Verification, validation, and predictive capability in computational engineering and physics.

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

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Computational engineering of the oxygen electrode-electrolyte interface in solid oxide fuel cells

npj Computational Materials ◽

10.1038/s41524-021-00584-8 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Kaiming Cheng ◽

Huixia Xu ◽

Lijun Zhang ◽

Jixue Zhou ◽

Xitao Wang ◽

...

Keyword(s):

Fuel Cells ◽

Solid Oxide Fuel Cells ◽

Microstructure Evolution ◽

Oxygen Electrode ◽

Solid Oxide ◽

Ohmic Loss ◽

Oxide Fuel ◽

Computational Engineering ◽

Electrolyte Interface

AbstractThe Ce0.8Gd0.2O2−δ (CGO) interlayer is commonly applied in solid oxide fuel cells (SOFCs) to prevent chemical reactions between the (La1−xSrx)(Co1−yFey)O3−δ (LSCF) oxygen electrode and the Y2O3-stabilized ZrO2 (YSZ) electrolyte. However, formation of the YSZ–CGO solid solution with low ionic conductivity and the SrZrO3 (SZO) insulating phase still happens during cell production and long-term operation, causing poor performance and degradation. Unlike many experimental investigations exploring these phenomena, consistent and quantitative computational modeling of the microstructure evolution at the oxygen electrode–electrolyte interface is scarce. We combine thermodynamic, 1D kinetic, and 3D phase-field modeling to computationally reproduce the element redistribution, microstructure evolution, and corresponding ohmic loss of this interface. The influences of different ceramic processing techniques for the CGO interlayer, i.e., screen printing and physical laser deposition (PLD), and of different processing and long-term operating parameters are explored, representing a successful case of quantitative computational engineering of the oxygen electrode–electrolyte interface in SOFCs.

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Comparison of pretherapeutic osseous tumor volume and standard hematology for prediction of hematotoxicity after PSMA-targeted radioligand therapy

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-021-05412-1 ◽

2021 ◽

Author(s):

Liam Widjaja ◽

Rudolf A. Werner ◽

Tobias L. Ross ◽

Frank M. Bengel ◽

Thorsten Derlin

Keyword(s):

Multivariate Analysis ◽

Tumor Volume ◽

Predictive Performance ◽

Castration Resistant Prostate Cancer ◽

Operating Characteristics ◽

Predictive Capability ◽

Radioligand Therapy ◽

Castration Resistant ◽

Pet Ct ◽

Late Stages

Abstract Purpose Hematotoxicity is a potentially dose-limiting adverse event in patients with metastasized castration-resistant prostate cancer (mCRPC) undergoing prostate-specific membrane antigen (PSMA)-directed radioligand therapy (RLT). We aimed to identify clinical or PSMA-targeted imaging-derived parameters to predict hematological adverse events at early and late stages in the treatment course. Methods In 67 patients with mCRPC scheduled for 177Lu-PSMA-617 RLT, pretherapeutic osseous tumor volume (TV) from 68Ga-PSMA-11 PET/CT and laboratory values were assessed. We then tested the predictive capability of these parameters for early and late hematotoxicity (according to CTCAE vers. 5.0) after one cycle of RLT and in a subgroup of 32/67 (47.8%) patients after four cycles of RLT. Results After one cycle, 10/67 (14.9%) patients developed leukocytopenia (lymphocytopenia, 39/67 [58.2%]; thrombocytopenia, 17/67 [25.4%]). A cut-off of 5.6 × 103/mm3 for baseline leukocytes was defined by receiver operating characteristics (ROC) and separated between patients with and without leukocytopenia (P < 0.001). Baseline leukocyte count emerged as a stronger predictive factor in multivariate analysis (hazard ratio [HR], 33.94, P = 0.001) relative to osseous TV (HR, 14.24, P = 0.01). After four cycles, 4/32 (12.5%) developed leukocytopenia and the pretherapeutic leukocyte cut-off (HR, 9.97, P = 0.082) tended to predict leukocytopenia better than TV (HR, 8.37, P = 0.109). In addition, a cut-off of 1.33 × 103/mm3 for baseline lymphocytes separated between patients with and without lymphocytopenia (P < 0.001), which was corroborated in multivariate analysis (HR, 21.39, P < 0.001 vs. TV, HR, 4.57, P = 0.03). After four cycles, 19/32 (59.4%) developed lymphocytopenia and the pretherapeutic cut-off for lymphocytes (HR, 46.76, P = 0.007) also demonstrated superior predictive performance for late lymphocytopenia (TV, HR, 5.15, P = 0.167). Moreover, a cut-off of 206 × 103/mm3 for baseline platelets separated between patients with and without thrombocytopenia (P < 0.001) and also demonstrated superior predictive capability in multivariate analysis (HR, 115.02, P < 0.001 vs.TV, HR, 12.75, P = 0.025). After four cycles, 9/32 (28.1%) developed thrombocytopenia and the pretherapeutic cut-off for platelets (HR, 5.44, P = 0.048) was also superior for the occurrence of late thrombocytopenia (TV, HR, 1.44, P = 0.7). Conclusions Pretherapeutic leukocyte, lymphocyte, and platelet levels themselves are strong predictors for early and late hematotoxicity under PSMA-directed RLT, and are better suited than PET-based osseous TV for this purpose.

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Free Energy of Metals from Quasi-Harmonic Models of Thermal Disorder

Metals ◽

10.3390/met11020195 ◽

2021 ◽

Vol 11 (2) ◽

pp. 195

Author(s):

Pavel A. Korzhavyi ◽

Jing Zhang

Keyword(s):

Free Energy ◽

First Principles ◽

Density Functional ◽

Helmholtz Free Energy ◽

Complex Materials ◽

Predictive Capability ◽

Temperature Dependent ◽

Disordered Alloys ◽

Thermal Disorder ◽

Structure Calculations

A simple modeling method to extend first-principles electronic structure calculations to finite temperatures is presented. The method is applicable to crystalline solids exhibiting complex thermal disorder and employs quasi-harmonic models to represent the vibrational and magnetic free energy contributions. The main outcome is the Helmholtz free energy, calculated as a function of volume and temperature, from which the other related thermophysical properties (such as temperature-dependent lattice and elastic constants) can be derived. Our test calculations for Fe, Ni, Ti, and W metals in the paramagnetic state at temperatures of up to 1600 K show that the predictive capability of the quasi-harmonic modeling approach is mainly limited by the electron density functional approximation used and, in the second place, by the neglect of higher-order anharmonic effects. The developed methodology is equally applicable to disordered alloys and ordered compounds and can therefore be useful in modeling realistically complex materials.

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A process-based method for predicting lateral erosion rates

Natural Hazards ◽

10.1007/s11069-021-04587-y ◽

2021 ◽

Author(s):

Myron van Damme

Keyword(s):

Soil Mechanics ◽

High Surface ◽

Shear Stresses ◽

Empirical Equations ◽

Predictive Capability ◽

Surface Shear ◽

Erosion Rates ◽

Material Texture ◽

Empirical Coefficients ◽

The Impact

AbstractAn accurate means of predicting erosion rates is essential to improve the predictive capability of breach models. During breach growth, erosion rates are often determined with empirical equations. The predictive capability of empirical equations is governed by the range for which they have been validated and the accuracy with which empirical coefficients can be established. Most empirical equations thereby do not account for the impact of material texture, moisture content, and compaction energy on the erosion rates. The method presented in this paper acknowledges the impact of these parameters by accounting for the process of dilation during erosion. The paper shows how, given high surface shear stresses, the erosion rate can be quantified by applying the principles of soil mechanics. Key is thereby to identify that stress balance situation for which the dilatency induced inflow gives a maximum averaged shear resistance. The effectiveness of the model in predicting erosion rates is indicated by means of three validation test cases. A sensitivity analysis of the method is also provided to show that the predictions lie within the range of inaccuracy of the input parameters.

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Kinetic and Microhydrodynamic Modeling of Fenofibrate Nanosuspension Production in a Wet Stirred Media Mill

Pharmaceutics ◽

10.3390/pharmaceutics13071055 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1055

Author(s):

Gulenay Guner ◽

Dogacan Yilmaz ◽

Ecevit Bilgili

Keyword(s):

Rate Constant ◽

Kinetic Models ◽

Multiple Linear Regression Model ◽

Empirical Correlation ◽

P Value ◽

Order Kinetic ◽

Stirrer Speed ◽

Predictive Capability ◽

Breakage Rate ◽

The Impact

This study examined the impact of stirrer speed and bead material loading on fenofibrate particle breakage during wet stirred media milling (WSMM) via three kinetic models and a microhydrodynamic model. Evolution of median particle size was tracked via laser diffraction during WSMM operating at 3000–4000 rpm with 35–50% (v/v) concentration of polystyrene or zirconia beads. Additional experiments were performed at the center points of the above conditions, as well as outside the range of these conditions, in order to test the predictive capability of the models. First-order, nth-order, and warped-time kinetic models were fitted to the data. Main effects plots helped to visualize the influence of the milling variables on the breakage kinetics and microhydrodynamic parameters. A subset selection algorithm was used along with a multiple linear regression model (MLRM) to delineate how the breakage rate constant k was affected by the microhydrodynamic parameters. As a comparison, a purely empirical correlation for k was also developed in terms of the process/bead parameters. The nth-order model was found to be the best model to describe the temporal evolution; nearly second-order kinetics (n ≅ 2) was observed. When the process was operated at a higher stirrer speed and/or higher loading with zirconia beads as opposed to polystyrene beads, the breakage occurred faster. A statistically significant (p-value ≤ 0.01) MLRM of three microhydrodynamic parameters explained the variation in the breakage rate constant best (R2 ≥ 0.99). Not only do the models and the nth-order kinetic–microhydrodynamic correlation enable deeper process understanding toward developing a WSMM process with reduced cycle time, but they also provide good predictive capability, while outperforming the purely empirical correlation.

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Modelling the elastic mechanical properties of bioactive glass-derived scaffolds

Biomedical glasses ◽

10.1515/bglass-2020-0005 ◽

2020 ◽

Vol 6 (1) ◽

pp. 50-56

Author(s):

Francesco Baino ◽

Elisa Fiume

Keyword(s):

Elastic Properties ◽

Poisson’S Ratio ◽

Mechanical Performance ◽

Solid Material ◽

Poisson's Ratio ◽

Predictive Capability ◽

Shear Moduli ◽

Wide Range ◽

Constitutive Parameters ◽

The Relationship

AbstractPorosity is known to play a pivotal role in dictating the functional properties of biomedical scaffolds, with special reference to mechanical performance. While compressive strength is relatively easy to be experimentally assessed even for brittle ceramic and glass foams, elastic properties are much more difficult to be reliably estimated. Therefore, describing and, hence, predicting the relationship between porosity and elastic properties based only on the constitutive parameters of the solid material is still a challenge. In this work, we quantitatively compare the predictive capability of a set of different models in describing, over a wide range of porosity, the elastic modulus (7 models), shear modulus (3 models) and Poisson’s ratio (7 models) of bioactive silicate glass-derived scaffolds produced by foam replication. For these types of biomedical materials, the porosity dependence of elastic and shear moduli follows a second-order power-law approximation, whereas the relationship between porosity and Poisson’s ratio is well fitted by a linear equation.

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