Characterization and uncertainty analysis of siliciclastic aquifer-fault system

The complex siliciclastic aquifer system underneath the Baton Rouge area, Louisiana, USA, is fluvial in origin. The east-west trending Baton Rouge fault and Denham Springs-Scotlandville fault cut across East Baton Rouge Parish and play an important role in groundwater flow and aquifer salinization. To better understand the salinization underneath Baton Rouge, it is imperative to study the hydrofacies architecture and the groundwater flow field of the Baton Rogue aquifer-fault system. This is done through developing multiple detailed hydrofacies architecture models and multiple groundwater flow models of the aquifer-fault system, representing various uncertain model propositions. The hydrofacies architecture models focus on the Miocene-Pliocene depth interval that consists of the “1,200-foot” sand, “1,500-foot” sand, “1,700-foot” sand and the “2,000-foot” sand, as these aquifer units are classified and named by their approximate depth below ground level. The groundwater flow models focus only on the “2,000-foot” sand. The study reveals the complexity of the Baton Rouge aquifer-fault system where the sand deposition is non-uniform, different sand units are interconnected, the sand unit displacement on the faults is significant, and the spatial distribution of flow pathways through the faults is sporadic. The identified locations of flow pathways through the Baton Rouge fault provide useful information on possible windows for saltwater intrusion from the south. From the results we learn that the “1,200-foot” sand, “1,500-foot” sand and the “1,700-foot” sand should not be modeled separately since they are very well connected near the Baton Rouge fault, while the “2,000-foot” sand between the two faults is a separate unit. Results suggest that at the “2,000-foot” sand the Denham Springs-Scotlandville fault has much lower permeability in comparison to the Baton Rouge fault, and that the Baton Rouge fault plays an important role in the aquifer salinization

Suboccipital Muscles Injection for Management of Post-Dural Puncture Headache After Cesarean Delivery: A Randomized-Controlled Trial

INTRODUCTION: Post-dural puncture headache (PDPH) is a common complication following neuraxial anaesthesia that increases the duration of hospital stay. AIM: This study aims to evaluate the effectiveness of injection of the dexamethasone-lidocaine mixture in suboccipital muscles treatment of PDPH after cesarean section. PATIENT AND METHODS: A group of 90 females with PDPH following cesarean section under spinal anaesthesia were randomly allocated into two equal groups: study group (Group S) and control group (group C). All patients received bilateral intramuscular (in the suboccipital muscle) (Group S) (n = 45) patients received lidocaine 40 mg (2 mL of 2% solution) and dexamethasone 8mg in a total volume of 4 mL; whilst, patients in the control group (group C) (n = 45) received 4 mL normal saline. The primary outcome is the Visual Analogue Score for a headache at 24 hours after injection. RESULTS: Demographic data and the baseline, headache score, neck muscle spasm, and nausea were comparable in both groups. Group S showed lower headache score compared to group C at all the post-injection time points. All patients in group S showed resolution of nausea after the intervention; while none of the control group showed any improvement. All patients of group C needed rescue analgesia; while only 6 (13.3%) patients in group S asked for an analgesic. Time to the first analgesic request was longer in group S compared to group C (10.17 ± 7.96 hours versus 1.00 ± 0.00 hours, P < 0.001). CONCLUSION: Ultrasound-guided injection of the dexamethasone-lidocaine mixture in suboccipital muscles is effective management of PDPH after CS

Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy

Bayesian inference of microbial soil respiration models is often based on the assumptions that the residuals are independent (i.e., no temporal or spatial correlation), identically distributed (i.e., Gaussian noise), and have constant variance (i.e., homoscedastic). In the presence of model discrepancy, as no model is perfect, this study shows that these assumptions are generally invalid in soil respiration modeling such that residuals have high temporal correlation, an increasing variance with increasing magnitude of CO2 efflux, and non-Gaussian distribution. Relaxing these three assumptions stepwise results in eight data models. Data models are the basis of formulating likelihood functions of Bayesian inference. This study presents a systematic and comprehensive investigation of the impacts of data model selection on Bayesian inference and predictive performance. We use three mechanistic soil respiration models with different levels of model fidelity (i.e., model discrepancy) with respect to the number of carbon pools and the explicit representations of soil moisture controls on carbon degradation; therefore, we have different levels of model complexity with respect to the number of model parameters. The study shows that data models have substantial impacts on Bayesian inference and predictive performance of the soil respiration models such that the following points are true: (i) the level of complexity of the best model is generally justified by the cross-validation results for different data models; (ii) not accounting for heteroscedasticity and autocorrelation might not necessarily result in biased parameter estimates or predictions, but will definitely underestimate uncertainty; (iii) using a non-Gaussian data model improves the parameter estimates and the predictive performance; and (iv) accounting for autocorrelation only or joint inversion of correlation and heteroscedasticity can be problematic and requires special treatment. Although the conclusions of this study are empirical, the analysis may provide insights for selecting appropriate data models for soil respiration modeling.U.S. Department of Energy [DE-SC0019438, DE-SC0008272]; U.S. National Science Foundation [EAR-1552329, OIA-1557349]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy

Bayesian inference of microbial soil respiration models is often based on the assumptions that the residuals are independent (i.e., no temporal or spatial correlation), identically distributed (i.e., Gaussian noise), and have constant variance (i.e., homoscedastic). In the presence of model discrepancy, as no model is perfect, this study shows that these assumptions are generally invalid in soil respiration modeling such that residuals have high temporal correlation, an increasing variance with increasing magnitude of CO2 efflux, and non-Gaussian distribution. Relaxing these three assumptions stepwise results in eight data models. Data models are the basis of formulating likelihood functions of Bayesian inference. This study presents a systematic and comprehensive investigation of the impacts of data model selection on Bayesian inference and predictive performance. We use three mechanistic soil respiration models with different levels of model fidelity (i.e., model discrepancy) with respect to the number of carbon pools and the explicit representations of soil moisture controls on carbon degradation; therefore, we have different levels of model complexity with respect to the number of model parameters. The study shows that data models have substantial impacts on Bayesian inference and predictive performance of the soil respiration models such that the following points are true: (i) the level of complexity of the best model is generally justified by the cross-validation results for different data models; (ii) not accounting for heteroscedasticity and autocorrelation might not necessarily result in biased parameter estimates or predictions, but will definitely underestimate uncertainty; (iii) using a non-Gaussian data model improves the parameter estimates and the predictive performance; and (iv) accounting for autocorrelation only or joint inversion of correlation and heteroscedasticity can be problematic and requires special treatment. Although the conclusions of this study are empirical, the analysis may provide insights for selecting appropriate data models for soil respiration modeling.</p

Bilateral intra-oral distraction osteogenesis for the management of severe congenital mandibular hypoplasia in early childhood

SUMMARY. Introduction: Young children with severe mandibular hypoplasia usually present with varying degrees of peripheral airway obstruction and difficulty with feeding. Early treatment is important for such children. Distraction osteogenesis (DO) using intra-oral devices provides an excellent alternative when other surgical techniques do not prove to be satisfactory. Aim of the work: To evaluate the long-term efficacy of intra-oral bilateral DO in the treatment of severe congenital mandibular hypoplasia in early childhood. Patients and methods: Seven patients (4 females and 3 males), their ages ranged from 7 months to 8 years (with a mean of 34 months). They presented with severe congenital mandibular hypoplasia with obstructive sleep apnoea and difficulty in feeding. All patients were treated with bilateral mandibular DO, using an intra-oral unidirectional unburied distractor. The average follow-up period was 3.7 years (range, 2e5 years). Results: The patients were successfully treated using bilateral intra-oral unidirectional distractor by the use of a modified technique. After completion of distraction, retrognathia was corrected in all patients. The &apos;&apos;subjective&apos;&apos; symptoms had disappeared completely or had been alleviated. The mean effective airway space increase (defined by the lateral cephalograms measurements) was 70.5% (range, 31el05%, p \0.01) when compared with predistraction. The apnoea/hypopnoea index was lowered from 60 (9.8e126.5) to 1.57 (0e16.4) and the sleep apnoea symptoms had disappeared. The mean oxygen saturation increase was from 80% to 98% postdistraction. Conclusion: DO can consistently produce a measurable cross-section airway improvement in patients as young as 7 months. Ó 2008 European Association for Cranio-Maxillofacial Surger

Stochastic Inversion of P-to-S Converted Waves for Mantle Composition and Thermal Structure: Methodology and Application

We present a new methodology for inverting P‐to‐S receiver function (RF) waveforms directly for mantle temperature and composition. This is achieved by interfacing the geophysical inversion with self‐consistent mineral phase equilibria calculations from which rock mineralogy and its elastic properties are predicted as a function of pressure, temperature, and bulk composition. This approach anchors temperatures, composition, seismic properties, and discontinuities that are in mineral physics data, while permitting the simultaneous use of geophysical inverse methods to optimize models of seismic properties to match RF waveforms. Resultant estimates of transition zone (TZ) topography and volumetric seismic velocities are independent of tomographic models usually required for correcting for upper mantle structure. We considered two end‐member compositional models: the equilibrated equilibrium assemblage (EA) and the disequilibrated mechanical mixture (MM) models. Thermal variations were found to influence arrival times of computed RF waveforms, whereas compositional variations affected amplitudes of waves converted at the TZ discontinuities. The robustness of the inversion strategy was tested by performing a set of synthetic inversions in which crustal structure was assumed both fixed and variable. These tests indicate that unaccounted‐for crustal structure strongly affects the retrieval of mantle properties, calling for a two‐step strategy presented herein to simultaneously recover both crustal and mantle parameters. As a proof of concept, the methodology is applied to data from two stations located in the Siberian and East European continental platforms.This work was supported by a grant from the Swiss National Science Foundation (SNF project 200021_159907). B. T. was funded by a Délégation CNRS and Congé pour Recherches et Conversion Thématique from the Université de Lyon to visit the Research School of Earth Sciences (RSES), The Australian National University (ANU). B. T. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement 79382

Making Steppingstones out of Stumbling Blocks: A Bayesian Model Evidence Estimator with Application to Groundwater Transport Model Selection

Bayesian model evidence (BME) is a measure of the average fit of a model to observation data given all the parameter values that the model can assume. By accounting for the trade-off between goodness-of-fit and model complexity, BME is used for model selection and model averaging purposes. For strict Bayesian computation, the theoretically unbiased Monte Carlo based numerical estimators are preferred over semi-analytical solutions. This study examines five BME numerical estimators and asks how accurate estimation of the BME is important for penalizing model complexity. The limiting cases for numerical BME estimators are the prior sampling arithmetic mean estimator (AM) and the posterior sampling harmonic mean (HM) estimator, which are straightforward to implement, yet they result in underestimation and overestimation, respectively. We also consider the path sampling methods of thermodynamic integration (TI) and steppingstone sampling (SS) that sample multiple intermediate distributions that link the prior and the posterior. Although TI and SS are theoretically unbiased estimators, they could have a bias in practice arising from numerical implementation. For example, sampling errors of some intermediate distributions can introduce bias. We propose a variant of SS, namely the multiple one-steppingstone sampling (MOSS) that is less sensitive to sampling errors. We evaluate these five estimators using a groundwater transport model selection problem. SS and MOSS give the least biased BME estimation at an efficient computational cost. If the estimated BME has a bias that covariates with the true BME, this would not be a problem because we are interested in BME ratios and not their absolute values. On the contrary, the results show that BME estimation bias can be a function of model complexity. Thus, biased BME estimation results in inaccurate penalization of more complex models, which changes the model ranking. This was less observed with SS and MOSS as with the three other methods