mmHg to 53 mmHg at 24th hour after birth in term neonatal infant cardiovascular system model whereas it increased from 33 mmHg to 43 mmHg in preterm neonatal infant cardiovascular system model. Left cardiac output decreased from 911 mL/min to 721 mL/min and right cardiac output increased 551 mL/min to 706 mL/min in term neonatal infant cardiovascular system model. Left cardiac output decreased from 705 mL/min to 612 mL/min and right cardiac output increased 369 mL/min to 484 mL/min in preterm neonatal infant cardiovascular system model. Cerebral blood flow rate increased from 115 mL/min to 147.40 mL/min in term neonatal infant cardiovascular system model at 24th hour after birth and it increased 29.65 mL/min to 38.92 mL/min in preterm neonatal cardiovascular system model. The results showed that blood flow rate through ductus arteriosus alters cardiac function and cerebral blood flow rate in both term and preterm cardiovascular system models.

Using joint probability density to create most informative unidimensional indices: a new method using pain and psychiatric severity as examples

BackgroundDimension reduction methods do not always reduce their underlying indicators to a single composite score. Furthermore, such methods are usually based on optimality criteria that require discarding some information. We suggest, under some conditions, to use the joint probability density function (joint pdf or JPD) of p-dimensional random variable (the p indicators), as an index or a composite score. It is proved that this index is more informative than any alternative composite score. In two examples, we compare the JPD index with some alternatives constructed from traditional methods.MethodsWe develop a probabilistic unsupervised dimension reduction method based on the probability density of multivariate data. We show that the conditional distribution of the variables given JPD is uniform, implying that the JPD is the most informative scalar summary under the most common notions of information. B. We show under some widely plausible conditions, JPD can be used as an index. To use JPD as an index, in addition to having a plausible interpretation, all the random variables should have approximately the same direction(unidirectionality) as the density values (codirectionality). We applied these ideas to two data sets: first, on the 7 Brief Pain Inventory Interference scale (BPI-I) items obtained from 8,889 US Veterans with chronic pain and, second, on a novel measure based on administrative data for 912 US Veterans. To estimate the JPD in both examples, among the available JPD estimation methods, we used its conditional specifications, identified a well-fitted parametric model for each factored conditional (regression) specification, and, by maximizing the corresponding likelihoods, estimated their parameters. Due to the non-uniqueness of conditional specification, the average of all estimated conditional specifications was used as the final estimate. Since a prevalent common use of indices is ranking, we used measures of monotone dependence [e.g., Spearman’s rank correlation (rho)] to assess the strength of unidirectionality and co-directionality. Finally, we cross-validate the JPD score against variance–covariance-based scores (factor scores in unidimensional models), and the “person’s parameter” estimates of (Generalized) Partial Credit and Graded Response IRT models. We used Pearson Divergence as a measure of information and Shannon’s entropy to compare uncertainties (informativeness) in these alternative scores.ResultsAn unsupervised dimension reduction was developed based on the joint probability density (JPD) of the multi-dimensional data. The JPD, under regularity conditions, may be used as an index. For the well-established Brief Pain Interference Inventory (BPI-I (the short form with 7 Items) and for a new mental health severity index (MoPSI) with 6 indicators, we estimated the JPD scoring. We compared, assuming unidimensionality, factor scores, Person’s scores of the Partial Credit model, the Generalized Partial Credit model, and the Graded Response model with JPD scoring. As expected, all scores’ rankings in both examples were monotonically dependent with various strengths. Shannon entropy was the smallest for JPD scores. Pearson Divergence of the estimated densities of different indices against uniform distribution was maximum for JPD scoring.ConclusionsAn unsupervised probabilistic dimension reduction is possible. When appropriate, the joint probability density function can be used as the most informative index. Model specification and estimation and steps to implement the scoring were demonstrated. As expected, when the required assumption in factor analysis and IRT models are satisfied, JPD scoring agrees with these established scores. However, when these assumptions are violated, JPD scores preserve all the information in the indicators with minimal assumption.

Enhancing cerebral arteriovenous malformation analysis: Development and application of patient-specific lumped parameter models based on 3D imaging data

Objectives:Cerebral arteriovenous malformations (AVMs) present complex neurovascular challenges, characterized by direct arteriovenous connections that disrupt normal brain blood flow dynamics. Traditional lumped parameter models (LPMs) offer a simplified angioarchitectural representation of AVMs, yet often fail to capture the intricate structure within the AVM nidus. This research aims at refining our understanding of AVM hemodynamics through the development of patient-specific LPMs utilizing three-dimensional (3D) medical imaging data for enhanced structural fidelity. Methods:This study commenced with the meticulous delineation of AVM vascular architecture using threshold segmentation and skeletonization techniques. The AVM nidus’s core structure was outlined, facilitating the extraction of vessel connections and the formation of a detailed fistulous vascular tree model. Sampling points, spatially distributed and derived from the pixel intensity in imaging data, guided the construction of a complex plexiform tree within the nidus by generating smaller Y-shaped vascular formations. This model was then integrated with an electrical analog model to enable precise numerical simulations of cerebral hemodynamics with AVMs. Results:The study successfully generated two distinct patient-specific AVM networks, mirroring the unique structural and morphological characteristics of the AVMs as captured in medical imaging. The models effectively represented the intricate fistulous and plexiform vessel structures within the nidus. Numerical analysis of these models revealed that AVMs induce a blood shunt effect, thereby diminishing blood perfusion to adjacent brain tissues. Conclusion:This investigation enhances the theoretical framework for AVM research by constructing patient-specific LPMs that accurately reflect the true vascular structures of AVMs. These models offer profound insights into the hemodynamic behaviors of AVMs, including their impact on cerebral circulation and the blood steal phenomenon. Further incorporation of clinical data into these models holds the promise of deepening the theoretical comprehension of AVMs and fostering advancements in the diagnosis and treatment of AVMs.

Mathematical Modeling of Tumor Growth in Preclinical Mouse Models with Applications in Biomarker Discovery and Drug Mechanism Studies.

Oncology drug efficacy is evaluated in mouse models by continuously monitoring tumor volumes, which can be mathematically described by growth kinetic models. While past studies have investigated various growth models, their reliance on small datasets raises concerns about whether their findings are truly representative of tumor growth in diverse mouse models under different vehicle or drug treatments. Here, we systematically evaluated six parametric models (exponential, exponential quadratic, monomolecular, logistic, Gompertz, and von Bertalanffy) and the semi-parametric generalized additive model (GAM) on fitting tumor volume data from over 30,000 mice in 930 experiments conducted in patient-derived xenografts, cell line-derived xenografts, and syngeneic models. We found that the exponential quadratic model is the best parametric model and can adequately model 87% studies, higher than other models including von Bertalanffy (82%) and Gompertz (80%) models, the latter is often considered the standard growth model. On the mouse group level, 7.5% of growth data could not be fit by any parametric model and were fitted by GAM. We show that eGaIT, a GAM derived efficacy metric, is equivalent to eGR, a metric we previously proposed and conveniently calculated by simple algebra. Using five studies on Paclitaxel, anti-PD-1 antibody, Cetuximab, Irinotecan, and Sorafenib, we show that exponential and exponential quadratic models achieve similar performance in uncovering drug mechanism and biomarkers. We also compared eGR-based association analysis and exponential modeling approach in biomarker discovery and found they complement each other. Modeling methods herein are implemented in an open-source R package freely available at https://github.com/hjzhou988/TuGroMix.

Scan-to-HBIM-to-VR: An Integrated Approach for the Documentation of an Industrial Archaeology Building

In this paper, we propose a comprehensive and optimised workflow for the documentation and the future maintenance and management of a historical building, integrating the state of the art of different techniques, in the challenging context of industrial archaeology. This approach has been applied to the hydraulic work of the “Sostegno del Battiferro” in Bologna, Italy, an example of built industrial heritage whose construction began in 1439 and remains in active use nowadays to control the Navile canal water flow rate. The initial step was the definition of a 3D topographic frame, including geodetic measurements, which served as a reference for the complete 3D survey integrating Terrestrial Laser Scanning (TLS), Structured Light Projection scanning, and the photogrammetric processing of Unmanned Aircraft System (UAS) imagery through a Structure from Motion (SfM) approach. The resulting 3D point cloud has supported as-built parametric modelling (Scan-to-BIM) with the consequent extraction of plans and sections. Finally, the Heritage/Historic Building Information Modelling (HBIM) model generated was rendered and tested for a VR-based immersive experience. Building Information Modelling (BIM) and virtual reality (VR) applications were tested as a support for the management of the building, the maintenance of the hydraulic system, and the training of qualified technicians. In addition, considering the historical value of the surveyed building, the methodology was also applied for dissemination purposes.

Sculptural Hybridization: Combining Digital Parametric Modeling and Manufacturing with Traditional Handcrafting Techniques

Abstract Traditional handcrafting techniques used in the art of sculpting usually produce three-dimensional works of art, while modern reproducing techniques specific to copying and printing machines usually produce two-dimensional results. Mixing these techniques has had a major role in the development of recently emergent 3D digital fabrication techniques. Through a personal case study, this article discusses the similarities and differences between specific traditional additive manual processes used in the art of sculpting and additive three-dimensional modeling and 3D printing techniques. The case study showcases the hybridization of digital modeling and 3D printing processes with traditional physical handcrafting techniques to chart and inform a mixed media approach, with an array of positive outcomes in the art of sculpting and architecture.

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Background and ObjectivesTechniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. MethodsTo construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images. ResultsComparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images. ConclusionsThe cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

Agent-based simulation model of micro-mobility trips in heterogeneous and perceived unsafe road environments

Safety concerns expressed by micro-mobility users seem to practically constrain the use and flexibility of these new modes. This paper introduces a new, parametric agent-based modeling approach for simulating micro-mobility, using MATSim as a platform. Perceived safety is introduced as a factor, which affects travel behavior of micro-mobility modes in a car-dominated, heterogeneous, and perceived unsafe road network. To meet the research objectives, different scenarios are simulated, using the city of Athens, Greece as a test bed, to examine the model performance and applicability. A universal scoring function based on travel time, cost, and safety, is proposed. This study also discusses the concept of unsafe discontinuities. Simulation results show that an unsafe road segment of 500 m long is sufficient to induce evident behavioral variations and hinder the practical use of e-bikes and e-scooters in the base scenario. The establishment of a cycle-friendly road network leads to significant changes in the chosen routes and decreases the average trip distance for micro-mobility users. In this case, the simulation process is guided by perceived safety, primarily focusing on formulating safer paths for micro-mobility mode users. Nevertheless, the routing behavior of car users is not modified due to the spatial uniformity of safety perceptions in a car-dominated road environment.

IFCI-SA: International financial conditions index for South American economies

The Russian invasion of Ukraine in early 2022, triggered a wave of risk aversion in the global financial markets. In contrast to previous events, South American emerging economies experienced limited impact to this more restrictive global financial environment. To measure the financial conditions of these economies over time, we propose an International Financial Conditions Index for South American economies (IFCI-SA), built from a parametric factor model. This index includes standard variables provided by the literature, along with sovereign debt risk premia and the most relevant commodity prices for the region. We use our model and index to: (i) discuss South American financial conditions from 2007 to 2024; (ii) explain the impact of a particular global event like the Russo-Ukrainian war; (iii) decompose risk premia movements into global and domestic shocks; and (iv) test its forecasting capabilities.

Impact of cancer on the mortality of patients with idiopathic inflammatory myopathies by flexible parametric multistate modelling.

Patients with idiopathic inflammatory myopathies (IIM) have an increased risk of cancer, but their cancer-related disease burden remains unclear. To explore how cancer might impact the mortality of patients with IIM and examine the associated prognostic factors for cancer and death. We identified patients with IIM diagnosed between 1998 and 2020 and ascertained their cancer and death records via linkage to the Swedish healthcare and population registers. Transition hazards from IIM diagnosis to cancer and death were estimated in multistate models using flexible parametric methods. We then predicted the probability of having cancer or death, and the duration of staying alive at a given time from IIM and cancer diagnoses from a crude model. We also explored prognostic factors for progression to cancer and death in a multivariable model. Of 1826 IIM patients, 310 (17%) were diagnosed with cancer before and 306 (17%) after IIM diagnosis. In patients diagnosed with cancer after IIM, the 5-year probability of death from cancer and from other causes was 31% and 18%, respectively, compared to 7% and 15% in patients without cancer after IIM. We reported several factors associated with risk of progression to cancer and death. Specifically, patients with first cancer after IIM who were older at IIM diagnosis, had cancer history, dermatomyositis and a cancer diagnosis within 1 year following IIM faced a greater cancer-specific mortality. We observed a substantial increase in mortality from cancer, compared to before, rather than other causes after a cancer diagnosis following IIM, suggesting an unmet medical need for effective cancer management in IIM patients. This finding, along with the identified prognostic factors, provides useful insight into future research directions for improving cancer management in IIM patients.

Directive transportation in smart cities with line connectivity at distinctive points using mode control algorithm

This article examines the operational functionality of intelligent transport systems to enhance smart cities by reducing traffic congestion. Given the increasing populations of smart cities, there is a growing demand for public transit systems to address the issue of traffic congestion. Therefore, the suggested system is developed using a few parametric design models, which combine point-to-point protocol and mode control optimization. The multi-objective parametric design for a smart transportation system is conducted using min–max functions to minimize the waiting time period for end users. Furthermore, customers are given the option to utilize a line following mechanism that offers suitable connectivity, along with independent identification and revitalize functions. The predicted model effectively eliminates the delay produced by transportation devices when positioning units are involved, ensuring that individual messages are delivered without any interruptions. In order to evaluate the results of the proposed system model, four different scenarios were examined. A comparison analysis revealed that the suggested method achieves a suitable directional flow for 96% of smart transport units. Additionally, it reduces delays and waiting periods by 2% and 6% respectively, while increasing energy consumption by 29%.

Failure analysis of titanium honeycomb sandwich structures subjected to three-point bending in high-temperature condition

In this study, the failure process and failure mechanism of Ti65 titanium sandwich structure subjected to three-point bending under high temperature are explored by the combination method of experimental analysis and numerical prediction. Test results show that the failure modes change and a 50% decrease in the peak compressive load occurs under high temperature. A self-developed parametric finite element model is deployed to recreate the structural details of sandwich honeycomb structures and exhibit the failure process of the titanium sandwich structure under three-point bending. The predicted failure process and critical parameters have excellent consistency with test results.