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The presented study focuses on a numerical simulation of the transport phenomena inside a solid oxide fuel cell anode. The classical mathematical model leads to a notable discrepancy between measured and predicted overpotentials. One of the possible reasons is the assumption of the constant values of electrochemical reaction charge transfer coefficients. A modified formulation of the problem includes data-driven correction of reaction charge transfer coefficients in the electrochemical reaction model. Here we show a dedicated computational scheme in which an artificial neural network updates the charge transfer coefficients depending on the operational conditions and the available datasets. The neural network was trained on twelve experimental data points of an anode's polarization curve obtained from the literature. The training set contained data for the anode operating in two different temperatures - 800 °C and 900 °C. The test set contained additional six data points for an anode operating at 1000 °C. Charge transfer coefficients were proposed by the Artificial Neural Network as a functional relation of the temperature and withdrawn current. The results of the predictions are juxtaposed with the experimental data from the literature. It was shown that an Artificial Neural Network could improve an electrochemical reaction model in Solid Oxide Fuel Cell modeling.

With the continuous development of artillery, the disadvantages of hydraulic recoil brakes gradually appear. At the same time, the appearance of high-performance NdFeB permanent magnet makes it possible to apply electromagnetic braking technology to recoil mechanism. In this paper, prototype tests of a certain artillery were carried out to verify the feasibility of the electromagnetic brake (EMB) and obtain the electromagnetic braking force. Due to the brittleness of NdFeB, in order to eliminate the worry about the safety of EMB, SHPB experiments of NdFeB were carried out. Then, based on the assumption of uniform crack distribution, the law of crack propagation and damage accumulation was described theoretically, and the damage constitutive model suitable for brittle materials was proposed by combining the Zhu-Wang-Tang (ZWT) equation. Finally, the numerical simulation model of the artillery prototype was established and through calculation, the dynamic mechanical characteristics of NdFeB in the prototype were analyzed. The calculation results show that the strength of NdFeB can meet the requirements of the use in the working process. From the perspective of damage factor, the damage value of the permanent magnet on the far right is larger, and the damage value of the inner ring gradually decreases to the outer ring.

In the fields of oceanography, hydrodynamics, and marine engineering, many mathematicians and physicists are interested in Burgers-type equations to show the different dynamics of nonlinear wave phenomena, one of which is a (3+1)-dimensional Burgers system that is currently being studied. In this paper, we apply two different analytical methods, namely the generalized Kudryashov (GK) method, and the generalized exponential rational function method, to derive abundant novel analytic exact solitary wave solutions, including multi-wave solitons, multi-wave peakon solitons, kink-wave profiles, stripe solitons, wave-wave interaction profiles, and periodic oscillating wave profiles for a (3+1)-dimensional Burgers system with the assistance of symbolic computation. By employing the generalized Kudryashov method, we obtain some new families of exact solitary wave solutions for the Burgers system. Further, we applied the generalized exponential rational function method to obtain a large number of soliton solutions in the forms of trigonometric and hyperbolic function solutions, exponential rational function solutions, periodic breather-wave soliton solutions, dark and bright solitons, singular periodic oscillating wave soliton solutions, and complex multi-wave solutions under various family cases. Based on soft computing via Wolfram Mathematica, all the newly established solutions are verified by back substituting them into the considered Burgers system. Eventually, the dynamical behaviors of some established results are exhibited graphically through three - and two-dimensional wave profiles via numerical simulation.

This work concentrates on the issue of leader-following bipartite synchronization of multiple memristive neural networks with Markovian jump topology. In contrast to conventional coupled neural network systems, the coupled neural network model under consideration possesses both cooperative and competitive connections among neuron nodes. Specifically, the interaction between neighbors' nodes is described by a signed graph, in which a positive weight represents an alliance relationship between two neuron nodes while a negative weight represents an adversarial relationship between two neuron nodes. By designing a pinning discontinuous controller that makes full use of the mode information, some effective criteria that ensure the stability of bipartite synchronization error states are obtained. All network nodes can synchronize the target node state bipartitely. Finally, two simulation examples are provided to demonstrate the viability of the suggested bipartite synchronization control approach.

Range of motion (ROM) and prevention of notching remain a challenge for reverse shoulder arthroplasty (RSA). Both may be affected by the morphology of the scapula. The purpose of this study was to define anteroinferior (a) and posteroinferior (p) relevant scapular neck offset (RSNO) and to examine the hypothesis that pRSNO is significantly smaller than aRSNO, and influences rigid body motion (RBM). Adapting glenosphere implantation strategies may therefore be of value.In this computer model study, we used deidentified computed tomographic scans of 22 patients (11 male and 11 female; mean age: 72.9 years) with massive cuff tears without joint space narrowing. Eight RSA glenoid configurations were tested with a constant neck-shaft angle (145°). Two baseplate types (25 mm; 25 + 3 mm lateralized) and 4 glenospheres (GS) (36 mm; 36 +2 mm of eccentricity; 39 mm; 39 + 3 mm) were used. RSNO was defined as the standardized measurement of the horizontal distance from the inferior extent of the GS to the bony margin of the scapula after baseplate positioning (flush to inferior glenoid extent; neutral position: 0° inclination and 0° version-both software computed).There was a highly significant difference between pRSNO and aRSNO for both genders (P .001). pRSNO was always smaller than aRSNO. pRSNO was strongly correlated with external rotation (ERO: 0.84) and extension (EXT: 0.74) and moderately correlated with global ROM (GROM: 0.68). There was a moderately strong correlation between aRSNO and internal rotation (IRO: 0.69). pRSNO was strongly correlated with aRSNO, EXT, ERO, IRO, adduction (ADD) and GROM (0.82, 0.72, 0,8, 0.71, 0.82, 0.76) in female patients and with EXT and ERO (0.82, 0.89) in male patients. The median pRSNO allowing for at least 45° ERO and 40° EXT was 14.2 mm for men and 13.8 mm for women. For all patients and models, pRSNO ≥14 mm increased EXT, ERO, and GROM significantly compared with pRSNO14 mm (P .001). The combination of lateralization and inferior overhang (eccentricity) led to the most significant increase of pRSNO for each GS size (P .001).This is one of the first RSA modeling studies evaluating nonarthritic glenoids of both genders. The lateral scapular extent to glenoid relationship is asymmetric. pRSNO is always smaller than aRSNO for both genders and was a critical variable for EXT and ERO, demonstrating additional strong correlation with aRSNO, IRO, ADD, and GROM in female patients. pRSNO ≥14 mm was a safe value to prevent friction-type impingement. Combining increased glenosphere size, lateralization, and inferior overhang gives the best results in this computer-simulated setting.

This paper deals with the closed-form solutions to the family of Boussinesq-like equations with the effect of spatio-temporal dispersion. The sine-Gordon expansion and the hyperbolic function approaches are efficiently applied to the family of Boussinesq-like equations to explore novel solitary, kink, anti-kink, combo, and singular-periodic wave solutions. The attained solutions are expressed by the trigonometric and hyperbolic functions including tan , sec , cot , csc , tanh , sech , coth , csch , and of their combination. In addition, the mentioned two approaches are applied to the aforesaid models in the sense of Atangana conformable derivative or Beta derivative to attain new wave solutions. Three-dimensional and two-dimensional graphs of some of the obtained novel solutions satisfying the corresponding equations of interest are provided to understand the underlying mechanisms of the stated family. For the bright wave solutions in terms of Atangana’s conformable derivative, the amplitudes of the bright wave gradually decrease, but the smoothness increases when the fractional parameters α and β increase. On the other hand, the periodicity increases for the periodic wave solutions. The attained new wave solutions can motivate applied scientists for engineering their ideas to an optimal level and they can be used for the validation of numerical simulation results in the propagation of waves in shallow water and other nonlinear cases. The performed approaches are found to be simple and efficient enough to estimate the solutions attained in the study and can be used to solve various classes of nonlinear partial differential equations arising in mathematical physics and engineering.

This paper presents a high-order low-pass filter for the equivalent-input-disturbance (EID) approach to improving the disturbance-rejection performance. The configuration characteristic of the presented filter clearly explains the reason why the disturbance-rejection performance is improved and provides a guideline to design it. Using the presented filter to replace the conventional filter derives a high-order EID (HEID) approach. It is easy to apply the small-gain theorem in deriving stability conditions of the HEID-based control system. Moreover, the presented filter is proved to be better than the conventional one. Finally, a comparison shows the validity and superiority of the presented method. And a simulation result shows that the HEID approach is easily extended in a multiple-input, multiple-output system even with effects of a white noise and parameter uncertainties.

Forward and backward reaching inverse kinematics (FABRIK) is an efficient two-stage iterative solver for inverse kinematics of spherical-joint manipulator without the calculation of Jacobian matrix. Based on FABRIK, this paper presents an incremental control scheme for a free-floating space manipulator consists of revolute joints and rigid links with the consideration of joint constraints and dynamic coupling effect. Due to the characteristics of FABRIK, it can induce large angular movements on specific joints. Apart from that, FABRIK maps three dimensional (3D) problem into two dimensional (2D) problem by a simple geometric projection. This operation can cause infinite loops in some cases. In order to overcome these issues and apply FABRIK on space manipulators, an increments allocation method is developed to constrain the angular movements as well as to re-orient the end-effector. The manipulator is re-positioned based on the momentum conservation law. Instead of pure target position tracking, the orientation control of the end-effector is also considered. Numerical simulation is performed to testify and demonstrate the effectiveness and reliability of the proposed incremental control approach.

Several recent multi-compartment diffusion MRI investigations and modeling strategies have utilized the orientationally-averaged, or spherical mean, diffusion-weighted signal to study tissue microstructure of the central nervous system. Most experimental designs sample a large number of diffusion weighted directions in order to calculate the spherical mean signal, however, sampling a subset of these directions may increase scanning efficiency and enable either a decrease in scan time or the ability to sample more diffusion weightings. Here, we aim to determine the minimum number of gradient directions needed for a robust measurement of the spherical mean signal. We used computer simulations to characterize the variation of the measured spherical mean signal as a function of the number of gradient directions, while also investigating the effects of diffusion weighting (b-value), signal-to-noise ratio (SNR), available hardware, and spherical mean fitting strategy. We then utilize empirically acquired data in the brain and spinal cord to validate simulations, showing experimental results are in good agreement with simulations. We summarize these results by providing an intuitive lookup table to facilitate the determination of the minimal number of sampling directions needed for robust spherical mean measurements, and give recommendations based on SNR and experimental conditions.

The purpose of this article is to tackle with the problem of data-driven robust control of multi-input multi-output discrete-time nonlinear plants under tracking error constraints and output perturbations. Thereby, based upon the concept of dynamic linearization, a novel predefined performance based model-free adaptive fractional-order fast terminal sliding-mode controller is proposed so that the tracking errors can converge and remain within a preassigned neighborhood. The presented approach does solely rely on the real-time input/output data of the process, and the transient response together with the steady-state manner of the errors can be arbitrarily predefined. In the meantime, the closed-loop behavior is investigated by mathematical analysis, and the efficiency of the method is validated through various simulation examples.

A mechanistic model has been applied for the prediction of corrosion rate in multiphase (water–oil-gas) flow regimes. This is due to inadequacies of existing models to adequately address a wide variety of corrosion dependent parameters with special emphasis on multiphase flow parameters. This model takes into account; water cut, volumetric phase fraction of oil and gas, temperature, pressure, other data collected relating to the pipe age, sand particle deposit, CO2 partial pressure, flow velocity, pH, chloride contents, mass transfer coefficients, and hydrogen sulfide. The applied model results show well above 91 percent prediction accuracy, while the prediction error ranges between 0.022 and 0.045 percent. When predicted results compared actual value (experimental data) with DeWaard and Milliams’ model; used in the oil and gas industries, it showed good agreement. It is seen that the resulting model produced an R2 value of 0.91 in comparison to 0.53, the value for Dewaard and Milliams’. It is obvious from numerical simulation that the multiphase flow parameters have major effects on the entire rate of corrosion of oil and gas multiphase flow pipelines. Finally, this research concluded by showing the developed model performance and comparison was made with numerical results. It is expected that the outcome of this research will be of significant help for the strengthening of the application of mechanistic models for predicting corrosion rate. This model will be useful for oil and gas industries worldwide for accurate prediction of corrosion rate.

Foam diversion acidizing can effectively solve the problem of acid distribution with severe heterogeneity between and within layers. Based on the foam diversion principle, the gas trap theory, and volume conservation principle, the foam slug diversion acidizing model was established and solved considering the change of bottomhole temperature and deviation factor of foam. The simulation results show that the change of temperature has a great influence on the diversion effect at the initial stage of injection, but a small influence at the middle and late stage. The effect of temperature on the highly permeable layer is greater than that of temperature on the low permeability layer. The deviation factor of foam is mainly controlled by temperature at the initial stage, and by pressure at the middle and late stage, and the whole process shows a downward trend, which has little influence on the diversion effect. The quasi-skin factor of gas trap is the most important parameter that influences the effect of foam diversion. The water saturation of the low permeability layer rises faster than that of the high permeability layer, and the effect of diversion is obvious. The research results have a strong guiding significance for foam diversion acidizing.

The ability to effectively and efficiently navigate an electronic health record (EHR) to complete an inpatient prospective drug review is a vital skill that entry level practitioners must possess. The purpose of this study was to evaluate the impact of an EHR-based inpatient order verification simulation on students' perceived preparedness and confidence when reviewing the accuracy of an inpatient medication order and providing a verbal recommendation to a health care prescriber. This was evaluated by comparing survey scores pre- and post-simulation.An educational series utilizing EHR technology was designed to simulate the sequence of tasks an inpatient pharmacist completes when processing an inpatient mediation order. This includes performing a prospective drug utilization review to identify medication-related problems, forming alternative recommendations, and verbalizing recommendations to prescriber.A total of 89 students across two cohorts (99%) completed the pre-survey; 31 students (34%) completed the post-survey. Comparison of pre- and post-survey scores resulted in significant findings for all categories: increased perceived preparedness, efficiency, and accuracy when processing or reviewing a medication order (P .001); increased confidence when efficiently and effectively utilizing EHR technology (P .001); increased confidence when efficiently (P .001) and effectively (P .001) providing a verbal recommendation to a health care prescriber.Incorporating a series of EHR-based activities simulating medication order verification and prescriber communication into the doctor of pharmacy curriculum positively impacts student perceived preparedness and confidence.

View classification is a key step toward building a fully automated system for interpretation of echocardiograms. However, compared with adult echocardiograms, creating a view classification model for pediatric echocardiograms poses additional challenges, such as greater variation in anatomy, structure size, and views. The aim of this study was to develop a computer vision model to autonomously perform view classification on pediatric echocardiographic images.Using a training set of 12,067 echocardiographic images from patients aged 0 to 19 years, a convolutional neural network model was trained to identify 27 preselected standard pediatric echocardiographic views which included anatomic sweeps, color Doppler, and Doppler tracings. A validation set of 6,197 images was used for parameter tuning and model selection. A test set of 9,684 images from 100 different patients was then used to evaluate model accuracy. The model was also evaluated on a per study basis using a second test set consisting of 524 echocardiograms from children with leukemia to identify six preselected views pertinent to cardiac dysfunction surveillance.The model identified the 27 preselected views with 90.3% accuracy. Accuracy was similar across age groups (89.3% for 0-4 years, 90.8% for 4-9 years, 90.0% for 9-14 years, and 91.2% for 14-19 years; P = .12). Examining the view subtypes, accuracy was 78.3% for the cine one location, 90.5% for sweeps with color Doppler, 82.2% for sweeps without color Doppler, and 91.1% for Doppler tracings. Among the leukemia cohort, the model identified the six preselected views on a per study basis with a positive predictive value of 98.7% to 99.2% and sensitivity of 76.9% to 94.8%.A convolutional neural network model was constructed for view classification of pediatric echocardiograms that was accurate across the spectrum of ages and view types. This work lays the foundation for automated quantitative analysis and diagnostic support to promote efficient, accurate, and scalable analysis of pediatric echocardiograms.

Asymmetric tibial insert design is expected to restore normal knee kinematics better than symmetric design. A tri-condylar implant has asymmetric and symmetric tibial inserts with a ball-and-socket joint to replace the post-cam mechanism. The purpose of this study was to compare the knee kinematics of the two designs and to measure tibiofemoral contact stresses, including that of the ball-and-socket joint.Using a computer simulation, the anteroposterior position and axial rotation of the femoral component were simulated during a weight-bearing deep knee bend for six validated models. Contact forces were simultaneously simulated in the medial, lateral, and ball-and-socket compartments. The relative position and the magnitude and direction of each contact force were applied to aforce/displacement control knee simulator. The contact stresses were measured individually using a pressure sensor.The asymmetric tibial insert demonstrated a more posterior position of the femoral component in the lateral compartment during the entire range of motion and greater external rotation of the femoral component, compared to the symmetrical tibial insert. The mean peak contact stress of the medial and lateral compartments was 9 Mpa, with no significant differences between the two designs except at 0°. The contact stress of the ball-and-socket joint was 5 MPa.Asymmetry of the tibial insert shows significant kinematic difference and has little influence on the peak contact stress, which is considerably lower than the yield strength of polyethylene. The asymmetric tibial insert can lead to clinical benefits owing to its kinematic and kinetic properties.

This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is proposed to estimate both the pseudo partial derivative and disturbances. Consequently, an input triggering rule for single-input single-output systems is provided by incorporating the estimated disturbances, the quantization error bound and tracking errors. Resorting to the Lyapunov method, sufficient conditions for synthesized error systems to be uniformly ultimately bounded are presented. The validity of the proposed scheme is demonstrated via a simulation example.

Although microdialysis is a common in vivo sampling technique, a detailed characterization of the performance of a microdialysis probe used for sampling ethanol molecules has not been conducted. In this work, experimental and computational investigations were carried out to quantitatively study ethanol diffusion characteristics for home-made and commercially available probes. Probe efficiency, i.e. recovery rate (defined as the ethanol concentration in the dialysate to that in the external medium surrounding the probe) was used to characterize the performance. The recovery rate was measured at different perfusion flow rates (0.1, 0.2, 0.5, 1, 1.5, 2 μL/min) and external ethanol concentrations (1, 2.5, 5, 10, 20 mM) with controlled environmental conditions. Effect of temperature was also investigated at 19, 37 and 47 °C. The results show that reducing the flow rate from 2 to 0.1 μL/min at least triples the recovery rate for the home-made probes, and it remains nearly unchanged when varying external ethanol concentration. The performance for two commercial microdialysis probes with different membrane materials and configurations were also determined and have similar recovery rates. Through computational modeling, the diffusion coefficient of ethanol in the semipermeable membrane of the home-made probe was determined by fitting the experimental data, and it was found to be 9 × 10

The concept of individual admixture (IA) assumes that the genome of individuals is composed of alleles inherited from $K$ ancestral populations. Each copy of each allele has the same chance $q_k$ to originate from population $k$, and together with the allele frequencies $p$ in all populations at all $M$ markers, comprises the admixture model. Here, we assume a supervised scheme, i.e.\ allele frequencies $p$ are given through a reference database of size $N$, and $q$ is estimated via maximum likelihood for a single sample. We study laws of large numbers and central limit theorems describing effects of finiteness of both, $M$ and $N$, on the estimate of $q$. We recall results for the effect of finite $M$, and provide a central limit theorem for the effect of finite $N$, introduce a new way to express the uncertainty in estimates in standard barplots, give simulation results, and discuss applications in forensic genetics., 25 pages, 7 figures

This study deals with the problem of the population shrinking in habitats affected by aging and excessive migration outflows. First, a control-oriented population dynamics model was proposed that catches the effect of depopulation. The model also includes the effect of spatial interaction-driven migration flows on population size. The resulting model is a non-homogeneous ordinary differential equation. It includes such phenomena that are important from the control point of view, such as the influence of migration costs on population dynamics, the impact of aging on population size, or the effect of the habitats' carrying capacity on migration flows. Based on the model, controllability conditions are formulated and a control strategy was developed that is meant to avoid the depopulation of the habitat. The control method acts on the migration costs to achieve the control goal and requires only population size measurements. Simulation measurements are presented in the paper to show the effectiveness of the proposed modeling and control methods.

Decision making in clinical practice is only possible if we are able to understand the limits of our own knowledge and manage the associated uncertainty. Tolerance of ambiguity is therefore an important attribute for trainee pharmacists and medical students to develop. This study aimed to explore the impact of an interprofessional simulation on the tolerance of ambiguity of trainee pharmacists and medical students.Trainee pharmacists and final year medical students participated in interprofessional simulation in two regions of Scotland. Participants completed pre- and post-session tolerance of ambiguity questionnaires. Analysis included differences between the pre-session scores of the two groups; the trainee pharmacists' pre- and post-session scores; and the medical students' pre- and post-session scores.A total of 15 trainee pharmacists and 15 medical students participated. Baseline tolerance of ambiguity was slightly higher in medical students than trainee pharmacists (56.9 vs. 52.6), but the study was insufficiently powered to detect whether this was a true difference (P = .21). Trainee pharmacists showed a statistically significant increase in self-reported tolerance of ambiguity (52.6 to 60.8, P = .004), but medical students did not (56.9 to 63.8, P = .04).Trainee pharmacists' tolerance of ambiguity was improved following participation in an interprofessional simulation. Further research could establish whether the improvements differ between professional groups, and explore the reasons why tolerance of ambiguity may be affected by interprofessional simulation.

In economic investment, the role of forecasting is very important because in an economic project, the investor must carefully examine the dimensions of the work such that one of the most important and perhaps the main factor of a future investor and an economic enterprise is the work done by Costs and revenues are determined. Due to the fact that the volatility of iron ore price is affected by various factors, so it is not possible to determine a simple and general function to predict its price. There are several methods for predicting price, but the most appropriate of these is a method that examines variables in a nonlinear and dynamic manner that is closer to reality. Therefore, in this research, an improved and optimized neural network is proposed to facilitate this task. The idea is to employ a developed version of Search and Rescue optimization algorithm to enhance the training ability of the neural network to present an efficient forecasting tool for iron ore price volatilities. Different variables are used for the method verification and its results are compared with basic neural network, particle swarm optimization-based, Intelligent Integrated Optimizer, Genetic Neural Network to show its superiority. Simulation results demonstrate that by the proposed method has a satisfying and better fitting with the data compared with the other methods.

Introduction: Recent advancements in surgical technology, reduced working hours, and training opportunities exacerbated by the COVID-19 pandemic have led to an increase in simulation-based training. Furthermore, a rise in endovascular procedures has led to a requirement for high-fidelity simulators that offer comprehensive feedback. This review aims to identify vascular surgery simulation models and assess their validity and levels of effectiveness (LoE) for each model in order to successfully implement them into current training curricula. Methods: PubMed and EMBASE were searched on January 1, 2021, for full-text English studies on vascular surgery simulators. Eligible articles were given validity ratings based on Messick's modern concept of validity alongside an LoE score according to McGaghie's translational outcomes. Results: Overall 76 eligible articles validated 34 vascular surgery simulators and training courses for open and endovascular procedures. High validity ratings were achieved across studies for: content (35), response processes (12), the internal structure (5), relations to other variables (57), and consequences (2). Only seven studies achieved an LoE greater than 3/5. Overall, ANGIO Mentor was the most highly validated and effective simulator and was the only simulator to achieve an LoE of 5/5. Conclusions: Simulation-based training in vascular surgery is a continuously developing field with exciting future prospects, demonstrated by the vast number of models and training courses. To effectively integrate simulation models into current vascular surgery curricula and assessments, there is a need for studies to look at trainee skill retention over a longer period of time. A more detailed discussion on cost-effectiveness is also needed.

This study aims to explore the biomechanical performance of implant-retained finger prostheses of different lengths and diameters designed specifically for Asians under external loads from different directions by the finite element method. According to the metacarpophalangeal stumps (length: 4, 7, 10 mm, diameter: 3.5, 4.5 mm) retained in Asian patients with finger defects, six implant-retained finger prosthesis models of different lengths and diameters were designed in Solid Works 3D, transported to Abaqus, and constructed based on computerized tomography (CT). The finite element simulation of the finger prosthesis structure was performed using HyperMesh. The strain and stress distribution of the finger prosthesis models under three loads were calculated: axial force, lateral force, and lateral force with bending moments. As the load increased, the cancellous bone yielded first, while the dense bone and implant could withstand much higher loads than the cancellous bone. As the implant depth increased, the maximum strain of the cancellous bone increased significantly, but the effect of the implant diameter remained unclear. In addition, the structure could withstand large axial loads but was much less able to withstand both lateral forces and bending moments. The yielding and destruction of the implant-retained finger prostheses designed specifically for Asians are mainly attributed to cancellous bone. As the depth of the implants increases, the maximum strain of cancellous bone rises significantly. In addition, in the treatment of patients with finger defects, this method may be used to analyze and select which implant can withstand stronger stress according to the finger stumps of the patients with finger defects so that the most suitable implant and individual surgical plan for the patient can be designed.

Speaking up with concerns is critical for patient safety. We studied whether witnessing a civil (i.e. polite, respectful) response to speaking up would increase the occurrence of further speaking up by hospital staff members as compared with witnessing a pseudo-civil (i.e. vague and slightly dismissive) or rude response.In this RCT in a single, large academic teaching hospital, a single simulated basic life support scenario was designed to elicit standardised opportunities to speak up. Participants in teams of two or three were randomly assigned to one of three experimental conditions in which the degree of civility in reacting to speaking up was manipulated by an embedded simulated person. Speaking up behaviour was assessed by behaviour coding of the video recordings of the team interactions by applying 10 codes using The Observer XT 14.1. Data were analysed using multilevel modelling.The sample included 125 interprofessional hospital staff members (82 [66%] women, 43 [34%] men). Participants were more likely to speak up when they felt psychologically safe (γ=0.47; standard error [se]=0.19; 95% confidence interval [CI], 0.09-0.85; P=0.017). Participants were more likely to speak up in the rude condition than in the other conditions (γ=0.28; se=0.12; 95% CI, 0.05-0.52; P=0.019). Across conditions, participants spoke up most frequently by structuring inquiry (n=289, 31.52%), proactive (n=240, 26.17%), and reactive (n=148, 16.14%) instruction statements, and gestures (n=139, 15.16%).Our study challenges current assumptions about the interplay of civility and speaking up behaviour in healthcare.

Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.

Accurate trajectory control of wheeled robot is an unavoidable problem in path planning, especially in the environment of large positioning error. In this paper, by designing the reverse trajectory and using martingale method, the accurate execution of the target trajectory is realized, and the error estimation is given. Finally, through numerical simulation, it is shown that when the observation error is 0.5 units, the average trajectory error is about 0.1 units, and the probability of error exceeding 0.347 units is less than 2%.

Biorelevant in vitro release models are valuable analytical tools for oral drug development but often tailored to gastrointestinal conditions in 'average' healthy adults. However, predicting in vivo performance in individual patients whose gastrointestinal conditions do not match those of healthy adults would be of great value for optimizing oral drug therapy for such patients. This study focused on establishing patient-specific in vitro and in silico models to predict the in vivo performance of levodopa extended-release products in Parkinson's disease patients. Current knowledge on gastrointestinal conditions in these patients was incorporated into model development. Relevant in vivo pharmacokinetic data and patient-specific in vitro release data from a novel in vitro test setup were integrated into patient-specific physiologically-based pharmacokinetic models. AUC, c

To develop a novel "bootcamp" simulation-based curriculum to introduce common urologic procedures and to improve readiness for performing them at bedside.Three hundred twenty-five third-year medical students at our single institution participated in a hands-on simulation "boot camp" over a 2-year accrual period. This curriculum was designed to teach basic urologic bedside procedures (ie, Foley catheter placement, Bladder irrigation) to third year medical students prior to the start of their surgical clerkships in a live instructional setting with direct feedback from instructors. The objective aspects of the study consisted of a survey administered to study participants following participation in the simulation boot camp, as well as pre- and post-module assessments.Medical students self-reported knowledge gains following the simulation session (P.001), with 45.8% increase in procedural confidence. Additionally, students reported that the educational intervention was beneficial to their understanding of the subject matter (P.001), were "very likely" to recommend the session to another medical student (P.001), and felt that educational intervention better prepared them for an upcoming national exam (P.001). Finally, the greatest gains were seen for those students who self-reported lower precurriculum knowledge (P.001).Findings from our M3 "boot camp" led to self-reported gains in subject matter knowledge following the hands-on simulation curriculum, indicating that this type of educational intervention can be beneficial in preparing medical students for common bedside procedures.

Hydrodynamics is a general theoretical framework for describing the long-time large-distance behaviors of various macroscopic physical systems, with its equations based on conservation laws such as energy-momentum conservation and charge conservation. Recently there has been significant interest in understanding the implications of angular momentum conservation for a corresponding hydrodynamic theory. In this work, we examine the key conceptual issues for such a theory in the relativistic regime where the orbital and spin components get entangled. We derive the equations for relativistic viscous hydrodynamics with angular momentum through Navier-Stokes type of gradient expansion analysis and find five new transport coefficients for angular momentum diffusion modes.

Both computational fluid dynamics (CFD) and time-resolved, three-dimensional, phase-contrast, magnetic resonance imaging (4D-flow MRI) enable visualization of time-varying blood flow structures and quantification of blood flow in vascular diseases. However, they are totally different. CFD is a method to calculate blood flow by solving the governing equations of fluid mechanics, so the obtained flow field is somewhat virtual. On the other hand, 4D-flow MRI measures blood flow in vivo, thus the flow is real. Recently, with the development and enhancement of computers, medical imaging techniques, and related software, blood flow analysis has become more accessible to clinicians and its usefulness in vascular diseases has been demonstrated. In this review, we have outlined the methods and characteristics of CFD and 4D-flow MRI, respectively. We have discussed the differences in the characteristics between both methods; reviewed the milestones achieved by blood flow analysis in various vascular diseases; and discussed the usefulness, challenges, and limitations of blood flow analysis. We have discussed the difficulties and limitations of current blood flow analysis. We have also discussed our views on future directions.

Theory for liability-scale models of the underlying genetic basis of complex disease provides an important way to interpret, compare and understand results generated from biological studies. In particular, liability models facilitate an understanding and comparison of the relative importance of genetic and environmental risk factors that shape different clinically important disease outcomes, through estimation of the liability-scale heritability (LSH). Increasingly, large-scale biobank studies that link genetic information to electronic health records are becoming available, containing hundreds of disease diagnosis indicators that mostly occur infrequently within the sample. Here, we propose an extension of the existing liability-scale model theory suitable for estimating LSH in biobank studies of low-prevalence disease. In a simulation study, we find that our derived expression yields lower MSE and is less sensitive to prevalence misspecification as compared to previous transformations, for diseases with ≤ 2% population prevalence and LSH of ≤ 0.45, especially if the biobank sample prevalence is less than that of the wider population. Applying our expression to 13 diagnostic outcomes of ≤ 3% prevalence in the UK Biobank study, revealed important differences in LSH obtained from the different theoretical expressions, that impact the conclusions made when comparing LSH across disease outcomes. This demonstrates the importance of careful consideration for estimation and prediction of low prevalence disease outcomes, and facilitates improved inference of the underlying genetic basis of ≤ 2% population prevalence diseases, especially where biobanking sample ascertainment results in a healthier sample population.

Motivational deficit is a core clinical manifestation of depression and a strong predictor of treatment failure. However, the underlying mechanisms, which cannot be accessed through conventional questionnaire-based scoring, remain largely unknown. According to decision theory, apathy could result either from biased subjective estimates (of action costs or outcomes) or from dysfunctional processes (in making decisions or allocating resources).Here, we combined a series of behavioral tasks with computational modeling to elucidate the motivational deficits of 35 patients with unipolar or bipolar depression under various treatments compared with 35 matched healthy control subjects.The most striking feature, which was observed independent of medication across preference tasks (likeability ratings and binary decisions), performance tasks (physical and mental effort exertion), and instrumental learning tasks (updating choices to maximize outcomes), was an elevated sensitivity to effort cost. By contrast, sensitivity to action outcomes (reward and punishment) and task-specific processes were relatively spared.These results highlight effort cost as a critical dimension that might explain multiple behavioral changes in patients with depression. More generally, they validate a test battery for computational phenotyping of motivational states, which could orientate toward specific medication or rehabilitation therapy, and thereby help pave the way for more personalized medicine in psychiatry.

There is limited data available about factors which promote competence with procedures in medical students. Specifically, the relationship between procedural clinical experience and performance on an assessment is unclear. We sought to determine whether a correlation exists between the amount and type of clinical experience with a procedure and student performance on a standardized assessment of that procedure.Faculty performed standardized assessments of third-year medical students on ten procedures using simulation. We prospectively surveyed students about 3 types of experience (performed, observed, and simulated) with these procedures during their clerkships. We then analyzed whether a correlation exists between student experience and their competency assessment scores using Pearson's correlation.Third-year medical students at the University of Kentucky College of Medicine.In 2018 to 2019, 131 students were assessed on procedural competency with 10 failures. One hundred and twenty students (91.6%) completed the clinical experience survey. Correlations between types of experience and competency scores were small to moderate, with only 5 of 40 being significant. We found no correlation between experience having performed a procedure and competency score.Overall, we did not find convincing evidence of a correlation between experience with procedures during clerkships and performance on a competency assessment. This suggests other factors may be contributing to procedural competence, which has implications for how educators should develop procedural competence in students.

The computational simulation of pathological conditions and surgical procedures, for example the removal of cancerous tissue, can contribute crucially to the future of medicine. Especially for brain surgery, these methods can be important, as the ultra-soft tissue controls vital functions of the body. However, the microstructural interactions and their effects on macroscopic material properties remain incompletely understood. Therefore, we investigated the mechanical behaviour of brain tissue under three different deformation modes, axial tension, compression, and semi-confined compression, in different anatomical regions, and for varying axon orientation. In addition, we characterised the underlying microstructure in terms of myelin, cells, glial cells and neuron area fraction, and density. The correlation of these quantities with the material parameters of the anisotropic Ogden model reveals a decrease in shear modulus with increasing myelin area fraction. Strikingly, the tensile shear modulus correlates positively with cell and neuronal area fraction (Spearman's correlation coefficient of rsubs/sub=0.40 and rsubs/sub=0.33), whereas the compressive shear modulus decreases with increasing glial cell area (rsubs/sub=-0.33). Our study finds that tissue non-linearity significantly depends on the myelin area fraction (rsubs/sub=0.47), cell density (rsubs/sub=0.41) and glial cell area (rsubs/sub=0.49). Our results provide an important step towards understanding the micromechanical load transfer that leads to the non-linear macromechanical behaviour of the brain. STATEMENT OF SIGNIFICANCE: Within this article, we investigate the mechanical behaviour of brain tissue under three different deformation modes, in different anatomical regions, and for varying axon orientation. Further, we characterise the underlying microstructure in terms of various constituents. The correlation of these quantities with the material parameters of the anisotropic Ogden model reveals a decrease in shear modulus with increasing myelin area fraction. Strikingly, the tensile shear modulus correlates positively with cell and neuronal area fraction, whereas the compressive shear modulus decreases with increasing glial cell area. Our study finds that tissue non-linearity significantly depends on the myelin area fraction, cell density, and glial cell area. Our results provide an important step towards understanding the micromechanical load transfer that leads to the non-linear macromechanical behaviour of the brain.

This paper focuses on the impedance control for robotic manipulators with time-varying output constraints. High-order control barrier functions (HoCBFs) are firstly proposed for a nonlinear system with high relative-degree time-varying constraints. Then, the HoCBFs are introduced to impedance control for robotic manipulators, where the HoCBFs are employed to avoid the violation of time-varying output constraints in Cartesian space by quadratic program (QP), and the impedance control is designed to achieve compliance for human-robot interaction (HRI). In this way, the desired trajectory within the safety-critical region can be tracked without violating the output constraints due to the controller generated from QP, and the safe HRI can also be achieved because of the usage of impedance control method. Finally, simulation tests are conducted to verify the proposed control design methods.

This paper is concerned with the injection attack estimation for a class of networked control systems with Deny-of-Service (DoS) attack, where unknown signals are injected into the sensor reading and actuator. The main goal is to design an estimator to estimate the injected signals subject to stochastic hidden DoS attack. By introducing a semi-Markov chain, the complex aperiodic sampling behavior caused by DoS attack is first modeled as a stochastic switching system. By using the Lyapunov stability theory and stochastic system analysis method, the σ-error mean square stability (σ-EMSS) conditions of estimation error system are then derived and the state of the attack estimation error system is shown to be uniformly ultimately bounded. Some linear matrix inequalities are proposed to determine the estimation gain matrices. Finally, both simulation and experimental studies on the motor system are conducted, and the effectiveness of the main results are demonstrated.

Background Psychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g., psychosis in bipolar disorder vs. schizophrenia). Computational modeling makes it possible to identify and mathematically differentiate behaviorally unobservable, specific reinforcement learning differences in patients with schizophrenia versus other disorders, likely owing to a higher reliance on prediction error–driven learning associated with basal ganglia and underreliance on explicit value representations associated with orbitofrontal cortex. Methods We used a well-established probabilistic reinforcement learning task to replicate those findings in individuals with schizophrenia both on (n = 120) and off (n = 44) antipsychotic medications and included a patient comparison group of bipolar patients with psychosis (n = 60) and healthy control subjects (n = 72). Results Using accuracy, there was a main effect of group (F3,279 = 7.87, p Conclusions Both medicated and unmedicated patients showed overreliance on prediction error–driven learning as well as significantly higher noise and value-related memory decay, compared with the healthy control subjects and the patients with bipolar disorder. Additionally, the computational model parameters capturing these processes can significantly improve patient/control classification, potentially providing useful diagnosis insight.

To demonstrate improvements in the precision of inverse probability-weighted estimators by use of auxiliary variables, i.e., determinants of the outcome that are independent of treatment, missingness or selection.First with simulated data, and then with public data from the National Health and Nutrition Examination Survey (NHANES), we estimated the mean of a continuous outcome using inverse probability weights to account for informative missingness. We assessed gains in precision resulting from the inclusion of auxiliary variables in the model for the weights. We compared the performance of robust and nonparametric bootstrap variance estimators in this setting.We found that the inclusion of auxiliary variables reduced the empirical variance of inverse probability-weighted estimators. However, that reduction was not captured in standard errors computed using the robust variance estimator, which is widely used in weighted analyses due to the non-independence of weighted observations. In contrast, a nonparametric bootstrap estimator properly captured the precision gain.Epidemiologists can leverage auxiliary data to improve the precision of weighted estimators by using bootstrap variance estimation, or a closed-form variance estimator that properly accounts for the estimation of the weights, in place of the standard robust variance estimator.

Natural fractures, like tectoclases, are essential in the formation of shale gas reservoirs and have been the focus of study for shale gas development. Tectoclases provide most storage space for gas and are largely controlled by the paleo-tectonic stress field in shale reservoirs of the Niutitang Formation, northern Guizhou area, China. An accurate prediction of the development and distribution of tectoclases in the reservoirs is of great significance to exploring and developing shale gas sweet spots in the area. Based on geological structure evolution and fracture characterization, this study is focused on factors that control the fracture development in the Niutitang Formation shale reservoirs in northern Guizhou through characterization and modeling of geomechanisms and tectonic movements. A geomechanical model is formulated for the shale reservoirs against the geological background of the area. On this basis, the fractures are predicted by using the acoustic emission data. Numerical simulation results show that the development and distribution of tectoclase is controlled by fault zones, some of which have no obvious turning points with tectoclase in the middle sections being more developed and fragmented than those at the two ends. Some of these have obvious S-shaped turning points where tectoclases are the most developed and fragmented .

In this paper, we formulate a mixed-integer problem for sparse signal reconstruction and reformulate it as a global optimization problem with a surrogate objective function subject to underdetermined linear equations. We propose a sparse signal reconstruction method based on collaborative neurodynamic optimization with multiple recurrent neural networks for scattered searches and a particle swarm optimization rule for repeated repositioning. We elaborate on experimental results to demonstrate the outperformance of the proposed approach against ten state-of-the-art algorithms for sparse signal reconstruction.

This paper proposes an adaptive dual control with outlier detection that is robust to the occurrence of outliers in uncertain systems. Outliers occasionally exist in system process noise and observation noise, which could cause poor parameter estimation and degraded control performance of uncertain systems. For this reason, we devise an online outlier detection mechanism to filter the outliers so as to enhance the parameter estimation of uncertain systems. The devised mechanism makes decisions on outlier detection via the generated predicted regions where the newly arriving data is expected to locate, and the predicted regions are updated in real-time according to the historical data. The detection mechanism is integrated into the design of adaptive dual control, which is derived based on the bicriterial method. Compared with classical dual control merely considering uncertainty in input and output data stream, we are the first to include the uncontrollable excitations into the structure of dual control to fit practical scenarios, and this inclusion also provides an extensive cover on outliers to be detected. The improved performance of the proposed approach is verified using a mathematical model through one-time simulation and Monte Carlo simulations under different conditions, and we also evaluate our method in the control of fermentation sterilization process for more convincing results.

Background Patients with Fontan circulation may develop heart failure resulting in atrial fibrillation during the late phase. Inotropic effects to ameliorate hemodynamics on the Fontan circulation are not well understood, especially when in atrial fibrillation. This study was performed to determine whether dobutamine therapy in patients with Fontan circulation has limited effects on improving hemodynamics. Methods Lumped computational models (sinus and atrial fibrillation) were employed including biventricular, atriopulmonary connection (APC), and extracardiac total cavopulmonary connection (TCPC) Fontan models. The condition of atrial fibrillation including lack of atrial beat, irregular ventricular contraction, and time-varying elastance for the ventricle was introduced. A different dose of dobutamine was given by varying the elastance of the ventricle, heart rate, and peripheral resistance. Results In all models, the cardiac output decreased by 22.5% to 25.8% in atrial fibrillation. At 10 μg/kg/min of dobutamine in sinus rhythm, the cardiac output increased by 32.3% in the biventricular model but by only 9.2% (p Conclusions In the Fontan circulation, atrial fibrillation itself reduced the cardiac output by approximately 25%, and dobutamine had a limited effect on increasing the cardiac output, especially when in atrial fibrillation. Maintaining sinus rhythm in patients with Fontan circulation is very important.

Hypothesis: Carbon dioxide hydrates are ice-like nonstoichiometric inclusion solid compounds with importance to global climate change, and gas transportation and storage. The thermodynamic and kinetic mechanisms that control carbon dioxide nucleation critically depend on hydrate-water interfacial free energy. Only two independent indirect experiments are available in the literature. Interfacial energies show large uncertainties due to the conditions at which experiments are performed. Under these circumstances, we hypothesize that accurate molecular models for water and carbon dioxide combined with computer simulation tools can offer an alternative but complementary way to estimate interfacial energies at coexistence conditions from a molecular perspective. Calculations: We have evaluated the interfacial free energy of carbon dioxide hydrates at coexistence conditions (three-phase equilibrium or dissociation line) implementing advanced computational methodologies, including the novel Mold Integration methodology. Our calculations are based on the definition of the interfacial free energy, standard statistical thermodynamic techniques, and the use of the most reliable and used molecular models for water (TIP4P/Ice) and carbon dioxide (TraPPE) available in the literature. Findings: We find that simulations provide an interfacial energy value, at coexistence conditions, consistent with the experiments from its thermodynamic definition. Our calculations are reliable since are based on the use of two molecular models that accurately predict: (1) The ice-water interfacial free energy; and (2) the dissociation line of carbon dioxide hydrates. Computer simulation predictions provide alternative but reliable estimates of the carbon dioxide interfacial energy. Our pioneering work demonstrates that is possible to predict interfacial energies of hydrates from a truly computational molecular perspective and opens a new door to the determination of free energies of hydrates., We thank Pedro J. Pérez for the critical reading of the manuscript. We also acknowledge Centro de Supercomputación de Galicia (CESGA, Santiago de Compostela, Spain) and MCIA (Mésocentre de Calcul Intensif Aquitain) of the Universités de Bordeaux and Pau et Pays de l’Adour (France) for providing access to computing facilities. We thank financial support from the Ministerio de Economía, Industria y Competitividad (FIS2017- 89361-C3-1-P), Junta de Andalucía (P20-00363), and Universidad de Huelva (P.O. FEDER UHU-1255522), all three cofinanced by EU FEDER funds. J.A. acknowledges Contrato Predoctoral de Investigación from XIX Plan Propio de Investigación de la Universidad de Huelva and a FPU Grant (Ref. FPU15/03754) from Ministerio de Educación, Cultura y Deporte. J. A., J. M. M., and F. J. B. thankfully acknowledge the computer resources at Magerit and the technical support provided by the Spanish Supercomputing Network (RES) (Project QCM- 2018–2- 0042). Funding for open access charge: Universidad de Huelva / CBUA

The presented control scheme in this paper aims at stabilizing uncertain time-delayed systems requiring all states to change within the preset time-varying constraints. The controller design framework is based on the backstepping method, drastically simplified by the dynamic surface control technique. Meanwhile, the radius basis function neural networks are utilized to deal with the unknown items. To prevent all state variables from violating time-varying predefined regions, we employ the time-varying barrier Lyapunov functions during the backstepping procedure. Moreover, appropriate Lyapunov-Krasovskii functionals are used to cancel the influence of the time-delay terms on the system's stability. Under the presented control laws and Lyapunov analysis, it is proven that constraints on all state variables are not breached, good tracking performance of desired output is achieved, and all signals in the closed-loop systems are bounded. The effectiveness of our control scheme is confirmed by a simulation example.

This paper studies the underwater glider trajectory tracking in currents field. The objective is to ensure that trajectories fit to the straight target track. The underwater glider model is introduced to demonstrate the vehicle dynamic properties. Considering currents disturbance as well as the uncertain status of the glider controlled by complicated roll policies, the trajectory tracking task can be classified into the model-free optimization. Such problem is difficult to solve with mathematical analysis. This work transfers the underwater glider trajectory tracking into a Markov Decision Process by specifying the actions and observations as well as rewards. On this basis, a neural network controls framework called experience breeding actor-critic is proposed to handle the trajectory tracking. The EBAC enhances the explorations to the potentially high reward area. And it steers glider heading meticulously so as to counteract the currents influence. Through simulation results, the EBAC shows a desired performance in controlling the gliders to accurately fit the target track.

In this paper, we are concerned with the multimode function multistability for Cohen-Grossberg neural networks (CGNNs) with mixed time delays. It is introduced the multimode function multistability as well as its specific mathematical expression, which is a generalization of multiple exponential stability, multiple polynomial stability, multiple logarithmic stability, and asymptotic stability. Also, according to the neural network (NN) model and the maximum and minimum values of activation functions, n pairs of upper and lower boundary functions are obtained. Via the locations of the zeros of the n pairs of upper and lower boundary functions, the state space is divided into ∏

In this paper, two kinds of distributed Nash equilibrium seeking strategies based on Kalman filter are proposed in non-cooperative games with incomplete information. In the discrete-time system with process and measurement noises, each player, selfish and only considering its own profit, utilizes the gradient method to maximize the benefit. Since the payoff function is related to all players' states, Kalman filter and leader-following consensus are used to estimate the states in the network. Furthermore, considering the trade-off between strategy precision of Nash equilibrium and communication rate, another Nash equilibrium seeking method is proposed by introducing an event-based scheduler. The convergence of both Nash equilibrium seeking strategies is analyzed based on Lyapunov method. It is proved that both strategies are bounded in the mean square sense. Simulation examples are given to verify the efficiency.

In this article, extensive three-dimensional simulations are conducted for tank-treading (TT) red blood cells (RBCs) in shear flow with different cell viscous properties and flow conditions. Apart from recent numerical studies on TT RBCs, this research considers the uncertainty in cytoplasm viscosity, covers a more complete range of shear flow situations of available experiments, and examines the TT behaviors in more details. Key TT characteristics, including the rotation frequency, deformation index, and inclination angle, are compared with available experimental results of similar shear flow conditions. Fairly good simulation-experiment agreements for these parameters can be obtained by adjusting the membrane viscosity values; however, different rheological relationships between the membrane viscosity and the flow shear rate are noted for these comparisons: shear thinning from the TT frequency, Newtonian from the inclination angle, and shear thickening from the cell deformation. Previous studies claimed a shear-thinning membrane viscosity model based on the TT frequency results; however, such a conclusion seems premature from our results and more carefully designed and better controlled investigations are required for the RBC membrane rheology. In addition, our simulation results reveal complicate RBC TT features and such information could be helpful for a better understanding of in vivo and in vitro RBC dynamics.

Bacterial infections related to medical devices can cause severe problems, whose solution requires in-depth understanding of the interactions between bacteria and surfaces. This work investigates the influence of surface physicochemistry on bacterial attachment and detachment under flow through both empirical and simulation studies. We employed polydimethylsiloxane (PDMS) substrates having different degrees of crosslinking as the model material and the extended Derjaguin − Landau − Verwey − Overbeek model as the simulation method. Experimentally, the different PDMS materials led to similar numbers of attached bacteria, which can be rationalized by the identical energy barriers simulated between bacteria and the different materials. However, different numbers of residual bacteria after detachment were observed, which was suggested by simulation that the detachment process is determined by the interfacial physicochemistry rather than the mechanical property of a material. This finding is further supported by analyzing the bacteria detachment from PDMS substrates from which non-crosslinked polymer chains had been removed: similar numbers of residual bacteria were found on the extracted PDMS substrates. The knowledge gained in this work can facilitate the projection of bacterial colonization on a given surface., Journal of Colloid and Interface Science, 622, ISSN:0021-9797, ISSN:1095-7103