Pool Model Research Articles

Developing nitrogen removal models for stormwater bioretention systems.

Bioretention systems have the potential of simultaneous runoff volume reduction and nitrogen removal. Internal water storage (IWS) layers and real-time control (RTC) strategies may further improve performance of bioretention systems. However, optimizing the design of these systems is limited by the lack of effective models to simulate nitrogen transformations under the influences of IWS design and environment conditions including soil moisture and temperature. In this study, nitrogen removal models (NRMs) are developed with two complexity levels of nitrogen cycling: the Single Nitrogen Pool (SP) models and the more complex 3 Nitrogen Pool (3P) models. The 0-order kinetics, 1st order kinetics, and the Michaelis-Menten equations are applied to both SP and 3P models, creating six different NRMs. The Storm Water Management Model (SWMM), in combination with each NRM, is calibrated and validated with a lab dataset. Results show that 0-order kinetics are not suitable in simulating nitrogen removal or transformations in bioretention systems, while 1st order kinetics and Michaelis-Menten equation models have similar performances. The best performing NRM (referred to as 3P-m) can accurately predict nitrogen event mean concentrations in bioretention effluent for 20% more events when compared to SWMM. When only calibrated with soil moisture conditions in bioretention systems without internal storage layers, 3P-m was sufficiently adaptable to predict cumulative nitrogen mass removal rates from systems with IWS or RTC rules with less than ±7% absolute error, while the absolute error from SWMM prediction can reach -23%. In general, 3P models provide higher prediction accuracy and improved time series of biochemical reaction rates, while SP models improve prediction accuracy with less required user input for initial conditions.

TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation

BackgroundWith the outbreak and spread of COVID-19 worldwide, limited ventilators fail to meet the surging demand for mechanical ventilation in the ICU. Clinical models based on structured data that have been proposed to rationalize ventilator allocation often suffer from poor ductility due to fixed fields and laborious normalization processes. The advent of pre-trained models and downstream fine-tuning methods allows for learning large amounts of unstructured clinical text for different tasks. But the hardware requirements of large-scale pre-trained models and purposeless networks downstream have led to a lack of promotion in the clinical domain. ObjectiveIn this study, an innovative architecture of a task-driven predictive model is proposed and a Task-driven Gated Recurrent Attention Pool model (TGRA-P) is developed based on the architecture. TGRA-P predicts early mortality risk from patients' clinical notes on mechanical ventilation in the ICU, which is used to assist clinicians in diagnosis and decision-making. MethodsSpecifically, a Task-Specific Embedding Module is proposed to fine-tune the embedding with task labels and save it as static files for downstream calls. It serves the task better and prevents GPU overload. The Gated Recurrent Attention Unit (GRA) is proposed to further enhance the dependency of the information preceding and following the text sequence with fewer parameters. In addition, we propose a Residual Max Pool (RMP) to avoid ignoring words in common text classification tasks by incorporating all word-level features of the notes for prediction. Finally, we use a fully connected decoding network as a classifier to predict the mortality risk. ResultThe proposed model shows very promising results with an AUROC of 0.8245±0.0096, an AUPRC of 0.7532±0.0115, an accuracy of 0.7422±0.0028 and F1-score of 0.6612±0.0059 for 90-day mortality prediction using clinical notes of ICU mechanically ventilated patients on the MIMIC-III dataset, all of which are better than previous studies. Moreover, the superiority of the proposed model in comparison with other baseline models is also statistically validated through the calculated Cohen's d effect sizes. ConclusionThe experimental results show that TGRA-P based on the innovative task-driven prognostic architecture obtains state-of-the-art performance. In future work, we will build upon the provided code and investigate its applicability to different datasets. The model balances performance and efficiency, not only reducing the cost of early mortality risk prediction but also assisting physicians in making timely clinical interventions and decisions. By incorporating textual records that are challenging for clinicians to utilize, the model serves as a valuable complement to physicians' judgment, enhancing their decision-making process.

FedScore: A privacy-preserving framework for federated scoring system development

ObjectiveWe propose FedScore, a privacy-preserving federated learning framework for scoring system generation across multiple sites to facilitate cross-institutional collaborations. Materials and methodsThe FedScore framework includes five modules: federated variable ranking, federated variable transformation, federated score derivation, federated model selection and federated model evaluation. To illustrate usage and assess FedScore’s performance, we built a hypothetical global scoring system for mortality prediction within 30 days after a visit to an emergency department using 10 simulated sites divided from a tertiary hospital in Singapore. We employed a pre-existing score generator to construct 10 local scoring systems independently at each site and we also developed a scoring system using centralized data for comparison. ResultsWe compared the acquired FedScore model’s performance with that of other scoring models using the receiver operating characteristic (ROC) analysis. The FedScore model achieved an average area under the curve (AUC) value of 0.763 across all sites, with a standard deviation (SD) of 0.020. We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models. ConclusionThis study demonstrates that FedScore is a privacy-preserving scoring system generator with potentially good generalizability.

FP-CNN: Fuzzy pooling-based convolutional neural network for lung ultrasound image classification with explainable AI

The COVID-19 pandemic wreaks havoc on healthcare systems all across the world. In pandemic scenarios like COVID-19, the applicability of diagnostic modalities is crucial in medical diagnosis, where non-invasive ultrasound imaging has the potential to be a useful biomarker. This research develops a computer-assisted intelligent methodology for ultrasound lung image classification by utilizing a fuzzy pooling-based convolutional neural network FP-CNN with underlying evidence of particular decisions. The fuzzy-pooling method finds better representative features for ultrasound image classification. The FPCNN model categorizes ultrasound images into one of three classes: covid, disease-free (normal), and pneumonia. Explanations of diagnostic decisions are crucial to ensure the fairness of an intelligent system. This research has used Shapley Additive Explanation (SHAP) to explain the prediction of the FP-CNN models. The prediction of the black-box model is illustrated using the SHAP explanation of the intermediate layers of the black-box model. To determine the most effective model, we have tested different state-of-the-art convolutional neural network architectures with various training strategies, including fine-tuned models, single-layer fuzzy pooling models, and fuzzy pooling at all pooling layers. Among different architectures, the Xception model with all pooling layers having fuzzy pooling achieves the best classification results of 97.2% accuracy. We hope our proposed method will be helpful for the clinical diagnosis of covid-19 from lung ultrasound (LUS) images.

In vitro antimicrobial activity of extracts and essential oils of Cinnamomum, Salvia, and Mentha spp. against foodborne pathogens: A meta-analysis study.

Essential oils (EOs) are a class of natural products that exhibit potent antimicrobial properties against a broad spectrum of bacteria. Inhibition diameters (IDs) and minimum inhibitory concentrations (MICs) are the typical measures of antimicrobial activity for extracts and EOs obtained from Cinnamomum, Salvia, and Mentha species. This study used a meta-analytical regression analysis to investigate the correlation between ID and MIC measurements and the variability in antimicrobial susceptibility tests. By utilizing pooled ID models, this study revealed significant differences in foodborne pathogens' susceptibility to extracts, which were dependent on both the plant species and the methodology employed (p<.05). Cassia showed the highest efficacy against Salmonella spp., exhibiting a pooled ID of 26.24mm, while cinnamon demonstrated the highest efficacy against Bacillus cereus, with a pooled ID of 23.35mm. Mint extract showed the greatest efficacy against Escherichia coli and Staphylococcus aureus. Interestingly, cinnamon extract demonstrated the lowest effect against Shiga toxin-producing E. coli, with a pooled ID of only 8.07mm, whereas its EOs were the most effective against this bacterial strain. The study found that plant species influenced the MIC, while the methodology did not affect MIC measurements (p>.05). An inverse correlation between ID and MIC measurements was identified (p<.0001). These findings suggest that extracts and EOs obtained from Cinnamomum, Salvia, and Mentha spp. have the potential to inhibit bacterial growth. The study highlights the importance of considering various factors that may influence ID and MIC measurements when assessing the effectiveness of antimicrobial agents.

Country Level Governance as A Capital Structure Determinant: A Case of Pakistani Non-Financial Listed Firms

Current paper investigated the effects of country governance indicators and firm level variables on the capital structure of non-financial listed firms registered with Pakistan Stock Exchange (PSX) for the interval of 2002-18. The collected panel data was analyzed by employing panel data analysis techniques e.g., pooled OLS, fixed effect and random effect (FR/RE) model and 2-step system GMM. The purpose of employing these techniques was to capture the un-observed heterogeneity and endogeneity in the model. FE model with robust standard error was applied to control the heteroskedasticity in the model. The suitability of pooled OLS and RE model was decided on the basis of P-value of “Breusch and Pagan Lagrangian Multiplier Test” (BPLM). The results revealed that corruption perception, country governance variables and firm level variables have a considerable impact on capital structure of non-financial registered firms. Equally theory and practice can benefit from the study's findings. The study aims to theoretically incorporate new independent variables into the capital structure literature in the context of Pakistan, including political patronage, democratic regime, rule of law, abuse of power, quality of regulation, stability of politics, voice and accountability, and government effectiveness. On the practical side, the findings provide guidelines for the firms to be careful about these determinants that may be helpful in minimizing the default risk associated while lending funds. Future research may be conducted by replicating in other sectors to validate the results.

Corporate governance quality, corporate life cycle and investor confidence in commercial banks: Evidence from Nigeria

A dominant strand of literature advances a positive association between corporate governance quality and investor confidence. However, the corporate life cycle may influence the relationship. Therefore, this study investigated the moderating role of the corporate life cycle in the association between corporate governance quality and investor confidence in the Nigerian banking industry. Corporate governance quality was proxied using a composite measure of board characteristics comprising board size, board meeting, independence, and board gender diversity, while investor confidence was proxied using the price-earnings ratio. Secondary data were obtained from the audited annual financial statements of 12 banks from 2006 to 2021. The study adopted a pooled regression model based on the results of Hausman, and the Breusch and Pagan Lagrangian multiplier test. The results showed that corporate governance quality positively and significantly impacted investor confidence at the introduction (coef = .318, p = 0.017) and decline (coef = 383, p = 0.011) phases of the life cycle. Banks at the introduction and decline phases of the life cycle were characterized by a narrow resource base, low profitability, and higher risky investments sufficient to attract investor confidence. The study concludes that corporate governance quality enhanced investor confidence at the introduction and decline phases of the banks’ life cycle.

Impact of land use on bike-sharing travel patterns: Evidence from large scale data analysis in China

With bike sharing flourishing around the world, researches focus on the optimisation of station layouts, but seldom explore the dynamical features of residents’ bike-sharing travel habits. Land use factors probably indicate the activities and reflect the reasons behind residents’ travel habits; however, few studies provide empirical evidences, especially with big data analysis technology. Thus, we conduct a big data-driven empirical research to clarify residents’ travel patterns and the effects of land use thereon, which is based on the full system records of one typical bike-sharing city in China. K-modes clustering method is used to identify residents’ single and daily bike-sharing travel patterns. Three multinomial logit regression models are applied to exploring the selection mechanisms of the travel patterns. Eventually, we find that with the reference of ST pattern, the TPT-ENW pattern selection is most affected by the number of accommodation service places around travel destinations and one standard deviation increase will increase relative risk of this pattern by 1.57467. The MNT-SC pattern is most affected by the number of corporations and the other five single travel patterns are all significantly affected by the number of catering service places, which indicates commuting and dining are the travel purposes of these patterns. In addition, we find a multinomial logit model with individual fixed effects can explain residents’ daily travel pattern selection better than a pooled multinomial logit model. The analysis of residents’ travel routines explains the distribution of bike-sharing mobility and provides basis for bike-sharing system policy formulation.

Determinants of liquidity risk: Empirical evidence from Indian commercial banks

Liquidity risk is a significant financial threat that must be handled carefully. Underestimation or mismanagement of liquidity risk may lead to severe financial losses or even bank failures. Therefore, timely and adequately estimating liquidity risk and examining factors that affect liquidity risk are essential. On that account, this paper aims to examine the determinants of liquidity risk for Indian commercial banks from 2013 to 2022. For this purpose, the study has employed a panel data regression model with pooled OLS, fixed effect, and random effect methods and has considered bank-specific and macroeconomic variables. The findings show that liquidity risk is affected by both bank-specific variables and macroeconomic variables. Bank-specific variables, such as bank age, have a negative impact on liquidity risk at the 1 percent significance using pooled OLS, FE, and RE models. In contrast, bank size and bank capitalization positively impacted liquidity risk. However, the operational efficiency of banks was found to have no significant impact on liquidity risk using both the liquid asset to total assets ratio and the loan to deposit ratio. In addition, the results show that macroeconomic variables such as GDP and inflation have a positive impact on liquidity risk. The study’s findings are expected to assist various stakeholders in making appropriate policies, decisions and managing their liquidity risk.

Cheapest-to-deliver pricing, optimal MBS securitization, and welfare implications

We study optimal securitization in the agency mortgage-backed securities (MBS) market. Many MBS are traded in the liquid to-be-announced (TBA) market, which however induces adverse selection due to cheapest-to-deliver pricing. We find that lenders pool high-value loans separately and trade them in a less liquid market. We estimate a model of MBS pooling and trading to study welfare implications of pooling policies. TBA market structure produces a trade-off between efficiency and equity; broader pooling increases liquidity and average welfare, but results in a larger cross-subsidy from smaller loans to larger loans. Minimizing costs or limiting strategic pooling results in a more regressive redistribution.

Valorization of food waste for biogas production; effect of co-ensiling with maize straw at different C/N ratios

Food waste (FW) contribute to emission of greenhouse gases as well as environmental pollution. One way of reducing the impact of FW on the environment is by using it for biofuel production. The yield of biofuel from FW can be improved if the substrate is pretreated. In this study, ensiling was used as a cost effective method of pretreating FW for biomethane production. Co-ensiling of FW and maize straw (MS) was carried out at carbon to nitrogen ratios (C/N) of 20, 25, 30 and 35 for 210 days at ambient temperature. Thereafter, the biomethane potential (BMP) of the silages was determined using an automatic biomethane potential test system. Results showed that ensiling reduced both structural and non-structural carbohydrate components of the silages. BMPs of 385.58, 497.39, 520.53, 551.37, 542.16 and 517.29mL/gVS from the unensiled FW, ensiled FW without MS, co-ensiled FW at C/N ratio of 20, co-ensiled FW at C/N ratio of 25, co-ensiled FW at C/N ratio of 30 and co-ensiled FW at C/N ratio of 35 respectively were obtained. A kinetic evaluation showed that the dual pool model gave a better prediction of the experimental BMP of all substrates than the first-order model and the modified Gompertz model.

Ecological and economic determinants of energy efficiency in European countries

Since economic development is often associated with an increase in greenhouse gas emissions, it is especially important to answer the question of whether it is possible to achieve sustainable economic development and improve energy efficiency at the same time, which implies a reduction in greenhouse gas emissions. The purpose of this study was to build models that can help find the environmental and economic determinants of energy efficiency in European countries. An ecological and economic determinants of energy efficiency indicators of 38 European countries were found based on panel regression models. The models were built based on statistical data characterizing the level of their economic development and consumption of diverse types of energy for 1995-2021. To obtain the necessary approximations of energy efficiency indicators, one of three types of models was used: the random effects method, the fixed effects method, or the pooled model. For this, appropriate statistical tests were used. As a result, it was found that the factors that have a statistically significant impact on the energy intensity of GDP in European countries include the intensity of carbon dioxide emissions, unemployment rate, primary energy consumption per capita, and gross electricity production per capita. The findings showed that determinants of primary energy consumption were domestic consumption of solid fossil fuels per capita, domestic natural gas consumption per capita, and primary energy consumption per capita. The study results generally confirm the modern-day thesis that economic development can be achieved while reducing greenhouse gas emissions. The monitoring of factors that have a statistically significant impact on the indicators under consideration can become an essential element of the modern energy efficiency management system of the national economies of European countries

Evolution of statistical models for meta-analysis and implications for best practice

Purpose of review Meta-analyses are a common and important component of clinical practice guidelines. Concomitantly, there has been a tremendous increase over the past three decades in the number of published meta-analyses. An important factor in the quality of the results from a meta-analysis rests on selecting the most appropriate pooling model. In this brief review, the evolution of the numerous different pooling models that extend beyond the traditional fixed effect, fixed effects, and random effects models is described, with a focus on estimating between-study variance, that is, heterogeneity. The most recent evidence, including alternative models, is also described and recommendations for model selection and reporting provided. Recent findings In the absence of checking for between-study normality, appropriately conducted simulation studies have found that the IVhet model, a quasi-likelihood approach, may be the best model for pooling results in an aggregate data meta-analysis. Summary The IVhet model is recommended for pooling results for an aggregate data meta-analysis. If there is insistence on a random effects model, the restricted maximum likelihood method along with the Knapp-Hartung adjustment is recommended. A need exists for a large, collaborative, appropriately conducted simulation study that examines which pooling models are best based on the scenario presented.

Living alone and mental health: parallel analyses in UK longitudinal population surveys and electronic health records prior to and during the COVID-19 pandemic

BackgroundPeople who live alone experience greater levels of mental illness; however, it is unclear whether the COVID-19 pandemic had a disproportionately negative impact on this demographic.ObjectiveTo describe the mental health...

Serum and supplemental vitamin D levels and insulin resistance in T2DM populations: a meta-analysis and systematic review

Observational studies have shown a negative correlation between Vitamin D level and the likelihood of developing insulin resistance (IR) and/or diabetes over time, yet evidence remains inconsistent. In this meta-analysis and systematic review, we strive to define the potential association between serum or supplemental Vitamin D Levels and insulin resistance respectively, as well as the contribution of Vitamin D to type 2 diabetes, and to summarize the biologic plausibility of Vitamin D. Four databases (PubMed, Embase, Cochrane Library, and Web of Science) were searched for this Systematic Literature Review (SLR) to find appropriate observational studies and clinical trials published in English through to July 2022. EndNote (version X9) is used to manage the literature search results. We calculated Standard Mean Differences (SMDs) and Risk Ratios (RRs) with their 95% Confidence Intervals (CIs), separately, for continuous and dichotomous outcomes. The correlation coefficients were normalized to z values through Fisher’s z-transformation to calculate the relevant statistics. Meta-analyses were carried out for all comparisons, based on a random-effects pooling model. Data analysis was performed using RevMan (version 5.3) and STATA (version 15.1). All statistical tests were two-sided, with P < 0.05 were regarded as significant. In our current meta-analysis, there are 18 RCTs and 20 observational studies including 1243 and 11,063 participants respectively. In the overall analysis, the diabetic with Vitamin D supplement treatment group showed significantly improve serum insulin (SMD = − 0.265, 95% CI − 0.394 to − 0.136, P < 0.05), glucose (SMD = − 0.17, 95% CI − 0.301to − 0.039, P < 0.05) and HOMA-IR (SMD = − 0.441, 95% CI − 0.582 to − 0.3, P < 0.05) compared with the routine treatment group. Correlation analysis results showed that all three outcomes were significantly correlated in a negative manner with raised Vitamin D (insulin: r = − 0.08 95% = − 0.12 to − 0.04; glucose: r = − 0.06 95% = − 0.11 to − 0.01; HOMA-IR: r = − 0.08 95% = − 0.09 to − 0.06). Results of overall analysis proved that vitamin D has shown significant effect on regulates insulin resistance, and there is a significant inverse association between serum Vitamin D level and IR. Vitamin D supplementation is expected to be integrated into conventional medical approaches to prevent type 2 diabetes and to mitigate the burden of diabetes for individuals and society.PROSPERO registration number: CRD42022348295.

Association between greenspace and cancer: evidence from a systematic review and meta-analysis of multiple large cohort studies.

Cancer is a chronic disease that seriously endangers human health, and studies on its association with greenspace have been published. We aimed to systematically review the epidemiological evidence and obtain the best available evidence. PubMed, Web of Science, Embase, and Cochrane Library were used as search databases, the time limit was September 12, 2022, and the cited articles were manually supplemented. Two researchers independently performed literature screening and data extraction. We performed a meta-analysis of data using a normalized difference vegetation index (NDVI) as the greenspace measure, providing hazard ratio (HR) and corresponding 95% CI. After standardization of the data, we used a random effects model for pooling. We also assessed the risk of bias for each study and the quality of each evidence body. We identified 10,108 items and included 14 studies from 11 institutions in eight countries. All studies had a low risk of bias. Quantitative analysis of 13 studies found a beneficial association of greenspace with the mortality of lung cancer (pooled HR [95% CI]=0.965 [0.947, 0.983]) and prostate cancer (HR [95% CI]=0.939 [0.898, 0.980]) based on 0.1-unit NDVI increment and a potential beneficial association with the incidence of prostate, lung, and breast cancer. Greenspace had opposite associations with cancer mortality for urban and rural populations. Indirect comparisons did not find statistically significant differences in the effects of greenspace on different cancer outcomes. The evidence body assessment was considered to be "very low." This review indicated potential beneficial associations between greenspace for lung, prostate, and breast cancer outcomes. However, there was a lack of mediation analysis to explore the underlying mechanism of a causal association. Meanwhile, the interstudy heterogeneity was large. Therefore, future studies should consider more accurate exposure assessment and more comprehensive covariate coverage, while focusing on mediating analysis. PROSPERO: CRD42022361068.

Competitive agricultural input markets in Ethiopia: Theoretical promises versus reality

AbstractUsing the agency theory framework to develop conceptual frameworks, this study investigates the main challenges to and potential determinants of competitive agricultural input markets by comparing intervention and control districts. Specifically, this study examines the impact of the Agriculture One‐Stop Shop Project on the competitiveness of agricultural input markets in Ethiopia. The paper uses data collected from farmers and project stakeholders through interviews and focus group discussions and compiles recorded price and quantity sold for each agricultural input for four years. The study employs a content analysis, concentration ratio, market margin, and pooled panel regression models to analyze the set of data. The finding shows the existence of weak innovative system functions that is the mirror of unsatisfactory project outcomes in some intervention areas. Overall, the findings indicate that the project has increased competition by reducing market margins and relative prices, delivering a high quantity of high‐quality agricultural inputs, and providing new services and a range of input choices. An input market's level of competitiveness is determined by contractual arrangements, capital, background, and risk management. An agricultural input market's function system is influenced by the structural composition of the public–private partnership network, coordination, and cooperation. The one‐stop‐shop approach, operationalized through institutional arrangements, has the potential to facilitate and coordinate the functions necessary to change competitiveness in the agricultural input market. This study's panel model results show that the project has had statistically significant effects on the competitiveness of the agricultural input markets. The government should replicate this new approach throughout the country going forward.

DSRA-DETR: An Improved DETR for Multiscale Traffic Sign Detection

Traffic sign detection plays an important role in improving the capabilities of automated driving systems by addressing road safety challenges in sustainable urban living. In this paper, we present DSRA-DETR, a novel approach focused on improving multiscale detection performance. Our approach integrates the dilated spatial pyramid pooling model (DSPP) and the multiscale feature residual aggregation module (FRAM) to aggregate features at various scales. These modules excel at reducing feature noise and minimizing loss of low-level features during feature map extraction. Additionally, they enhance the model’s capability to detect objects at different scales, thereby improving the accuracy and robustness of traffic sign detection. We evaluate the performance of our method on two widely used datasets, the GTSDB and CCTSDB, and achieve impressive average accuracies (APs) of 76.13% and 78.24%, respectively. Compared with other well-known algorithms, our method shows a significant improvement in detection accuracy, demonstrating its superiority and generality. Our proposed method shows great potential for improving the performance of traffic sign detection for autonomous driving systems and will help in the development of safe and efficient autonomous driving technologies.

Sex matters: acute functional connectivity changes as markers of remission in late-life depression differ by sex.

The efficacy of antidepressant treatment in late-life is modest, a problem magnified by an aging population and increased prevalence of depression. Understanding the neurobiological mechanisms of treatment response in late-life depression (LLD) is imperative. Despite established sex differences in depression and neural circuits, sex differences associated with fMRI markers of antidepressant treatment response are underexplored. In this analysis, we assess the role of sex on the relationship of acute functional connectivity changes with treatment response in LLD. Resting state fMRI scans were collected at baseline and day one of SSRI/SNRI treatment for 80 LLD participants. One-day changes in functional connectivity (differential connectivity) were related to remission status after 12 weeks. Sex differences in differential connectivity profiles that distinguished remitters from non-remitters were assessed. A random forest classifier was used to predict the remission status with models containing various combinations of demographic, clinical, symptomatological, and connectivity measures. Model performance was assessed with area under the curve, and variable importance was assessed with permutation importance. The differential connectivity profile associated with remission status differed significantly by sex. We observed evidence for a difference in one-day connectivity changes between remitters and non-remitters in males but not females. Additionally, prediction of remission was significantly improved in male-only and female-only models over pooled models. Predictions of treatment outcome based on early changes in functional connectivity show marked differences between sexes and should be considered in future MR-based treatment decision-making algorithms.

Hierarchical Bayesian model for predicting small-strain stiffness of sand

This paper develops a hierarchical Bayesian model (HBM) that integrates the physical knowledge and the test data to predict the small-strain shear modulus Gmax for a target sand type. The limited target-specific data are combined with the abundant generic data through a hierarchical structure so that the variability of Gmax within one sand type and across different sand types can be captured. The hyperparameters that characterize the same underlying distribution of physical model parameters across all the sand types are first estimated from the abundant generic data. The model parameters for the new sand type are then updated as the limited site-specific data become available. The approach is illustrated using a generic database and two real examples not covered by the generic database. Multiple possible hierarchical models are compared in terms of model complexity and goodness-of-fit. The results show that the hierarchical modeling of small-strain shear modulus data is reasonable and necessary. The hierarchical model can provide less biased and more accurate predictions of Gmax compared to the commonly used complete pooling model, especially in cases where the site-specific data are quite different from the overall average of the generic database.