Type I collagen hydrogels have been used successfully as three-dimensional substrates for cell culture and have shown promise as scaffolds for engineered tissues and tumors. A critical step in the ...development of collagen hydrogels as viable tissue mimics is quantitative characterization of hydrogel properties and their correlation with fabrication parameters, which enables hydrogels to be tuned to match specific tissues or fulfill engineering requirements. A significant body of work has been devoted to characterization of collagen I hydrogels; however, due to the breadth of materials and techniques used for characterization, published data are often disjoint and hence their utility to the community is reduced. This review aims to determine the parameter space covered by existing data and identify key gaps in the literature so that future characterization and use of collagen I hydrogels for research can be most efficiently conducted. This review is divided into three sections: (1) relevant fabrication parameters are introduced and several of the most popular methods of controlling and regulating them are described, (2) hydrogel properties most relevant for tissue engineering are presented and discussed along with their characterization techniques, (3) the state of collagen I hydrogel characterization is recapitulated and future directions are proposed. Ultimately, this review can serve as a resource for selection of fabrication parameters and material characterization methodologies in order to increase the usefulness of future collagen-hydrogel-based characterization studies and tissue engineering experiments.
Collagen I hydrogels are commonly used to mimic the extracellular matrix (ECM) for tissue engineering applications. However, the ability to design collagen I hydrogels similar to the properties of ...physiological tissues has been elusive. This is primarily due to the lack of quantitative correlations between multiple fabrication parameters and resulting material properties. This study aims to enable informed design and fabrication of collagen hydrogels in order to reliably and reproducibly mimic a variety of soft tissues. We developed empirical predictive models relating fabrication parameters with material and transport properties. These models were obtained through extensive experimental characterization of these properties, which include compression modulus, pore and fiber diameter, and diffusivity. Fabrication parameters were varied within biologically relevant ranges and included collagen concentration, polymerization pH, and polymerization temperature. The data obtained from this study elucidates previously unknown fabrication-property relationships, while the resulting equations facilitate informed a priori design of collagen hydrogels with prescribed properties. By enabling hydrogel fabrication by design, this study has the potential to greatly enhance the utility and relevance of collagen hydrogels in order to develop physiological tissue microenvironments for a wide range of tissue engineering applications.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Numerous studies have established firmly that particle image velocimetry (PIV) is a robust method for non-invasive, quantitative measurements of fluid velocity, and that when carefully conducted, ...typical measurements can accurately detect displacements in digital images with a resolution well below a single pixel (in some cases well below a hundredth of a pixel). However, to date, these estimates have only been able to provide guidance on the expected error for an average measurement under specific image quality and flow conditions. This paper demonstrates a new method for estimating the uncertainty bounds to within a given confidence interval for a specific, individual measurement. Here, cross-correlation peak ratio, the ratio of primary to secondary peak height, is shown to correlate strongly with the range of observed error values for a given measurement, regardless of flow condition or image quality. This relationship is significantly stronger for phase-only generalized cross-correlation PIV processing, while the standard correlation approach showed weaker performance. Using an analytical model of the relationship derived from synthetic data sets, the uncertainty bounds at a 95% confidence interval are then computed for several artificial and experimental flow fields, and the resulting errors are shown to match closely to the predicted uncertainties. While this method stops short of being able to predict the true error for a given measurement, knowledge of the uncertainty level for a PIV experiment should provide great benefits when applying the results of PIV analysis to engineering design studies and computational fluid dynamics validation efforts. Moreover, this approach is exceptionally simple to implement and requires negligible additional computational cost.
Abstract
This work introduces a method to estimate the uncertainty of the pressure fields reconstructed from particle image velocimetry / particle tracking velocimetry (PIV/PTV) measurements by ...propagating the instantaneous velocity vector uncertainty through the pressure reconstruction. The uncertainty propagations through the calculation and integration of pressure gradients are modelled as linear transformations. The autocorrelation coefficient was modelled and incorporated in the uncertainty estimation to reproduce the effect of the autocorrelation of velocity errors on the reconstructed pressure’s accuracy. The method was first tested on synthetic velocity fields contaminated with varying levels of artificial noise correlated in space, time, or between components. The error analysis shows that the proposed method could predict the spatiotemporal variations of the pressure errors. The estimated pressure uncertainty also captures the effects of the velocity noise level, the autocorrelation, and the different pressure-gradient integration methods, with more than 80% accuracy in most test cases. The method was applied to an experimental vortex ring flow with planar PIV and a laminar pipe flow with volumetric PTV. The error analysis shows that the obtained pressure uncertainty possessed similar spatial and statistical distributions as the pressure errors. The results also indicate that the performance of the proposed uncertainty estimation method depends on the accuracy of the velocity uncertainty. The proposed uncertainty estimation method exhibits reliability in obtaining the local and instantaneous pressure uncertainty from the PIV/PTV measurements.
We present a new uncertainty estimation method for particle image velocimetry (PIV), that uses the correlation plane as a model for the probability density function (PDF) of displacements and ...calculates the second order moment of the correlation (MC). The cross-correlation between particle image patterns is the summation of all particle matches convolved with the apparent particle image diameter. MC uses this property to estimate the PIV uncertainty from the shape of the cross-correlation plane. In this new approach, the generalized cross-correlation (GCC) plane corresponding to a PIV measurement is obtained by removing the particle image diameter contribution. The GCC primary peak represents a discretization of the displacement PDF, from which the standard uncertainty is obtained by convolving the GCC plane with a Gaussian function. Then a Gaussian least-squares-fit is applied to the peak region, accounting for the stretching and rotation of the peak, due to the local velocity gradients and the effect of the convolved Gaussian. The MC method was tested with simulated image sets and the predicted uncertainties show good sensitivity to the error sources and agreement with the expected RMS error. Subsequently, the method was demonstrated in three PIV challenge cases and two experimental datasets and was compared with the published image matching (IM) and correlation statistics (CS) techniques. Results show that the MC method has a better response to spatial variation in RMS error and the predicted uncertainty is in good agreement with the expected standard uncertainty. The uncertainty prediction was also explored as a function of PIV interrogation window size. Overall, the MC method performance establishes itself as a valid uncertainty estimation tool for planar PIV.
We introduce the first comprehensive approach to determine the uncertainty in volumetric Particle Tracking Velocimetry (PTV) measurements. Volumetric PTV is a state-of-the-art non-invasive flow ...measurement technique, which measures the velocity field by recording successive snapshots of the tracer particle motion using a multi-camera set-up. The measurement chain involves reconstructing the three-dimensional particle positions by a triangulation process using the calibrated camera mapping functions. The non-linear combination of the elemental error sources during the iterative self-calibration correction and particle reconstruction steps increases the complexity of the task. Here, we first estimate the uncertainty in the particle image location, which we model as a combination of the particle position estimation uncertainty and the reprojection error uncertainty. The latter is obtained by a gaussian fit to the histogram of disparity estimates within a sub-volume. Next, we determine the uncertainty in the camera calibration coefficients. As a final step, the previous two uncertainties are combined using an uncertainty propagation through the volumetric reconstruction process. The uncertainty in the velocity vector is directly obtained as a function of the reconstructed particle position uncertainty. The framework is tested with synthetic vortex ring images. The results show good agreement between the predicted and the expected RMS uncertainty values. The prediction is consistent for seeding densities tested in the range of 0.01–0.1 particles per pixel. Finally, the methodology is also successfully validated for an experimental test case of laminar pipe flow velocity profile measurement where the predicted uncertainty in the streamwise component is within 9% of the RMS error value.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Do employee judgments of their organization’s corporate social responsibility (CSR) programs relate to CSR-specific performance and in-role job performance? Can middle managers influence the ...formation of such judgments and what factors might moderate such cascading influences? To answer these yet unaddressed questions, we conduct three studies. Study 1 takes an organizational justice perspective and tests our baseline model. Results show that employees’ CSR judgments trigger their affective commitment and performance on extra-role CSR-specific behaviors; however, extra-role CSR-specific performance is unrelated to in-role job performance. Study 2 replicates Study 1’s findings while, in addition, applies a social information processing approach and offers novel insights by demonstrating the cascading effects of managers’ CSR judgments on employee CSR judgments. Investments made in CSR programs in order to improve employee judgments and behaviors may be unsuccessful if employees’ CSR judgments are based on social information that remains unchanged. In addition to replicating the findings from studies 1 and 2, study 3 draws from middle management involvement and leadership theories to show that leadership styles and managers’ involvement in implementing deliberate strategy can strengthen or weaken these cascading effects. This highlights the important role of middle managers as “linking pins” in the CSR strategy implementation process.
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A posteriori uncertainty quantification of particle image velocimetry (PIV) data is essential to obtain accurate estimates of the uncertainty associated with a given experiment. This is particularly ...relevant when measurements are used to validate computational models or in design and decision processes. In spite of the importance of the subject, the first PIV uncertainty quantification (PIV-UQ) methods have been developed only in the last three years. The present work is a comparative assessment of four approaches recently proposed in the literature: the uncertainty surface method (Timmins et al 2012), the particle disparity approach (Sciacchitano et al 2013), the peak ratio criterion (Charonko and Vlachos 2013) and the correlation statistics method (Wieneke 2015). The analysis is based upon experiments conducted for this specific purpose, where several measurement techniques are employed simultaneously. The performances of the above approaches are surveyed across different measurement conditions and flow regimes.
Interest in corporate social responsibility (CSR) is gaining momentum in academic and managerial circles. However, prior work in the area has paid little attention to how CSR initiatives should be ...implemented inside the organization. Against this backdrop, this study examines the impact of CSR initiatives on an important stakeholder group—employees. We build and test a comprehensive multilevel framework that focuses on whether employees derive job satisfaction from CSR programs. The proposed model predicts that a manager's charismatic leadership influences employees' interpretations about the motives underlying their companies' engagement in CSR initiatives (intrinsic and extrinsic CSR-induced attributions) which, in turn, influence employee job satisfaction. Hierarchical linear modeling of data from 47 organizational units comprising 438 employees from three world-leading manufacturing organizations shows that when employees think that their manager possesses charismatic leadership qualities, they tend to attribute the organization's motives for engaging in CSR activities to intrinsic values, which, in turn, are positively associated with job satisfaction. Also, the extent to which managers are perceived as charismatic leaders relates positively to job satisfaction. Interestingly, CSR-induced extrinsic attributions are neither explained by charismatic leadership nor do they predict job satisfaction. Implications for both theory and practice are discussed.
The pharmaceutical industry has experienced a remarkable increase in the use of subcutaneous injection of monoclonal antibodies (mAbs), attributed mainly to its advantages in reducing ...healthcare-related costs and enhancing patient compliance. Despite this growth, there is a limited understanding of how tissue mechanics, physiological parameters, and different injection devices and techniques influence the transport and absorption of the drug. In this work, we propose a high-fidelity computational model to study drug transport and absorption during and after subcutaneous injection of mAbs. Our numerical model includes large-deformation mechanics, fluid flow, drug transport, and blood and lymphatic uptake. Through this computational framework, we analyze the tissue material responses, plume dynamics, and drug absorption. We analyze different devices, injection techniques, and physiological parameters such as BMI, flow rate, and injection depth. Finally, we compare our numerical results against the experimental data from the literature.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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