Maximum Time Research Articles

Ventilation of large section blind headings under conditions of changing ore load size

Relevance. The need to ensure safe working conditions in the working areas of underground mines. Stagnant zones with low air velocities can be formed in large section blind heading behind the ore bulk when ore is shipped by mining machines. There is a possibility of the formation of local gas accumulations and their abrupt removal later in such stagnant zones due to the unsteadiness of accumulation and transfer of gas impurities in a blind heading. A sharp removal of gas will lead to contamination of the load-haul-dump operators’ workplace. Aim. To identify removal of exhaust gases in large section blind headings in conditions of changing ore load size. Objects. Large section blind headings with of complex geometry. Methods. Three-dimensional numerical simulation of unsteady turbulent flow of a gas-air mixture in the Ansys CFX, visualization and analysis of simulation data in the Wolfram Mathematica. Results. The paper presents the results of mathematical modeling of ventilation of large section blind headings of complex geometry under conditions of changing ore load size formed during mining operations by the load-haul-dump machines with an internal combustion engine. It is shown that either a single vortex is formed that ventilates the blind headings space, or a stagnant zone behind the bulk ore, which is characterized by a relatively low intensity of mass transfer. Vortex formation depends on the height of the ore bulk. The authors have obtained the dependences of the change in the average concentration of exhaust gases at the outlet of the blind headings space on the operating time of the machine. They determined the dependence of the correction volume coefficient on the geometric parameters of the blind headings. An analytical model of the removal of gases from the blind headings space under conditions of changing bulk ore volume is obtained. The expression of increment in gas concentration on an operator’s workplace makes it possible to derive formula to find maximum time of load-haul dump operation in a stope such that gas concentration is never higher than maximum allowable concentration.

Improving prediction accuracy of open shop scheduling problems using hybrid artificial neural network and genetic algorithm

Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. Regarding the existence of one machine, identification of works processing sequence forms a complete time schedule. Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. The GA and ANN methodologies were employed to analyze and forecast the scheduling of activities within the shoe manufacturing sector. The findings indicated that the probability associated with the third population of the GA was 0.15. Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives.

Development of brain metastases in patients managed with non-curative thoracic radiotherapy for stage II/III non-small cell lung cancer

BackgroundThis retrospective study analyzed the incidence of subsequent brain metastases after palliative radiotherapy or chemoradiation in patients with stage II/III non-small cell lung cancer (NSCLC). Risk factors for brain metastases development and survival after diagnosis were evaluated.MethodsDifferent baseline parameters including but not limited to age, stage and target volume size were assessed. Outcomes were abstracted from electronic health records. Uni- and multivariate tests were performed.ResultsThe study included 102 patients and found an actuarial risk of brain metastases of 15% (standard error ± 4) at one year and 20% (± 5) at two years. The maximum time interval was 15 months from start of radiation treatment. A non-significant survival difference was observed (median 12 months without versus 8.3 months with brain metastases, p = 0.21). Incidence was higher in patients with N2/3 stage, larger planning target volume size, and younger age (univariately significant factors). Trends were seen for stage III and adenocarcinoma histology. The multivariate analysis confirmed age as the most important risk factor.ConclusionThe risk of brain metastases development was comparable to that reported in studies of curative chemoradiation. All events occurred within 15 months of follow-up, suggesting that long-term surveillance imaging may not be warranted. Patients younger than 60 years had a very high risk of brain metastases development.

Early outcomes with a fully retrievable SinoCrown transcatheter heart valve in patients with severe aortic stenosis.

This study summarizes and analyzes data from patients suffering from symptomatic aortic stenosis who successfully underwent transcatheter aortic valve implantation (TAVI) using a novel, completely retrievable transcatheter heart valve. We included patients who underwent a TAVI procedure with SinoCrown valves at our center between December 2021 and September 2022. We collected 1-year follow-up data on survival, complications, echocardiographic results, New York Heart Association functional class in heart failure, and patient-reported health-related quality of life outcomes. Eight successive patients (73.3 ± 4.3 years) were included in the study, with a median Society of Thoracic Surgery risk score of 4.26%. The procedure had a 100% success rate. Median postoperative discharge time was 7 days, with no 30-day hospital readmissions. Postoperative aortic valve hemodynamics improved, indicated by decreased transvalvular flow velocity compared with preoperative values (1.9 ± 0.2 vs. 4.9 ± 0.2 m/s, p < 0.0001). The median and maximum follow-up times were 8 and 12 months, respectively. During the follow-up period, there were no serious complications such as death, stroke, valve embolization, or high-grade atrioventricular block. The results from eight initial TAVI cases performed with the SinoCrown valve demonstrated promising safety and efficacy.

Higuchi fractal dimension and deep learning on near-infrared spectroscopy for determination of free fatty acid (FFA) content in oil palm fruit

Free fatty acid (FFA) content is an essential parameter with a significant influence on the quality of oil palm, with lower levels showing higher quality. The measurement of this parameter is often carried out with conventional methods in chemical laboratories using solvents and reagents, but these techniques have several limitations. Therefore, this study aimed to develop a rapid and non-destructive technique for determining FFA content based on near-infrared (NIR) spectroscopy. This technique comprised the use of Higuchi fractal dimension (HFD) and deep learning to process NIR spectra as a new chemometric analysis. The sample population consisted of 350 oil palm fruits at various maturity ages collected from the Cikabayan Oil Palm Plantation. Each sample had its NIR spectrum acquired with a wavelength of 1000–1500 nm. Good performance was shown by a low root mean squared error (RMSE) value and a high coefficient of determination (R2). Based on numerical analysis, this study proposed HFD with a maximum discrete time interval (kmax) value of 10 and deep learning with long short-term memory (LSTM) as a robust architectural model. The results showed that the proposed technique could predict oil palm fruit FFA levels with RMSE and R2 values of 0.167 and 0.959, respectively. Furthermore, it could identify FFA content at each process step, before and after harvesting. Based on these results, its implementation in oil palm control management was expected to enhance effectiveness and efficiency, leading to the production of high-quality products.

Transport of a comb-like polymer across a nanochannel subject to a pulling force.

We investigate the dynamics of comb-like polymer translocation through a nanochannel using three-dimensional Langevin dynamics simulations based on a coarse-grained chain model. A comprehensive set of simulations are performed to examine the effects of system parameters such as the grafting densityρof the side chains, the polymer chain length, the nanochannel dimensions, and the magnitude of the pulling force on the translocation dynamics. For a given polymer chain length, keeping the backbone length is constant while varyingρ, we have found that the dependence of the mean translocation time⟨τ⟩onρis non-monotonic, with a maximum translocation time for a specificρat which the translocation is the slowest. The simulation results also show that⟨τ⟩is not significantly affected by the channel width above a certain radius, while the comb-like polymer translocation is hindered by a narrower channel due to increased interactions between the chain monomers and the channel. In addition,⟨τ⟩increases linearly with the nanochannel length. A linear scaling relationship between the mean translocation time⟨τ⟩and the chain lengthNof polymer is obtained,⟨τ⟩∼N. Similarly, the dependence of⟨τ⟩on the backbone chain sizeNbbhas a quasi-linear dependence,⟨τ⟩∼Nbb. On the other hand, the translocation velocityvfollows a power-law relationship with the polymer chain lengthNasv∼N-1. The mean translocation time also shows an inverse linear relationship with the magnitude of the pulling forceF,⟨τ⟩∼F-1. The power-law relationships discovered in this study contribute to the fundamental understanding of the comb polymer translocation dynamics and to establishing a framework for further investigations in this field.

Monitoring Anesthetic Depth Using the Patient State Index in Electroconvulsive Therapy Improves Seizure Quality.

The determination of anesthetic depth has been used to assess the optimal moment for applying electrical stimuli in electroconvulsive therapy (ECT), as some of the anesthetics used can reduce its effectiveness. In this study, seizure quality was assessed using anesthetic depth measurement with the patient state index (PSI). A prospective experimental study was conducted with a control group, including a sample of 346 stimulations (PSI=134; Control=212) in 51 patients admitted and diagnosed with major depressive disorders. Seizure adequacy variables (seizure time in electroencephalogram [EEG] and motor activity, visual evaluation of the EEG, ECT-EEG parameter rating scale [EEPRS], seizure concordance, central inhibition, automated parameters, and autonomic activation) were assessed using linear mixed-effects models for continuous variables and generalized linear mixed-effects models for dichotomous variables. The PSI group required lower stimulation energy. The use of the PSI was associated with longer seizure time, both motor and electroencephalographic, higher quality of the EEG recording, better seizure concordance, and higher values for the automated parameters of maximum sustained coherence and time to peak coherence. The use of the PSI to measure anesthetic depth may reduce the electrical stimulus charge required and improve seizure quality in ECT modified with propofol.

Virtual Simulation-Based Optimization for Assembly Flow Shop Scheduling Using Migratory Bird Algorithm.

As industrial informatization progresses, virtual simulation technologies are increasingly demonstrating their potential in industrial applications. These systems utilize various sensors to capture real-time factory data, which are then transmitted to servers via communication interfaces to construct corresponding digital models. This integration facilitates tasks such as monitoring and prediction, enabling more accurate and convenient production scheduling and forecasting. This is particularly significant for flexible or mixed-flow production modes. Bionic optimization algorithms have demonstrated strong performance in factory scheduling and operations. Centered around these algorithms, researchers have explored various strategies to enhance efficiency and optimize processes within manufacturing environments.This study introduces an efficient migratory bird optimization algorithm designed to address production scheduling challenges in an assembly shop with mold quantity constraints. The research aims to minimize the maximum completion time in a batch flow mixed assembly flow shop scheduling problem, incorporating variable batch partitioning strategies. A tailored virtual simulation framework supports this objective. The algorithm employs a two-stage encoding mechanism for batch partitioning and sequencing, adapted to the unique constraints of each production stage. To enhance the search performance of the neighborhood structure, the study identifies and analyzes optimization strategies for batch partitioning and sequencing, and incorporates an adaptive neighborhood structure adjustment strategy. A competition mechanism is also designed to enhance the algorithm's optimization efficiency. Simulation experiments of varying scales demonstrate the effectiveness of the variable batch partitioning strategy, showing a 5-6% improvement over equal batch strategies. Results across different scales and parameters confirm the robustness of the algorithm.

Two-agent proportionate flowshop scheduling with deadlines: polynomial-time optimization algorithms

AbstractVolatility in the supply chain of critical products, notably the vaccine shortage during the pandemic, influences livelihoods and may lead to significant delays and long waiting times. Considering the capital- and time-intensive nature of capacity expansion plans, strategic operational production decisions are required best to address the supply-demand mismatches given the limited production resources. This research article investigates the production scenarios where the demand of one agent must be completed within a specified due date, hereinafter referred to as the deadline, while minimizing the maximum or total completion time of another agent's demand. For this purpose, the Two-Agent Proportionate Flowshop Scheduling Problem with deadlines is introduced. Two polynomial-time optimization algorithms are developed to solve these optimization problems. This study will serve as a basis for further developing this practical yet understudied scheduling problem.

Scheduling a two-stage assembly problem with separated setup time to minimise total tardiness

The two-stage assembly scheduling problem (TASP) is widely existed in our real-life. Minimising the total tardiness of all jobs is important for increasing customers’ satisfaction. Although a few researchers have focused on such optimisation criterion, the performance of proposed algorithms depends on four or more fine-tuned parameters. This work focuses on TASPs with separated setup time. There are multiple and one machine at stages one and two, respectively. A branch and bound algorithm and a variable neighbourhood search (VNS) are proposed to minimise the total tardiness of all jobs. Both of them contain only one parameter, i.e. maximum CPU time, which needs no turning. In order to guide the search of the branch and bound algorithm, lower and upper bounds and dominance rules are proposed. In order to avoid VNS falling into local optima, three neighbourhoods and a novel shaking subroutine are proposed. Experimental results show that the proposed VNS outperforms all existing algorithms on thousands of large scale TASPs generated randomly. Branch and bound algorithm can not only ensure the optimal schedule for small scale TASPs but also enhance the search ability of the proposed VNS to some extent.

Data-driven discovery of dynamics from time-resolved coherent scattering

Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.

The Influence of Anomalous Biomass Emissions on ENSO in CESM2

Abstract The influence of biomass burning (BB) aerosols arising from wildfires and agricultural fires on the transient coupled evolution of ENSO is explored in CESM2. For both El Niño and La Niña, two 20-member ensembles are generated from initial states that are predisposed to evolve into ENSO events. For each ENSO phase, one ensemble is forced with the observed BB emissions during satellite-era ENSO events while the other is forced with a climatological annual cycle, with the responses to anomalous BB emissions estimated from inter-ensemble differences. It is found that the regional responses to anomalous BB emissions occur mainly during boreal fall, which is also the time of the climatological seasonal maximum in emissions. Transient responses are identified in precipitation, clouds, and radiation in both the tropics and extratropics. At the onset of El Niño, these include an increase precipitation in the northern branch of the ITCZ and an enhancement of cloud albedo and amount across the Maritime Continent and eastern subtropical Pacific Ocean. Additional responses are identified through the course of El Niño and successive La Niña events, the net effect of which is to strengthen SST anomalies in the eastern Pacific Ocean during El Niño and warm the tropical Pacific Ocean during La Niña. These responses improve simulation of ENSO power, diversity, and asymmetry in CESM2.

Optimizing Dry Port Locations: A Comparative Analysis of Deterministic, Stochastic, and Robust Models

The increasing international maritime transportation of freight has caused a huge operation in important ports, which are often established near cities and therefore do not have enough room for expanding the operations. This causes long delays and poor productivity in ports. The construction of dry ports has been proposed as a suitable solution to mitigate the problems. A dry port is an inland intermodal terminal directly connected to a seaport by road or rail, where importers and exporters can clear their cargo through customs and handle other shipping formalities. Selecting the right place for the establishment of dry ports has a significant impact on the time and costs of transportation in the network, as well as environmental consequences. In this research, to determine the optimal location of dry ports among the potential locations, three mathematical models as deterministic, stochastic, and robust with three objectives of minimizing the maximum transportation time in the entire network, environmental pollutants, and transportation costs are presented, which simultaneously specify the required number of dry ports in the network and the optimal place to build them. The presented models have been implemented for data related to 30 million bills of lading in a 10-year period in Iran with four major ports and 400 cities. Both deterministic and non-deterministic approaches reveal a preference not to build a dry port in the first optimal solution. However, in the following desirable answers, different answers have been suggested. According to the sensitivity analysis performed by making changes in the fuel price situation or the existing rail network, the model introduces several potential points for the construction of dry ports. The results of this model show that, with the construction of dry ports, despite the increase in overall costs of the network because of the construction of dry ports, operation, and storage in them, the amount of greenhouse gas emissions will decrease significantly.

Investigating the Effects of Practice Time on Student Achievement Considering Variations in Demographics across Various Chemistry Topics

This study examined the relationship between the time students spend on practice problems and their performance on exams in various chemistry topics, considering their demographics. The researchers divided 91 general chemistry students into three groups based on the time allotted for solving intervention questions: Minimum, Average, and Maximum. The results showed that the Minimum and Average time groups benefited almost equally, but the performance of the Maximum time group declined. This suggests that, while additional practice is beneficial, there could be an optimal amount of time that students should spend on each question. Spending too much time on a single question can lead to mental and emotional fatigue, resulting in a decline in performance. Additionally, the researchers noted variations in performance across different chemistry topics and student groups, and they examined the relationship between student demographics and their problem-solving performances. The study provides recommendations for educators, testing services, and online homework systems to improve the effectiveness of chemistry instruction, highlighting the importance of finding the right balance between practice time and student engagement, and suggesting that a uniform approach to practice problems may not be ideal for every student.

Backcalculation of elastic moduli of elements of layered halfspace on the basis of dynamic deformation analysis (by the example of highways)

The paper is devoted to the improvement of the method of inverse calculation of elasticity moduli of layers of motorway pavements in the dynamic setting, which involves the analysis of deformation characteristics in the time domain. To solve this problem, the mathematical model of a layered half-space is adapted to the calculation of amplitude-time characteristics of deformation on the surface of the layered medium, and the construction of the corresponding bowls of maximum values of vertical displacements. Adjustment of the calculated values of vertical displacements with respect to the registered experimental displacements in the field was performed. The correspondence between the final values of maximum vertical displacements, amplitude and time characteristics on the surface of the layered medium, and the shapes and areas of dynamic hysteresis loops on the surface of the investigated medium, achieved by adjusting the calculated characteristics relative to the experimental ones, has been demonstrated. For the first time in solving the problem of determining the mechanical parameters of a layered medium, it was proposed to consider dynamic hysteresis loops as one of the parameters characterising them and the correspondence of their calculated and experimental areas as a criterion of the adequacy of the achieved result.

In situ determination of soybean leaves nutritional status by portable X-ray fluorescence: An initial approach for data collection and predictive modelling

X-ray fluorescence (XRF) analyses are fast, clean, non-destructive, and compatible with on-field operations, which are some advantages over traditional determinations using coupled plasma optical emission spectroscopy (ICP-OES). The aim of this study was to advance in situ XRF approaches for assessing the nutritional status of soybean leaves (i.e., P, S, K, Ca, Mn, Fe, Cu and Zn). More specifically, we propose a protocol to ensure accuracy of in-field analysis and then evaluate the predictive performance of XRF via different data modelling strategies for macro- and micronutrient determination. Therefore, the XRF sensor dwell time of 60 s and the maximum time of 5 min were determined for the analysis of the leaves after leaf abscission, taking into account the influence of moisture loss on the signal intensity of the lighter elements. Regarding the predictive performance of XRF data for nutrients determination, multiple linear regression (MLR) models resulted in lower root mean square errors (RMSE) for P (433 mg kg−1), S (204 mg kg−1) and K (1957 mg kg−1); Partial least squares regression (PLS) for Ca (519 mg kg−1); and simple linear regression (SLR) for Mn (9 mg kg−1), Fe (18 mg kg−1), Zn (5 mg kg−1). The different modelling strategies exhibited equivalent RMSE for Cu (2 mg kg−1). These prediction errors are within a ±20% range, demonstrating that the in situ protocols developed in this research are useful for predicting the nutrients concentration in soybean leaves. Our study shows the possibility of using the in situ XRF sensor for the rapid and practical nutrients determination in soybean leaves, presenting good potential as a crop diagnosis tool.

Relationship between paraspinal muscle morphology and function in different directions in a healthy Chinese population at different ages: a cross-sectional study

BackgroundParaspinal muscle degeneration occurs with age; however, it is unknown whether strength and endurance change with muscle cross-sectional area (CSA) and fatty infiltration (FI) parameters in Chinese healthy individuals.MethodsA total of 94 asymptomatic Chinese volunteers were enrolled in this study. The participants were divided into three groups: young (20–39 years old, n = 27), middle-aged (40–59 years old, n = 49), and elderly (≥ 60 years old, n = 18). CSA and FI of the psoas (PS), quadratus lumborum (QL), multifidus (MF), and erector spinae (ES) were measured using magnetic resonance imaging. The Bionix Sim3 Pro was used to evaluate the maximum isometric torque and the Ito test to evaluate endurance.ResultsThe CSA of the PS and ES in the elderly group was smaller than those in the other groups, while the CSA of QL in the young group was larger than that in the other groups. There were differences in the MF and ES FI among the three groups. The maximum isometric torque and endurance test time decreased with increasing age; however, these differences were not statistically significant. Maximum isometric torque positively correlated with the average paraspinal muscle CSA and negatively correlated with the torque and FI of the MF and ES muscles. The endurance test was found to be positively correlated with the FCSA of the MF and to be negatively correlated with the FI of the MF and ES. PS and QL can predict the maximum isometric torque, and MF and PS can predict the endurance time.ConclusionMF and ES showed earlier degeneration than PS and QL. MF is the first paraspinal muscle to undergo functional area atrophy, and it plays an important role in the endurance test. The maximum moment of equal length in all directions of the lumbar spine is not completely symmetrical, but it is correlated with the imaging parameters of the paraspinal muscles. QL and PS were more activated in the lumbar activity.Trial registrationThe study was registered in Chinese Clinical Trial Registry and the registration number is ChiCTR2000039073 on 15/10/2020 (https://www.chictr.org.cn/showproj.html?proj=62785). Ethical Approval was obtained from the Peking University Third Hospital Medical Science Research Ethics Committee (IRB00006761-M2020305).

BioMOF@PAN Mixed Matrix Membranes as Fast and Efficient Adsorbing Materials for Multiple Heavy Metals' Removal.

Heavy metal ions are a common source of water pollution. In this study, two novel membranes with biobased metal-organic frameworks (BioMOFs) embedded in a polyacrylonitrile matrix with tailored porosity were prepared via nonsolvent induced phase separation methods and designed to efficiently adsorb heavy metal ions from oligomineral water. Under optimized preparation conditions, stable membranes with high MOF loading up to 50 wt % and a cocontinuous sponge-like morphology and a high water permeability of 50-60 L m-2 h-1 bar-1 were obtained. The tortuous flow path in combination with a low water flow rate guarantees maximum contact time between the fluid and the MOFs, and thus a high heavy metal capture efficiency in a single pass. The performances of these BioMOF@PAN membranes were investigated in the dynamic regime for the simultaneous removal of Pb2+, Cd2+, and Hg2+ heavy metals from aqueous environments in the presence of common interfering ions. The new composite adsorbing membranes are capable of reducing the concentration of heavy metal pollutants in a single pass and at much higher efficiency than previously reported membranes. The enhanced performance of the mixed matrix membranes is attributed to the presence of multiple recognition sites which densely decorate the BioMOF channels: (i) the thioether groups, deriving from the S-methyl-l-cysteine and (S)-methionine amino acid residues, able to recognize and capture Pb2+ and Hg2+ ions and (ii) the oxygen atoms of the oxamate moieties, which preferentially interact with Cd2+ ions, as revealed by single crystal X-ray diffraction. The flexibility of the pore environments allows these sites to work synergically for the simultaneous capture of different metal ions. The stability of the membranes for a potential regeneration process, a key-factor for the effective feasibility of the process in real life applications, was also evaluated and confirmed less than 1% capacity loss in each cycle.

Ocular changes in Alzheimer’s disease

Alzheimer&amp;rsquo;s disease (AD) is an incurable, irreversible condition that bears a large global burden. An early diagnosis is imperative to allow the maximum time for treatment interventions. The eye&amp;rsquo;s involvement has been identified in AD, making it a promising option for a non-invasive early diagnostic and screening tool. The pathology of AD observed in the brain such as amyloid beta (A&amp;beta;) plaques and phosphorylated tau have been mirrored in the retina. There is also evidence that AD pathology in the retina precedes the onset of symptoms and the deposition of plaques in the brain. It is estimated that A&amp;beta; burden results in structural changes in the eye such as degeneration of the retinal nerve fiber layer, thinning of the macula, reduced blood flow rate, and thinning of the choroid. These structural changes can be observed using non-invasive imaging techniques such as optical coherence tomography. This review examines the existing literature on AD pathology in the retina and discusses the possibility of using retinal imaging techniques to screen for AD.

Sustainability of shear stress conditioning in endothelial colony-forming cells compared to human aortic endothelial cells to underline suitability for tissue-engineered vascular grafts

The endothelialization of cardiovascular implants is supposed to improve the long-term patency of these implants. In addition, in previous studies, it has been shown, that the conditioning of endothelial cells by dynamic cultivation leads to the expression of an anti-thrombogenic phenotype. For the creation of a tissue-engineered vascular graft (TEVG), these two strategies were combined to achieve optimal hemocompatibility. In a clinical setup, this would require the transfer of the already endothelialized construct from the conditioning bioreactor to the patient. Therefore, the reversibility of the dynamic conditioning of the endothelial cells with arterial-like high shear stress (20 dyn/cm2) was investigated to define the timeframe (tested in a range of up to 24 h) for the perseverance of dynamically induced phenotypical changes. Two types of endothelial cells were compared: endothelial colony-forming cells (ECFCs) and human aortic endothelial cells (HAECs). The results showed that ECFCs respond far more sensitively and rapidly to flow than HAECs. The resulting cell alignment and increased protein expression of KLF-2, Notch-4, Thrombomodulin, Tie2 and eNOS monomer was paralleled by increased eNOS and unaltered KLF-2 mRNA levels even under stopped-flow conditions. VCAM-1 mRNA and protein expression was downregulated under flow and did not recover under stopped flow. From these time kinetic results, we concluded, that the maximum time gap between the TEVG cultivated with autologous ECFCs in future reactor cultivations and the transfer to the potential TEVG recipient should be limited to ∼6 h.