PS Models Research Articles

Prognostic score-based model averaging approach for propensity score estimation

BackgroundPropensity scores (PS) are typically evaluated using balance metrics that focus on covariate balance, often without considering their predictive power for the outcome. This approach may not always result in optimal bias reduction in the treatment effect estimate. To address this issue, evaluating covariate balance through prognostic scores, which account for the relationship between covariates and the outcome, has been proposed. Similarly, using a typical model averaging approach for PS estimation that minimizes prediction error for treatment status and covariate imbalance does not necessarily optimize PS-based confounding adjustment. As an alternative approach, using the averaged PS model that minimizes inter-group differences in the prognostic score may further reduce bias in the treatment effect estimate. Moreover, since the prognostic score is also an estimated quantity, model averaging in the prognostic scores can help identify a better prognostic score model. Utilizing the model-averaged prognostic scores as the balance metric for constructing the averaged PS model can contribute to further decreasing bias in treatment effect estimates. This paper demonstrates the effectiveness of the PS model averaging approach based on prognostic score balance and proposes a method that uses the model-averaged prognostic score as a balance metric, evaluating its performance through simulations and empirical analysis.MethodsWe conduct a series of simulations alongside an analysis of empirical observational data to compare the performances of weighted treatment effect estimates using the proposed and existing approaches. In our examination, we separately provid four candidate estimates for the PS and prognostic score models using traditional regression and machine learning methods. The model averaging of PS based on these candidate estimators is performed to either maximize the prediction accuracy of the treatment or to minimize intergroup differences in covariate distributions or prognostic scores. We also utilize not only the prognostic scores from each candidate model but also an averaged score that best predicted the outcome, for the balance assessment.ResultsThe simulation and empirical data analysis reveal that our proposed model-averaging approaches for PS estimation consistently yield lower bias and less variability in treatment effect estimates across various scenarios compared to existing methods. Specifically, using the optimally averaged prognostic scores as a balance metric significantly improves the robustness of the weighted treatment effect estimates.DiscussionThe prognostic score-based model averaging approach for estimating PS can outperform existing model averaging methods. In particular, the estimator using the model averaging prognostic score as a balance metric can produce more robust estimates. Since our results are obtained under relatively simple conditions, applying them to real data analysis requires adjustments to obtain accurate estimates according to the complexity and dimensionality of the data.ConclusionsUsing the prognostic score as the balance metric for the PS model averaging enhances the performance of the treatment effect estimator, which can be recommended for a wide variety of situations. When applying the proposed method to real-world data, it is important to use it in conjunction with techniques that mitigate issues arising from the complexity and high dimensionality of the data.

Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of Ki images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower Ki in general as well as incorrect Ki in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion Ki predictions.

Qualitative discrimination and quantitative prediction of microplastics in ash based on near-infrared spectroscopy

Microplastics are recognized as a new environmental pollutant. Researchers have detected their presence in waste incineration ash. However, traditional testing methods take a very long testing period. There is a lack of research on detecting microplastics in waste incineration ash. In this paper, a portable near-infrared spectra (NIRS) spectrometer was used for qualitative discrimination and quantitative prediction of microplastics in ash. A total of 84 sets of simulated ash samples containing different types (PP, PS, PE, and PVC) and contents (2.4 wt% − 20 wt%) of microplastics were used in the model. The results show the qualitative discrimination model using support vector machines (SVM) method with multiplicative scatter correction (MSC) preprocessing could effectively identify the microplastic types in the ash with 100% detection accuracy. Furthermore, the partial least squares regression (PLSR) model was effective in quantitatively predicting the content of microplastics in ash. The Rp2 of the PP, PS, PE, and PVC models are 0.95, 0.93, 0.89, and 0.95, respectively. The RPD of the PP, PS, PE, and PVC models are 3.97, 3.96, 2.89 and 5.02, respectively. This study shows that microplastics in ash can be detected rapidly and accurately using portable near-infrared spectrometers.

Multiply robust estimator for the difference in survival functions using pseudo-observations

BackgroundWhen estimating the causal effect on survival outcomes in observational studies, it is necessary to adjust confounding factors due to unbalanced covariates between treatment and control groups. There is no study on multiple robust method for estimating the difference in survival functions. In this study, we propose a multiply robust (MR) estimator, allowing multiple propensity score models and outcome regression models, to provide multiple protection.MethodBased on the previous MR estimator (Han 2014) and pseudo-observation approach, we proposed a new MR estimator for estimating the difference in survival functions. The proposed MR estimator based on the pseudo-observation approach has several advantages. First, the proposed estimator has a small bias when any PS and OR models were correctly specified. Second, the proposed estimator considers the advantage pf the pseudo-observation approach, which avoids proportional hazards assumption. A Monte Carlo simulation study was performed to evaluate the performance of the proposed estimator. And the proposed estimator was used to estimate the effect of chemotherapy on triple-negative breast cancer (TNBC) in real data.ResultsThe simulation studies showed that the bias of the proposed estimator was small, and the coverage rate was close to 95% when any model for propensity score or outcome regression is correctly specified regardless of whether the proportional hazard assumption holds, finite sample size and censoring rate. And the simulation results also showed that even though the propensity score models are misspecified, the bias of the proposed estimator was still small when there is a correct model in candidate outcome regression models. And we applied the proposed estimator in real data, finding that chemotherapy could improve the prognosis of TNBC.ConclusionsThe proposed estimator, allowing multiple propensity score and outcome regression models, provides multiple protection for estimating the difference in survival functions.The proposed estimator provided a new choice when researchers have a "difficult time" choosing only one model for their studies.

Simultaneous object detection and segmentation for patient-specific markerless lung tumor tracking in simulated radiographs with deep learning.

Real-time tumor tracking is one motion management method to address motion-induced uncertainty. To date, fiducial markers are often required to reliably track lung tumors with X-ray imaging, which carries risks of complications and leads to prolonged treatment time. A markerless tracking approach is thus desirable. Deep learning-based approaches have shown promise for markerless tracking, but systematic evaluation and procedures to investigate applicability in individual cases are missing. Moreover, few efforts have been made to provide bounding box prediction and mask segmentation simultaneously, which could allow either rigid or deformable multi-leaf collimatortracking. The purpose of this study was to implement a deep learning-based markerless lung tumor tracking model exploiting patient-specific training which outputs both a bounding box and a mask segmentation simultaneously. We also aimed to compare the two kinds of predictions and to implement a specific procedure to understand the feasibility of markerless tracking on individualcases. We first trained a Retina U-Net baseline model on digitally reconstructed radiographs (DRRs) generated from a public dataset containing 875 CT scans and corresponding lung nodule annotations. Afterwards, we used an independent cohort of 97 lung patients to develop a patient-specific refinement procedure. In order to determine the optimal hyperparameters for automatic patient-specific training, we selected 13 patients for validation where the baseline model predicted a bounding box on planning CT (PCT)-DRR with intersection over union (IoU) with the ground-truth higher than 0.7. The final test set contained the remaining 84 patients with varying PCT-DRR IoU. For each testing patient, the baseline model was refined on the PCT-DRR to generate a patient-specific model, which was then tested on a separate 10-phase 4DCT-DRR to mimic the intrafraction motion during treatment. A template matching algorithm served as benchmark model. The testing results were evaluated by four metrics: the center of mass (COM) error and the Dice similarity coefficient (DSC) for segmentation masks, and the center of box (COB) error and the DSC for bounding box detections. Performance was compared to the benchmark model including statistical testing forsignificance. A PCT-DRR IoU value of 0.2 was shown to be the threshold dividing inconsistent (68%) and consistent (100%) success (defined as mean bounding box DSC > 0.6) of PS models on 4DCT-DRRs. Thirty-seven out of the eighty-four testing cases had a PCT-DRR IoU above 0.2. For these 37 cases, the mean COM error was 2.6 mm, the mean segmentation DSC was 0.78, the mean COB error was 2.7 mm, and the mean box DSC was 0.83. Including the validation cases, the model was applicable to 50 out of 97 patients when using the PCT-DRR IoU threshold of 0.2. The inference time per frame was 170 ms. The model outperformed the benchmark model on all metrics, and the comparison was significant (p < 0.001) over the 37 PCT-DRR IoU > 0.2 cases, but not over the undifferentiated 84 testingcases. The implemented patient-specific refinement approach based on a pre-trained baseline model was shown to be applicable to markerless tumor tracking in simulated radiographs for lungcases.

Calculation of the Halfwidth and the Activation Energy for the Soft Raman Modes in the Brominated Compounds of Tris-Sarcosine Calcium Chloride

This letter contributes how to calculate the anomalous behavior for the damping constant (halfwidth) of the ferroelectric Tris-Sarcosine Calcium Chloride (TSSC) and its brominated compounds TSSC1−xBrx (x = 0.13, 0.42 and 0.60) from the wavenumber data of the soft modes below the phase transition temperature of TC. The pseudospin-phonon coupled (PS) and the energy fluctuation (EF) models derived from the dynamical Ising model were used. Both PS and EF models have been used to contribute understanding the temperature dependence of the phase transition mechanism of TSSC1−xBrx. In addition, values of the activation energy for TSSC1−xBrx (x = 0, 0.13, 0.42 and 0.60) were extracted from the damping constant as calculated from both models (PS and EF). Our results indicate order-disorder type phase transition for TSSC1−xBrx.&#x0D; HIGHLIGHTS&#x0D; &#x0D; This work contributes how to calculating the critical behavior of the halfwidth for the soft Raman modes in ferroelectric TSSC1-xBrx (x = 0, 0.13, 0.42 and 0.60) liquid crystals from their observed wavenumbers data. Also, the values of the activation energy close to the ferroelectric-paraelectric phase transition of these crystals was deduced.&#x0D; This calculation of the halfwidth and the activation energy were performed using the pseudospin-phonon coupled (PS) and the energy fluctuation (EF) models in the ferroelectric phases (T &lt; TC) of the crystals studied here.&#x0D; &#x0D; GRAPHICAL ABSTRACT

Calculation of the spin-lattice relaxation time and the activation energy near the IV–III phase transition in pyridinium fluorosulfonate (C5NH6)FSO3

The spin-lattice relaxation time T1 H for protons nuclei is calculated in term of the pseudospin–phonon (PS) coupled and the energy fluctuation (EF) models close to the IV–III solid–solid phase transition of in (C5NH6)FSO3. This calculation was performed by associating the observed second moment of the 1H as the order parameter below T C and the disorder parameter above T C. Values of the activation energy for the cation reorientation in this crystal are also deduced by using both models. In addition, the observed dielectric permittivity of this crystal is analyzed within the framework of the Landau theory and values of the spontaneous polarization (P S) are determined as a function of temperature. The normalized values of P S are used in the PS and EF models to extract the activation energy for the reorientation of the dipole moment of this compound arising from cation–anion interaction. Our results show that the PS and EF models can describe the observed behavior of the spin-lattice relaxation time adequately for the IV–III solid–solid transition in (C5NH6)FSO3.

Assessment of groundwater quality for irrigation purposes in ilesha West local government, Osun State, Nigeria

This study assessed the suitability of different groundwater resources in a basement complex of Ilesha West Local Govt. Area (LGA) for irrigation purposes. A total of 207 water samples were collected from hand-dug wells, boreholes and springs during the peak periods of wet and dry seasons. The physicochemical characteristics (pH, total dissolved solids (TDS), electrical conductivity (EC), Alkalinity, Hardness), basic cations, anions (Ca2+, Mg2+, Na+, K+, HCO3−, SO42−, Cl−, NO3−) and heavy metals (Pb2+, Cr2+, Fe2+ and As5+) present were determined. The results were subjected to irrigation quality models such as sodium adsorption ratio (SAR), Kelly ratio (KR), sodium hazard (SH), permeability index (PI) and magnesium hazard (MH). To reveal the hydro-geochemical facies of the groundwater, Piper and Durov diagrams were plotted using the geochemist workbench sheet (GWB) software. A maximum SAR value of 6.0197 was recorded in the hand-dug wells during the dry season and the mean concentration of Cr2+ was observed to be higher than the recommended limits for irrigation. The percentage suitability of the water sources for irrigation purposes are 76%, 94%, 46% and 96% for SH, PS, KR, and MH models, respectively. Piper and Durov's diagrams revealed Na++ K+ (80%), Ca+ (90%) and HCO3− ​+ ​CO3−, Cl−. as the dominant cations, and anions respectively. Additional Hydro-geochemical plots revealed intermixing of the cations and anions, suggesting the dissolution of carbonate and silicate minerals from the parent soil or rock. Generally, the groundwater in the study area is suitable for irrigation, but the sustainability based on quantity should be investigated.

Calculation Of The Damping Constant (FWHM), The Relaxation Time, And The Activation Energy As A Function Of Temperature For DmaCd(N3)3

• Raman and IR frequencies are calculated from the molecular field theory for DMACd(N 3 ) 3 . • Raman and IR frequencies were associated with the order parameter. • The relaxation time and the activation energy were calculated for DMACd(N 3 ) 3 . • PS and EF models used for the analysis of IR and Raman active modes were adequate to describe the experimental behavior of the wavenumber and FWHM for DMACd(N 3 ) 3 The Raman frequencies of τ(CH 3 ), υ s (CNC), υ 1 (N 3 ) and δ(CH 3 ) modes, the IR frequencies of δ(NH 2 ) and ρ(NH 2 ) modes, and the corresponding FWHM values for these modes in DMACd(N 3 ) 3 were analyzed by using the experimental data from the literature for both ferroelastic and paraelastic phases. In these analyses, the Raman and the IR frequencies of these modes were associated with the order parameter calculated from the molecular field theory with regard to the structural phase transition (Tc=174 K) in DMACd(N 3 ) 3 , the frequencies and the damping constants of τ(CH 3 ), υ s (CNC), υ 1 (N 3 ), δ(CH 3 ), δ(NH 2 ), and ρ(NH 2 ) modes as a function of temperature through the model of pseudospin (dynamic disorder of DMA + cations and partially N 3 anion), phonon coupling (PS) model, and the energy fluctuation (EF) model by analyzing the experimental data for the frequencies and FWHM of these modes in hybrid organic-inorganic perovskite. Moreover, the relaxation time and the activation energy were calculated for all these modes at various temperatures by using the damping constants (FWHM) for the PS and EF models studied in DMACd(N 3 ) 3 . The results of the present research showed that the calculated Raman and IR frequencies with the damping constants of τ(CH 3 ), υ s (CNC), υ 1 (N 3 ), δ(CH 3 ), δ(NH 2 ), and ρ(NH 2 ) modes adequately accounted for the experimental data in DMACd(N 3 ) 3 . The calculated activation energies for these modes were found to be acceptable compared to the values available in the literature and the k B T c values at the critical temperature. This method for calculating the frequencies (IR and Raman) and FWHM of these modes that were associated with the phase transitions could also be applied to some other hybrid organic-inorganic perovskites.

High-dimensional generalized propensity score with application to omics data

Propensity score (PS) methods are popular when estimating causal effects in non-randomized studies. Drawing causal conclusion relies on the unconfoundedness assumption. This assumption is untestable and is considered more plausible if a large number of pre-treatment covariates are included in the analysis. However, previous studies have shown that including unnecessary covariates into PS models can lead to bias and efficiency loss. With the ever-increasing amounts of available data, such as the omics data, there is often little prior knowledge of the exact set of important covariates. Therefore, variable selection for causal inference in high-dimensional settings has received considerable attention in recent years. However, recent studies have focused mainly on binary treatments. In this study, we considered continuous treatments and proposed the generalized outcome-adaptive LASSO (GOAL) to select covariates that can provide an unbiased and statistically efficient estimation. Simulation studies showed that when the outcome model was linear, the GOAL selected almost all true confounders and predictors of outcome and excluded other covariates. The accuracy and precision of the estimates were close to ideal. Furthermore, the GOAL is robust to model misspecification. We applied the GOAL to seven DNA methylation datasets from the Gene Expression Omnibus database, which covered four brain regions, to estimate the causal effects of epigenetic aging acceleration on the incidence of Alzheimer's disease.

Calculation of the Relaxation Time and the Activation Energy Close to the Lower Phase Transition in Imidazolium Perchlorate

The temperature dependence of the relaxation time of imidazolium perchlorate (Im-ClO4) was calculated from the pseudospin-phonon coupled (PS) and the energy fluctuation (EF) models close to the first-order phase transition temperature of 247 K. This calculation was performed in terms of the proton second moment M2 that was associated with the order parameter which was predicted from the mean-field theory. Our results were in good agreement with the observed data. In addition, values of the activation energy were deduced in terms of the Arrhenius plot using our calculated values of the relaxation time from both PS and EF models.

3D finite element model based on CT images of tooth

Aim: This study aimed the description of a protocol to acquire a 3D finite element (FE) model of a human maxillary central incisor tooth restored with ceramic crowns with enhanced geometric detail through an easy-to-use and low-cost concept and validate it through finite element analysis (FEA). Methods: A human maxillary central incisor was digitalized using a Cone Beam Computer Tomography (CBCT) scanner. The resulted tooth CBCT DICOM files were imported into a free medical imaging software (Invesalius) for 3D surface/geometric reconstruction in stereolithographic file format (STL). The STL file was exported to a computer-aided-design (CAD) software (SolidWorks), converted into a 3D solid model and edited to simulate different materials for full crown restorations. The obtained model was exported into a FEA software to evaluate the influence of different core materials (zirconia - Zr, lithium disilicate - Ds or palladium/silver - Ps) on the mechanical behavior of the restorations under a 100 N applied to the palatal surface at 135 degrees to the long axis of the tooth, followed by a load of 25.5 N perpendicular to the incisal edge of the crown. The quantitative and qualitative analysis of maximum principal stress (ceramic veneer) and maximum principal strain (core) were obtained. Results: The Zr model presented lower stress and strain concentration in the ceramic veneer and core than Ds and Ps models. For all models, the stresses were concentrated in the external surface of the veneering ceramic and strains in the internal surface of core, both near to the loading area. Conclusion: The described procedure is a quick, inexpensive and feasible protocol to obtain a highly detailed 3D FE model, and thus could be considered for future 3D FE analysis. The results of numerical simulation confirm that stiffer core materials result in a reduced stress concentration in ceramic veneer.

Utilidad del drenaje quirúrgico en la cirugía de Artroplastia Total de Rodilla

Introduction. The aim of this work is to study the effect of the use of surgical drains in Total Knee Arthroplasty surgery, regarding the appearance of associated complications, the need for reoperation, blood loss and the need for transfusion, as well as postoperative mobility. Material and methods. This is a retrospective, observational, descriptive study in which 112 patients operated on for Total Knee Arthroplasty are divided into 2 groups based on the use or not of drainage. In addition to demographic variables, the following were studied: Days of post-surgery stay, loss of Hemoglobin and Hematocrit, need for transfusion, complications, and reoperations. Results. Demographic characteristics were similar in the two study groups. In the variables hemoglobin loss and hematocrit loss, statistically significant differences (p &lt;0.05) were highlighted, with a greater decrease in the group of patients who used drainage. The demographic variables, radiological Ahlbäck classification and type of prosthesis, showed potential to be modifying factors for blood loss, showing a greater decrease in PS models and in high degrees of osteoarthritis. In the other variables, no relevant differences were found between two groups. Conclusions. This study shows the absence of benefit in the use of surgical drains in total knee arthroplasty surgery. The use of this surgical technique does not entail any postoperative improvement compared to patients who did not use it in terms of reoperation needs, the appearance of complications or limitations in mobility. In fact, the drains produce a greater blood loss characterized by a greater decrease in hemoglobin and hematocrit after surgery, despite the fact that said loss does not lead to an increased need for blood transfusion.

Order–disorder transition in the ferroelectric LiTaO3

The temperature dependences of the damping constant and the relaxation time are calculated by using the Raman frequencies of a* and the lowest A1 (TO) phonons in the ferroelectric phase close to the ferroelectric-paraelectric transition in LiTaO3 (TC = 963 K). Both calculations are performed by considering the frequency as an order parameter for the pseudospin–phonon (PS) and the energy fluctuation (EF) models using the observed data from the literature. Values of the activation energies of this crystal are also deduced by using both models in this crystal. Our results show that the PS and EF models can describe the observed behavior adequately for the order–disorder transition in LiTaO3.

Enhancing student’s science process skills through problem solving model: an effectiveness study

The present study aimed to determine the effectiveness of the problem solving model for science learning. The model effectiveness was determined by student’s activities, science process skill, and knowledge aspect. The data was obtained through experimental research with a randomized control-group pretest-posttest design. This study was conducted at three different school, namely state junior high school 1 Padang, state junior high school 12 Padang and national junior high school Padang. Two classes of each school were choosen as simple random sampling. The instrument were observation sheets to collect student activities and multiple choice questions to collect students SPS and knowledge aspect. Student activity data were analyzed using the Cohen’s Kappa formula and percentage. The data of improving SPS and knowledge aspect were analyzed by U Mann-Whitney test using SPSS 19. The results revealed that the PS models proved effective which was based on the students’ activities in which they were categorized as a very active category, as well as to improve students’ SPS and knowledge aspect. Therefore, reflected from the findings of the present study, it is recommended for the teachers to implement the use of PS models in science learning at junior high school.

Predictability of Chinese Summer Extreme Rainfall Based on Arctic Sea Ice and Tropical Sea Surface Temperature

Chinese summer extreme rainfall often brings huge economic losses, so the prediction of summer extreme rainfall is necessary. This study focuses on the predictability of the leading mode of Chinese summer extreme rainfall from empirical orthogonal function (EOF) analysis. The predictors used in this study are Arctic sea ice concentration (ASIC) and regional sea surface temperature (SST) in selected optimal time periods. The most important role that Arctic sea ice (ASI) plays in the appearance of EOF1 may be strengthening the high pressure over North China, thereby preventing water vapor from going north. The contribution of SST is mainly at low latitudes and characterized by a significant cyclone anomaly over South China. The forecast models using predictor ASIC (PA), SST (PS), and the two together (PAS) are established by using data from 1980 to 2004. An independent forecast is made for the last 11 years (2005–2015). The correlation coefficient (COR) skills between the observed and cross-validation reforecast principal components (PC) of the PA, PS, and PAS models are 0.47, 0.66, and 0.76, respectively. These values indicate that SST is a major cause of Chinese summer extreme rainfall during 1980–2004. The COR skill of the PA model during the independent forecast period of 2004–2015 is 0.7, which is significantly higher than those of the PS and PAS models. Thus, the main factor influencing Chinese summer extreme rainfall in recent years has changed from low latitudes to high latitudes. The impact of ASI on Chinese summer extreme rainfall is becoming increasingly significant.

Calculation of the Raman and IR frequencies as order parameters and the damping constant (FWHM) close to phase transitions in methylhydrazinium structures

Temperature dependences of the frequencies for the Raman modes of ν (NH2), νs (CH3), ν1 (HCOO−), νS (CNN) and IR mode of ρ (NH2) are calculated in particular, for MHyMn close to the phase transition temperature (TC = 220 K) in the family of compounds CH3NH2NH2M(HCOO)3, MHyM with M = Mn, Mg, Fe and Zn. By assuming Raman and infrared frequency as an order parameter, this calculation is performed from the molecular field theory by using the experimental data from the literature. We also calculate the damping constant (FWHM) from the order parameter (Raman and IR frequency of those modes) by using the pseudospin (MHy+ cations)-phonon coupled model (PS) and the energy-fluctuation model (EF) for methylhydrazinium metal formate frameworks. Expressions of the damping constant from both models are fitted to the observed FWHM data for the Raman and infrared modes studied for these metal formates.Our results show that the anomalous behaviour of the Raman and IR frequencies of those modes, except the νS (CH3) Raman mode which are associated with the phase transition can be described adequately by the molecular field theory in MHyM, as observed experimentally. Also, the damping constant calculated from the PS and EF models can explain the observed FWHM of those Raman and IR modes studied in methylhydrazinium metal formate frameworks.

The Use of PS or CR Models is not Sufficient to Explain the Differences in the Results of Total Knee Arthroplasty. Study of Interactions

The Use of PS or CR Models is not Sufficient to Explain the Differences in the Results of Total Knee Arthroplasty. Study of Interactions

Commentary: Balancing automated procedures for confounding control with background knowledge.

In the publication by Patorno et al.1 found in this issue of Epidemiology, the authors illustrate the importance of using subject matter knowledge to complement the automated high-dimensional propensity score (hdPS) algorithm when controlling for confounding in studies based on claims data with few exposed outcomes. The topic of variable selection for PS models in settings involving large numbers of potential confounders has received considerable attention in recent years. This interest is in part due to the uncertainty in determining what role automated procedures should play in the variable selection process. With large healthcare databases becoming increasingly used in epidemiology,2–4 automated procedures can be beneficial in selecting potential confounders that are unknown to the investigator.5–7 Further, the application of automated procedures is likely to expand as safety surveillance receives more attention as part of the Food and Drug Administration’s Sentinel Initiative.8 In these settings, automated procedures, such as the hdPS, can increase the speed and efficiency of active surveillance.7 With an increasing need for automated methods for confounding control in these areas of epidemiologic research, the question becomes: how should investigators balance automated procedures with the use of subject matter knowledge?

Feasibility of periodic surface models to develop gas diffusion layers: A gas permeability study

A geometric modeling scheme called periodic surface model (PS) is used to construct three dimensional (3D) models of a gas diffusion layer's (GDL) microstructure, which allows for rapid model construction and modification of representative volume elements (RVE) with embedded periodic boundary conditions. The reconstructed PS models are optimized with the help of the genetic algorithm embedded in MATLAB to generate models with refined mesh for computational fluid dynamics (CFD) analysis. The GDL geometry is built in ANSYS/ICEM CFD, automatically, using a customized code that couples MATLAB and ICEM. To verify the validity of the suggested modeling approach the microstructures of the GDLs with different porosity and fiber orientation are generated and the in-plane and through-plane permeability and tortuosity are calculated using ANSYS/FLUENT software. The numerically predicted values of in-plane and through-plane permeability are compared to experimental measurements. Using the genetic algorithm significantly decreases the fibers intersection volume in the RVE, especially as porosity decreases. It has been found that the tortuosity of the GDL is a function of the spatial orientation of the fibers in the RVE, when the fibers are at a small angle, the in-plane tortuosity can be higher than the through-plane tortuosity.