Predict Volume Changes Research Articles

The Identification of Structural Changes in the Lithium Hexamethyldisilazide-Toluene System via Ultrasonic Relaxation Spectroscopy and Theoretical Calculations.

Ultrasonic absorption measurements were carried out over a wide concentration and temperature range by means of a pulse technique to examine the structural mechanisms and the dynamical properties in lithium hexamethyldisilazide (LiHMDS)-toluene solutions. Acoustic spectra revealed two distinct Debye-type relaxational absorptions attributed to the formation of trimers from dimeric and monomer units and to the formation of aggregates between a LiHMDS dimer and one toluene molecule in low and high frequencies, respectively. The formation of aggregates was clarified by means of molecular docking and DFT methodologies. The aggregation number, the rate constants and the thermodynamic properties of these structural changes were determined by analyzing in detail the concentration-dependent relaxation parameters. The low-frequency relaxation mechanism dominates the acoustic spectra in the high LiHMDS mole fractions, while the high-frequency relaxation influences the spectra in the low LiHMDS mole fractions. In the intermediate mole fraction region (0.25 to 0.46), both relaxations prevail in the spectra. The adiabatic compressibility, the excess adiabatic compressibility and the theoretically estimated mean free length revealed a crossover in the 0.25 to 0.46 LiHMDS mole fractions that signified the transition from one structural mechanism related with the hetero-association of LiHMDS dimers with toluene molecules to the other structural mechanism assigned to the formation of LiHMDS trimers. The combined use of acoustic spectroscopy with theoretical calculations permitted us to disentangle the underlying structural mechanisms and evaluate the volume changes associated with each reaction. The results were compared with the corresponding theoretically predicted volume changes and discussed in the context of the concentration effect on intermolecular bonding.

AN INTEGRATED MACHINE LEARNING-PROBABILISTIC APPROACH TO PREDICT BEACH VOLUME CHANGE

In the DataBeach study a machine learning model was developed with the aim to improve the efficiency and sustainability of design of soft coastal defense projects. A morphological model based on machine learning was trained and tested to predict beach volume changes with significantly reduced computational time compared to traditional process-based models. The machine learning model was then applied, combined with a ‘penalty function’ for inclusion of morphological feedback, to predict beach volume changes for the study area of approximately 2 km alongshore and on a 10-year project timescale. In order to run many different scenarios for the 10-year prediction, a probabilistic methodology was developed to take into account the uncertainties in this time period. The machine learning-based model provides great benefits for probabilistic simulations, due to the lower computational time, compared to process-based numerical models such as XBeach, and a flexible way in which it can incorporate measurement data. The performance of the tested machine learning models was comparable to that of the short term volume predictions of XBeach. Comparison of 10-year predicted volume changes using the machine learning model and measured beach topography (LiDAR) showed good agreement between measured and predicted volume changes for the dry beach area (when accounting for nourishments), but overestimation in the beach volume change predictions for the intertidal beach. These differences are partially attributed to the poor performance on XBeach for long term, normal wave conditions, which are an important factor in the intertidal area.

A Numerical-Hierarchical Framework for Predicting Volume Changes in Expansive Soils under Variable Surface and Weather Conditions

This research developed a numerical-hierarchical framework that captured surface conditions and climate parameters. Volume changes under distinct scenarios of surface boundary, antecedent moisture, and meteorological parameters were predicted using a coupled seepage-deformation model. Risk was hierarchically based on expert judgment for surface scenarios (Stage-I indices) and normal distribution for antecedent moisture and atmospheric parameters scenarios (Stage-II indices). Results indicated seasonal volumetric changes with minor variations of −5 mm from January to April, a steady settlement of −17 mm by June, and a gradual heave of +8 mm by December. All Stage-I indices showed similar trends such that the fluctuations were highest for vegetation, followed by slope, then by cover, and lowest for loading. Volume changes gradually reduced with depth and diminished at 3.1 m. Similar seasonal and profile trends were generally found for most Stage-II indices. Nonetheless, different trends under wet and dry conditions were observed for initial water content, precipitation, and air temperature. For the datum scenario, risk was non-existent till February, increased to 2.3 by June, diminished by October, and rose back to 1.0 by December. Similar values of cyclic variations in risk were found in most urban facilities. Volume changes were found to be two times higher in parks, insignificant for roads, half for five story buildings, and one-fourth for pipes under roads. Among the Stage-II indices, risk for the initial water content inhibited seasonal variations whereas that for precipitation was about half with a wider distribution; all the other indices showed about one-third the values. Under a higher occurrence probability of 0.129, a magnified risk was observed for all the indices such that the most critical were the initial water content and precipitation.

Volume prediction for large brain metastases after hypofractionated gamma knife radiosurgery through artificial neural network.

The effectiveness of single-session gamma knife radiosurgery (GKRS) for small metastatic brain tumors has been proven, but hypofractionated GKRS (hfGKRS) for large brain metastases (BM) from the linear quadratic (LQ) model is uncertain. The purpose of this study was to investigate volume changes large BM after hfGKRS from the LQ model and predict volume changes using artificial neural network (ANN). We retrospectively investigated the clinical findings of 28 patients who underwent hfGKRS with large BM (diameter >3 cm or volume >10 cc). A total of 44 tumors were extracted from 28 patients with features. We randomly divided 30 large brain tumors as training set and 14 large brain tumors as test set. To predict the volume changes after hfGKRS, we used ANN models (single-layer perceptron (SLP) and multi-layer perceptron (MLP)). The volume reduction was 96% after hfGKRS for large BM from the LQ model. ANN model predicted volume changes with 70% and 80% accuracy for SLP and MLP, respectively. Even in large BM, hfGKRS from the LQ model could be a good treatment option. Additionally, the MLP model could predict volume changes with 80% accuracy after hfGKRS for large BM.

Determination of volumetric changes in cracked expansive clays

The main objective of this research was to determine volumetric changes in cracked expansive clays. The presence of soil discontinuities was captured by using appropriate soil property functions, namely: a bimodal water retention curve and a sigmoidal swell–shrink curve. Field measurements were used in empirical equations to predict volume changes, and the results were validated using published data (laboratory testing, numerical modeling, or site monitoring). The results indicated that the average swell potential and swell pressure of the expansive Regina clay are 18 ± 2% and 155 ± 15 kPa, respectively. Likewise, heave was found to be 20–30 mm at surface and gradually diminished at 1.75 m depth. The predicted results closely matched the ranges and trends as validated using published data of laboratory testing, numerical modeling, or site monitoring. It is concluded that for natural and compacted expansive soils, the bimodal water retention curve differentiates between flow through the cracks and through the soil matrix. Likewise, the proposed sigmoidal equation accurately describes the swell–shrink curve with most of the deformations between the shrinkage limit and the crack limit.

A numerical model coupling phase transformation to predict microstructure evolution and residual stress during quenching of 1045 steel

This article deals with the numerical simulation of quenching in low alloy steels based on finite element method adapting the effect of stress on the martensitic transformation to predict temperature history, microstructure evolution, and internal stresses. By superimposing the cooling curves of selected points on the CCT diagram of 1045 steel, verification of the simulated microstructures in the cylindrical specimen is carried out by comparing it with the micrographs of microstructures. Afterward, steel gears are treated in two cooling mediums to predict volume changes of gear samples quenched in oil and water. A comparison of the experimental results with those of numerical simulation generates an overall good agreement and indicates the well-demonstrated predictive capability of the model associated with kinetics of phase transformation, thermo-physical properties, and latent heat release for real-time quenching applications.

Length Changes of Unconfined Nb3Sn Rutherford Cables During Reaction Heat Treatment

In order to predict volume changes of Nb 3 Sn coils during reaction heat treatment (RHT), coefficients of thermal expansion (CTE) of the involved materials need to be known. While the CTEs of bulk materials are widely available, there is a lack of knowledge about the dimensional changes of the Nb 3 Sn Rutherford cables. By extensometry we have measured the length changes of 11 T dipole Nb3Sn Rutherford cables during the RHT. Similar to the length change behavior of multifilament wires, after an initial cable length increase the cable length decreases when the temperature exceeds about 150 °C, due to relaxation of the Nb subelements when the Cu matrix is annealed. At higher temperatures the cable length increase is determined by the CTE of the unreacted Nb, before the onset of Nb 3 Sn formation leads to an increased expansion rate. During cooling, the thermal contraction of fully reacted Nb 3 Sn cable is similar to that of Nb 3 Sn bulk.

(Invited) Modeling Battery Performance Due to Volume Change in Porous Electrodes

In order to accurately predict the behavior of electrochemical devices, it is necessary to develop sophisticated models that take into consideration transport processes, electrochemical phenomena, mechanical stresses, and structural deformations on the operation of an electrochemical system. Many different models exist that can predict the electrochemical performance of these devices under a variety of operating and design conditions. In many of these models, the porosity of a porous electrode is often assumed constant since the volume changes seen during the intercalation reaction can be small. However, electrode materials developed in recent years show significant volume changes during intercalation, which are unable to be accurately predicted using these constant porosity models. Porosity and dimensional changes in an electrode can significantly affect the resistance of the battery during cycling and can cause premature failure of the battery due to generated stresses. Previously[1-5], we have shown the ability to incorporate dimensional and porosity changes in a porous electrode into a model to predict volume changes in the active material of a single electrode during intercalation through the coupling of porous rock mechanics and porous electrode theory. Many assumptions were used to obtain an analytical solution, including the assumption of bulk strain, uniform porosity, and uniform concentration across the electrode.[2] This model was extended to look at design considerations[5] as well as non-uniformity within a single porous electrode coupled to a lithium metal reference.[4] Here, we present a model that removes some of these assumptions and illustrates the coupling of porous electrode theory and rock mechanics in order to predict stress, strain gradients, and porosity gradients across a porous electrode during cycling in a battery containing a blended anode undergoing volume change coupled to a Lithium Nickel Manganese Cobalt Oxide (NMC) cathode.[6] In this model, we will examine separator deformation, volume change during high charge rates, and consider pressure generated in the cell due to restriction in a battery module. P. M. Gomadam and J. W. Weidner, J Electrochem Soc, 153, A179 (2006).T. R. Garrick, K. Kanneganti, X. Y. Huang and J. W. Weidner, J Electrochem Soc, 161, E3297 (2014).T. R. Garrick, Y. Dai, K. Higa, V. Srinivasan and J. W. Weidner, Ecs Transactions, 72, 11 (2016).T. R. Garrick, K. Higa, S.-L. Wu, Y. Dai, X. Huang, V. Srinivasan and J. W. Weidner, J Electrochem Soc, 164, E3592 (2017).T. R. Garrick, X. Huang, V. Srinivasan and J. W. Weidner, J Electrochem Soc, 164, E3552 (2017).K. Higa, S. Wu, D. Y. Parkinson, Y. Fu, S. Ferreira, V. Battaglia and V. Srinivasan, J Electrochem Soc, 164, E3473 (2017).

Modeling Intercalation Driven Volume Change in Porous Electrodes

In order to accurately predict the behavior of electrochemical devices, it is necessary to develop sophisticated models that take into consideration transport processes, electrochemical phenomena, mechanical stresses, and structural deformations on the operation of an electrochemical system. Many different models exist that can predict the electrochemical performance of these devices under a variety of operating and design conditions. In many of these models, the porosity of a porous electrode is often assumed constant since the volume changes seen during the intercalation reaction can be small. However, electrode materials developed in recent years show significant volume changes during intercalation, which are unable to be accurately predicted using these constant porosity models. Porosity and dimensional changes in an electrode can significantly affect the resistance of the battery during cycling and can cause premature failure of the battery due to generated stresses. Previously, we have shown the ability to incorporate dimensional and porosity changes in a porous electrode into a model to predict volume changes in the active material of a single electrode during intercalation through the coupling of porous rock mechanics and porous electrode theory. There were many assumptions used to obtain an analytical solution, including the assumption of bulk stress, bulk strain, uniform porosity, and uniform concentration across the electrode. Here, we present a model that removes these assumptions and illustrates the coupling of porous electrode theory and rock mechanics in order to predict stress, strain gradients, and porosity gradients across a porous electrode during cycling.

Chemical exchange saturation transfer for predicting response to stereotactic radiosurgery in human brain metastasis.

The purpose of this work was to determine the predictive value of chemical exchange saturation transfer (CEST) metrics in brain metastases treated with stereotactic radiosurgery (SRS). CEST spectra at a radiofrequency power of 0.52 µT were collected on a 3 Tesla (T) magnetic resonance imaging from 25 patients at three time points: pretreatment, 1 week, and 1 month post-treatment. Amide proton transfer-weighted images and maps of the amplitude and width of Lorentzian-shaped CEST peaks and the relaxation-compensated AREX metric were constructed at the offset frequencies of amide, amine, and relayed nuclear Overhauser effect (NOE) from aliphatic groups as well as the broad magnetization transfer effect. Pretreatment CEST metrics, as well as CEST metric changes at 1 week post-treatment, were compared to changes in tumor volume at 1 month. Significant (P < 0.05) 1-week predictive metrics included NOE peak amplitude (R = 0.69) in normal-appearing white matter (NAWM) and width (R = -0.55) in tumor. Baseline NOE in contralateral NAWM was negatively correlated (R = -0.69) with volume changes at 1 month. Metrics-defined outside tumor margins had higher correlation with volume changes than tumor regions of interest. CEST metrics, in particular, the NOE peak amplitude, can predict volume changes 1 month post-SRS. Magn Reson Med 78:1110-1120, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Modeling Volume Change and Battery Performance Due to Intercalation in Porous Electrodes

In order to accurately predict the behavior or electrochemical devices, it is necessary to develop sophisticated models that take into consideration transport processes, electrochemical phenomena, mechanical stresses, and structural deformations on the operation of an electrochemical system. Many different models exist that can predict the electrochemical performance of these devices under a variety of operating and design conditions.[1-7] However, many of these models assume constant porosity and do not take into consideration the effects of a variable porosity on the porous electrode during cycling and what effects that may have on performance. In recent years, electrodes have been developed that show a significant volume change during intercalation and deintercalation, which are unable to be accurately predicted using these constant porosity models. Porosity and dimensional changes in an electrode can significantly affect the resistance of the battery during cycling and cause premature failure of the battery due to generated stresses. Previously, we have shown the ability to incorporate dimensional and porosity changes in a porous electrode into a model to predict volume changes in the active material during intercalation through the coupling of porous rock mechanics and porous electrode theory.[6] However, that model incorporated many assumptions in order to obtain an analytical solution, including the assumption of bulk stress, strain, and porosity. Here, we present a model that removes those assumptions and illustrates the coupling of porous electrode theory and rock mechanics in order to predict stress, strain, and porosity gradients across a porous electrode during cycling. 1. M. Jain and J. W. Weidner, J Electrochem Soc, 146, 1370 (1999). 2. M. Jain, G. Nagasubramanian, R. G. Jungst and J. W. Weidner, J Electrochem Soc, 146, 4023 (1999). 3. P. M. Gomadam and J. W. Weidner, J Electrochem Soc, 153, A179 (2006). 4. X. C. Zhang, A. M. Sastry and W. Shyy, J Electrochem Soc, 155, A542 (2008). 5. J. Christensen and J. Newman, J Solid State Electr, 10, 293 (2006). 6. T. R. Garrick, K. Kanneganti, X. Y. Huang and J. W. Weidner, J Electrochem Soc, 161, E3297 (2014). 7. K. Higa and V. Srinivasan, J Electrochem Soc, 162, A1111 (2015).

Updating residual stem volume estimates using ALS- and UAV-acquired stereo-photogrammetric point clouds

ABSTRACTTo improve precision management and the cost effectiveness of forest practices, we investigate a pre-harvest airborne laser scanning (ALS) forest inventory with an unmanned aerial vehicle (UAV) acquired post-harvest digital aerial photogrammetry (DAP) inventory to identify the location and residual volume of stands following selection harvesting. ALS data and field measurements collected pre-harvest in 2013 (T1) and UAV imagery collected post-harvest in 2015 (T2) were processed to produce analogous point clouds of the study area near Williams Lake, British Columbia, Canada. Tree height, diameter at breast height (DBH), and species were recorded from systematically located variable radius plots subsequent to ALS and DAP collection. Point cloud metrics and field measurements from each data set were used to create T1 ALS and T2 DAP predictive volume models. Direct and indirect volume change estimates were created from the difference between T1 ALS and T2 DAP model results. The estimated root mean square error (RMSE) for volume was 17.34% and 18.50% for the 2013 ALS and 2015 DAP models, respectively. The indirect and direct models predicting volume change produced errors of 16.65% and 86.56%, respectively. Results achieved from ALS and DAP models indicate strong potential for inventories generated using UAV-acquired DAP to estimate the quantity and location of residual volume after harvest operations, and could be applied in tandem to act as a semi-automated inventory cycling method to improve operational efficiency and cost effectiveness in Canadian forest management.

Influence of mineral composition of siliceous rock on its volume change

A method for predicting volume changes of aggregate during variations in humidity and temperature was studied. Siliceous rock aggregates commercially available in Japan were sampled from various regions and subjected to XRD (X-ray diffraction) analysis, thermal analysis, and volume change experiments. Drying shrinkage strain and the thermal expansion coefficient of the aggregates were found to be related to their mineral composition and bound water content. Experiments confirmed that drying shrinkage was correlated with the chlorite and bound water content of the aggregate, and that thermal expansion coefficients were correlated with the aggregate’s quartz content.

TU‐G‐BRA‐05: Predicting Volume Change of the Tumor and Critical Structures Throughout Radiation Therapy by CT‐CBCT Registration with Local Intensity Correction

Purpose:The purpose of this study is to develop an accurate and effective technique to predict and monitor volume changes of the tumor and organs at risk (OARs) from daily cone‐beam CTs (CBCTs).Methods:While CBCT is typically used to minimize the patient setup error, its poor image quality impedes accurate monitoring of daily anatomical changes in radiotherapy. Reconstruction artifacts in CBCT often cause undesirable errors in registration‐based contour propagation from the planning CT, a conventional way to estimate anatomical changes. To improve the registration and segmentation accuracy, we developed a new deformable image registration (DIR) that iteratively corrects CBCT intensities using slice‐based histogram matching during the registration process. Three popular DIR algorithms (hierarchical B‐spline, demons, optical flow) augmented by the intensity correction were implemented on a graphics processing unit for efficient computation, and their performances were evaluated on six head and neck (HN) cancer cases. Four trained scientists manually contoured nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs for each case, to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial software, VelocityAI (Varian Medical Systems Inc.).Results:Manual contouring showed significant variations, [‐76, +141]% from the mean of all four sets of contours. The volume differences (mean±std in cc) between the average manual segmentation and four automatic segmentations are 3.70±2.30(B‐spline), 1.25±1.78(demons), 0.93±1.14(optical flow), and 4.39±3.86 (VelocityAI). In comparison to the average volume of the manual segmentations, the proposed approach significantly reduced the estimation error by 9%(B‐spline), 38%(demons), and 51%(optical flow) over the conventional mutual information based method (VelocityAI).Conclusion:The proposed CT‐CBCT registration with local CBCT intensity correction can accurately predict the tumor volume change with reduced errors. Although demonstrated only on HN nodal GTVs, the results imply improved accuracy for other critical structures.This work was supported by NIH/NCI under grant R42CA137886.

MATLAB code to estimate landslide volume from single remote sensed image using genetic algorithm and imagery similarity measurement

Information regarding the scale of a hazard is crucial for the evaluation of its associated impact. Quantitative analysis of landslide volume immediately following the event can offer better understanding and control of contributory factors and their relative importance. Such information cannot be gathered for each landslide event, owing to limitations in obtaining useable raw data and the necessary procedures of each applied technology. Empirical rules are often used to predict volume change, but the resulting accuracy is very low. Traditional methods use photogrammetry or light detection and ranging (LiDAR) to produce a post-event digital terrain model (DTM). These methods are both costly and time-intensive. This study presents a technique to estimate terrain change volumes quickly and easily, not only reducing waiting time but also offering results with less than 25% error. A genetic algorithm (GA) programmed MATLAB is used to intelligently predict the elevation change for each pixel of an image. This deviation from the pre-event DTM becomes a candidate for the post-event DTM. Thus, each changed DTM is converted into a shadow relief image and compared with a single post-event remotely sensed image for similarity ranking. The candidates ranked in the top two thirds are retained as parent chromosomes to produce offspring in the next generation according to the rules of GAs. When the highest similarity index reaches 0.75, the DTM corresponding to that hillshade image is taken as the calculated post-event DTM. As an example, a pit with known volume is removed from a flat, inclined plane to demonstrate the theoretical capability of the code. The method is able to rapidly estimate the volume of terrain change within an error of 25%, without the delays involved in obtaining stereo image pairs, or the need for ground control points (GCPs) or professional photogrammetry software.

Human AQP1 Is a Constitutively Open Channel that Closes by a Membrane-Tension-Mediated Mechanism

This work presents experimental results combined with model-dependent predictions regarding the osmotic-permeability regulation of human aquaporin 1 (hAQP1) expressed in Xenopus oocyte membranes. Membrane elastic properties were studied under fully controlled conditions to obtain a function that relates internal volume and pressure. This function was used to design a model in which osmotic permeability could be studied as a pressure-dependent variable. The model states that hAQP1 closes with membrane-tension increments. It is important to emphasize that the only parameter of the model is the initial osmotic permeability coefficient, which was obtained by model-dependent fitting. The model was contrasted with experimental records from emptied-out Xenopus laevis oocytes expressing hAQP1. Simulated results reproduce and predict volume changes in high-water-permeability membranes under hypoosmotic gradients of different magnitude, as well as under consecutive hypo- and hyperosmotic conditions. In all cases, the simulated permeability coefficients are similar to experimental values. Predicted pressure, volume, and permeability changes indicate that hAQP1 water channels can transit from a high-water-permeability state to a closed state. This behavior is reversible and occurs in a cooperative manner among monomers. We conclude that hAQP1 is a constitutively open channel that closes mediated by membrane-tension increments.

EPR-based material modelling of soils considering volume changes

In this paper an approach is presented for developing material models for soils based on evolutionary polynomial regression (EPR), taking into account its volumetric behaviour. EPR is a recently developed hybrid data mining technique that searches for structured mathematical equations (representing the behaviour of a system) using genetic algorithm and the least squares method. Stress–strain data from triaxial test are used to train and develop EPR-based material models for soil. The developed models are compared with some of the well known conventional material models. In particular, the capability of the developed EPR models in predicting volume change behaviour of soils is illustrated. It is also shown that the developed EPR-based material models can be incorporated in finite element (FE) analysis. Two geotechnical examples are presented to verify the developed EPR-based FE model (EPR-FEM). The results of the EPR-FEM are compared with those of a standard FEM where conventional constitutive models are used to describe the material behaviour. The results show that EPR-FEM can be successfully employed to analyse geotechnical engineering problems. The advantages of the proposed EPR models are highlighted.

Mathematical Model for Simulating the Springback Effect of Gel Matrixes During Drying Processes and Its Experimental Validation

Volume change is one a fundamental aspect in characterization of drying processes. Several attempts to simulate the volume change during drying have been reported in the open literature. However, so far no theoretical approach has been used to support these simulations, especially when it comes to dealing with the springback effect. In this contribution, a theoretical model was built to predict the volume change including the springback phenomenon. The theory behind the present model is based on three physical mechanisms, which are represented by the shrinkage, collapse, and swelling functions. The resulting set of equations was implemented and solved in MATLAB(Mathworks, Inc., Natick, MA) by formulating the model as a constrained optimization problem. Data for three gels reported by an independent group and characterized by different profiles in terms of the springback effect were used to validate the model. This validation showed excellent agreement between the predictions obtained by this model and the experimental data. The average error lies somewhere between 1.6 and 4.4% depending on the gel. The information extracted from the parameters included in this theoretical model should assist in understanding the mechanisms that occur during processes involving moisture/solvent changes. Hence, the present model can be used as a reliable tool to predict volume changes and to understand the dynamic mechanisms involved in pore formation/disappearance during drying processes.

Assessment of cardiac preload status by pulse pressure variation in patients after anesthesia induction: comparison with central venous pressure and initial distribution volume of glucose

Recognition of intraoperative hypovolemia is important for fluid management. Previous studies demonstrated functional preload parameter pulse pressure variation (PPV) could predict volume changes in response to fluid loading and loss. In this study, we examined the correlation between PPV and other two cardiac preload indicators, central venous pressure (CVP) or initial distribution volume of glucose (IDVG), in patients after anesthesia induction. In 30 patients undergoing scheduled craniotomy surgery, we compared measurement of PPV (%) using the Ohmeda monitor method to simultaneously measure CVP and IDVG after anesthesia induction through correlation analysis and receiver operating characteristic (ROC) curves. Pulse pressure variation has negative linear correlation with IDVG (r = -0.65, P < 0.01). IDVG values (n = 13) when PPV ≥ 11% showed a significant difference compared with those (n = 17) when PPV < 11% (P < 0.001). The ROC curve showed the best cutoff value of IDVG is 122 ml/kg, equivalent to the threshold of PPV (11%) for predicting fluid responsiveness. However, there is no significant correlation between CVP in normal ranges (4-9 mmHg) and PPV (r = -0.12, P > 0.05). As an indicator of cardiac preload, PPV has a negative linear correlation with IDVG in patients after anesthesia induction. It does not correlate well with CVP in the normal range. Our results imply that an individual PPV, not CVP, is equivalent to IDVG in assessing volume status after induction.

Investigation of the effect of coefficient of thermal expansion on prediction of refractive index of thermally formed glass lenses using FEM simulation

During the cooling phase of thermal forming processes, the properties of glass undergo minor changes. These changes are a result of structural relaxation near the glass transition region. One such property, refractive index, is the most important material property that governs the optical performance of a glass lens. Therefore, it must be properly considered prior to manufacturing. A finite element model is established in this research to simulate structural relaxation behavior of glass during cooling. This is done in order to accurately predict the refractive index change which occurs during and after the forming process. This study shows that the success of the simulation model depends largely on the accuracy of material information available to the lens manufacturers. Specifically, the comparison between simulated and experimental data demonstrates that the published information for the generic coefficient of thermal expansion is inadequate for precisely predicting volume change. To resolve the issue, a reverse calculation method was presented. In addition, simulated finite element results showed that the value of the liquid coefficient of thermal expansion has a major effect on the simulated results, as compared to fraction parameter.