Bayesian Model Averaging Research Articles

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.

Distribution‐Based Model Evaluation and Diagnostics: Elicitability, Propriety, and Scoring Rules for Hydrograph Functionals

AbstractDistribution forecasts P over future quantities or events are routinely made in hydrology but usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures or scoring functions such as the mean absolute error or mean squared error. Case in point is the so‐called KG efficiency (KGE) of Gupta et al. (2009, https://doi.org/10.1016/j.jhydrol.2009.08.003) and improvements thereof (Lamontagne et al., 2020, https://doi.org/10.1029/2020wr027101), which have rapidly gained popularity among hydrologists as alternative scoring functions to the commonly used Nash and Sutcliffe (1970, https://doi.org/10.1016/0022‐1694(70)90255‐6) efficiency, but are equally exclusive in how they quantify model performance using only single‐valued output of the quantities of interest. This point‐valued mapping necessarily implies a loss of information about model performance. This paper advocates the use of probabilistic watershed model training, evaluation and diagnostics. Distribution evaluation opens a mature literature on scoring rules whose strong statistical underpinning provides, as we will demonstrate, the theory, context and guidelines necessary for the development of robust information‐theoretically principled metrics for watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. We discuss past developments that led to the current state‐of‐the‐art of distribution evaluation in hydrology and review scoring rules for dichotomous and categorical events, quantiles (intervals) and density forecasts. We are particularly concerned with elicitable functionals and scoring rule propriety, discuss the decomposition of scoring rules into a sharpness, reliability and entropy term and present diagnostically appealing strictly proper divergence scores of hydrograph functionals for flood frequency analysis, flow duration and recession curves. The usefulness and power of distribution‐based model evaluation and diagnostics by means of scoring rules is demonstrated on simple illustrative problems and discharge distributions simulated with watershed models using random sampling and Bayesian model averaging. The presented theory (a) enables a more complete evaluation of distribution forecasts, (b) offers a statistically principled means for watershed model training, evaluation, diagnostics and selection using hydrograph functionals and/or extreme events and (c) provides a universal framework for metric development of watershed signatures, promoting metric standardization and reproducibility.

Causal relationships between circulating metabolites and rheumatoid arthritis: A mendelian randomization study

BackgroundBlood metabolites serve as pivotal indicators in identifying and predicting the course of rheumatoid arthritis (RA). However, empirical substantiation of a direct causal link between these serum biomarkers and the development of RA is still lacking comprehensive support. MethodIn pursuit of a thorough exploration of the causal links between circulating blood metabolites and RA, we deployed a two-sample Mendelian randomization (MR) approach during our initial investigative phase. This method was utilized to examine the potential connections between 249 distinct circulating metabolites and the prevalence of RA. In the validation phase, we conducted replication analyses with a new metabolic dataset consisting of 123 metabolites. Furthermore, we employed the Mendelian randomization based on Bayesian model averaging (MR-BMA) technique to pinpoint key metabolic characteristics that have significant causal implications. ResultsIn our primary analysis, we found that acetate, acetoacetate and pyruvate exhibited a consistent protective causal association with rheumatoid arthritis, while lactate demonstrated a positive correlation with rheumatoid arthritis risk. It is also noteworthy that a substantial subset of traits related to both saturated and unsaturated fatty acids showed causal influences. Subsequent secondary analyses substantiated these observations, revealing that traits associated with the average number of methylene groups in a fatty acid chain exhibited protective effects. Ultimately, our MR-BMA analyses unveiled that the ratio of polyunsaturated fatty acids (PUFAs) to total fatty acids assumes a paramount role in increasing the susceptibility to rheumatoid arthritis. ConclusionsBy employing systemic MR analyses, our study has successfully generated an all-encompassing atlas elucidating the intricate connections between circulating metabolites and the susceptibility to rheumatoid arthritis. Our results indicate the high unsaturation degree is a dominant risk factors correlated with rheumatoid arthritis.

Lateral temporal atrophy is a better predictor of baseline MMSE scores than hippocampal atrophy in Alzheimer’s disease: A retrospective cross-sectional study

Background: Mini-Mental State Exam (MMSE) is widely used for the cognitive assessment in Alzheimer’s disease (AD), but its interpretation could be affected by acute medical conditions, depression, or anxiety. Conversely, brain magnetic resonance imaging (MRI) is an independent diagnostic tool to evaluate specific cerebral pathology. Many studies have investigated the correlation between MRI visual scales such as hippocampal atrophy and baseline MMSE in AD patients. However, the correlation between a comprehensive MRI visual scale and baseline MMSE in people with AD remains less well known. Method: We retrospectively collected records of outpatients diagnosed with probable AD according to DSM -5 criteria. The comprehensive visual rating scale (CVRS) was used to semiquantitatively measure structural changes and vascular lesions. The relationship between MRI changes and baseline MMSE was evaluated using Bayesian model averaging (BMA). Results: A total of 65 patients, among whom 21 (32.31%) had early-onset AD, were included. Lateral temporal atrophy, level of education, and late age at onset were the strongest independent predictors of baseline MMSE. Furthermore, hippocampal atrophy was only correlated with delayed recall, while temporal atrophy was correlated with orientation, attention, language, and visual-spatial items of the MMSE. The cerebral atrophy and small vessel lesions scores of the late-onset AD group were significantly higher than those of the early-onset group, despite negligible differences in education and MMSE. Conclusion: Our study suggests lateral temporal atrophy correlates with baseline MMSE scores in people with AD better than the hippocampal atrophy. Age-related atrophy and silent small vessel disease lesions may have negligible impact on AD patients’ cognitive impairment.

Short-term solar photovoltaic power forecasting using ensemble forecasting strategy for renewable resources based power systems

Environmentally-friendly renewable energy sources have been developed and commercialized to mitigate impact of climate change on the environment. Solar photovoltaic (PV) systems have gained much attention as a power generation source for various uses, including the primary utility grid power supply. There has been a significant increase in both on-grid and off-grid solar PV installations. Because of the highly unpredictable nature of solar power generation, it is crucial to forecast solar power accurately for renewable resources-based power systems. In this research, a swarm-based ensemble forecasting strategy has been proposed to predict solar PV power by combining three strategies, i.e., particle swarm optimization-based gated recurrent unit (PSO-GRU), PSO-based long short-term memory (PSO-LSTM), and PSO-based bidirectional long short-term memory (PSO-BiLSTM). Bayesian model averaging (BMA) combines the output of the proposed strategy by aggregating the output of each swarm-based approach. The performance of the suggested approach is evaluated and verified using historical data of solar PV power which is acquired from Griffith University, Australia. Python 3.11 is used to validate the performance of the proposed ensemble strategy and compared it with several competing strategies. The proposed ensemble strategy outperforms other comparative strategies in terms of RMSE, NRMSE, and MAE.

Stock price index analysis of four OPEC members: a Bayesian approach

This study examines the relationship between macroeconomic variables and stock price indices of four prominent OPEC oil-exporting members. Bayesian model averaging (BMA) and regularized linear regression (RLR) are employed to address uncertainties arising from different estimation models and variable selection. Jointness is utilized to determine the nature of relationships among variable pairs. The case study spans macroeconomic variables and stock prices from 1996 to 2018. BMA findings reveal a strong positive association between stock price indices and both consumer price index (CPI) and broad money growth in each analyzed OPEC country. Additionally, the study suggests a weak negative correlation between OPEC oil prices and the stock price index. RLR results align with BMA analysis, offering insights valuable for policymakers and international wealth managers.

Predicting maximum scour depth uncertainty downstream of grade control structures: a multi-model approach with Bayesian model averaging

This research proposes a novel multi-model combination approach to overcome the limitations of traditional methods in predicting the scour depth downstream of grade control structures. The study evaluates 26 scenarios using a combination of five individual models and assesses the efficiency of four combination methods. Results indicate that GEP and SVM outperform traditional combination methods. Additionally, the study employs Bayesian Model Average (BMA) to assess the uncertainty originating from individual models. The research concludes by providing predictive equations for GEP and SVM, enabling users to apply these methods for accurate maximum scour depth prediction. In the ideal case, the SVM model had an R 2 = 0.91, with an RMSE of 0.84, whereas the GEP model achieved an R 2 = 0.94, with an RMSE of 0.67. The proposed multi-model approach is more reliable and accurate for predicting maximum scour depth downstream of GCS.

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on historical mountain torrent hazards, a mountain torrent hazard prediction model was established by using Bayesian Model Average (BMA) and three classic machine learning algorithms (gradient-boosted decision tree (GBDT), backpropagation neural network (BP), and random forest (RF)). The mountain torrent hazard condition factors used in modeling were distance to river, elevation, precipitation, slope, gross domestic product (GDP), population, and land use type. Based on the proposed BMA model, flood risk maps were produced using GIS. The results demonstrated that the BMA model significantly improved upon the accuracy and stability of single models in identifying mountain torrent hazards. The F1-values (comprehensively displays the Precision and Recall) of the BMA model under three sets of test samples at different locations were 3.31–24.61% higher than those of single models. The risk assessment results of mountain torrents found that high-risk areas were mainly concentrated in the northern border and southern valleys of Yuanyang County, China. In addition, the feature importance analysis result demonstrated that distance to river and elevation were the most important factors affecting mountain torrent hazards. The construction of projects in mountainous areas should be as far away from rivers and low-lying areas as possible. The results of this study can provide a scientific basis for improving the identification methods of mountain torrent hazards and assisting decision-makers in the implementation of appropriate measures for mountain torrent hazard prevention and reduction.

Optimal Instrument Selection Using Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators

Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, noniterative estimator for latent variable models. Associated with this estimator are equation-specific tests of model misspecification. One issue with equation-specific tests is that they lack specificity, in that they indicate that some instruments are problematic without revealing which specific ones. Instruments that are poor predictors of their target variables (“weak instruments”) is a second potential problem. We propose a novel extension to detect instrument-specific tests of misspecification and weak instruments. We term this the Model-Implied Instrumental Variable Two-Stage Bayesian Model Averaging (MIIV-2SBMA) estimator. We evaluate the performance of MIIV-2SBMA against MIIV-2SLS in a simulation study and show that it has comparable performance in terms of parameter estimation. Additionally, our instrument-specific overidentification tests developed within the MIIV-2SBMA framework show increased power to detect specific problematic and weak instruments. Finally, we demonstrate MIIV-2SBMA using an empirical example.

Setting the Stage for the Search for Life with the Habitable Worlds Observatory: Properties of 164 Promising Planet-survey Targets

The Decadal Survey on Astronomy and Astrophysics 2020 has recommended that NASA realize a large IR/optical/UV space telescope optimized for high-contrast imaging and spectroscopy of ∼25 exo-Earths and transformative general astrophysics. The NASA Exoplanet Exploration Program (ExEP) has subsequently released a list of 164 nearby (d < 25 pc) targets deemed the most accessible to survey for potentially habitable exoplanets with the Habitable Worlds Observatory (HWO). We present a catalog of system properties for the 164 ExEP targets, including 1744 abundance measurements for 14 elements from the Hypatia Catalog and 924 photometry measurements spanning from 151.6 nm to 22 μm in the GALEX, Strömgren, Tycho, Gaia, Two Micron All Sky Survey, and Wide-field Infrared Survey Explorer bandpasses. We independently derive stellar properties for these systems by modeling their spectral energy distributions with Bayesian model averaging. Additionally, by consulting the literature, we identify TESS flare rates for 46 stars, optical variability for 78 stars, and X-ray emission for 46 stars in our sample. We discuss our catalog in the context of planet habitability and draw attention to key gaps in our knowledge where precursor science can help to inform HWO mission design trade studies in the near future. Notably, only 33 of the 164 stars in our sample have reliable space-based UV measurements, and only 40 have a mid-IR measurement. We also find that phosphorus, a bioessential element, has only been measured in 11 of these stars, motivating future abundance surveys. Our catalog is publicly available and we advocate for its use in future studies of promising HWO targets.

Investigating the Bidirectional Association of Rheumatoid Arthritis and Thyroid Function: A Methodologic Assessment of Mendelian Randomization.

Rheumatoid arthritis (RA) and thyroid dysfunction are frequently observed in the same patient. However, whether they co-occur or exhibit a causal relationship remains uncertain. We aimed to systematically investigate the causal relationship between RA and thyroid function using a large sample and advanced methods. Bidirectional two-sample Mendelian randomization (MR) analysis was performed based on RA and six thyroid function trait data sets from the European population. The robustness of the results was demonstrated using multiple MR methods and a series of sensitivity analyses. Multivariable MR using Bayesian model averaging (MR-BMA) was performed to adjust for possible competing risk factors. A sensitivity data set, which included data from patients with seropositive RA and controls, was used to repeat the analyses. Furthermore, enrichment analysis was employed to discover the underlying mechanism between RA and thyroid functions. A significantly positive causal effect was identified for RA on autoimmune thyroid disease (AITD) as well as for AITD on RA (P < 0.001). Further sensitivity analyses showed consistent causal estimates from a variety of MR methods. After removing the outliers, MR-BMA results showed that RA and AITD were independent risk factors in their bidirectional causality, even in the presence of other competing risk factors (adjusted P < 0.05). Enrichment analysis showed immune cell activation and immune response play crucial roles in them. Our results illustrate the significant bidirectional causal effect of RA and AITD, which holds even in multiple competing risk factors. Clinical screening for thyroid dysfunction in patients with RA deserves further attention, and vice versa.

Research on Stock Price Multi-Factor Prediction Model Based on Bayesian Model Averaging

Factor mining is a crucial component in constructing stock price prediction and quantitative models, where factor mining methods based on feature dimensionality reduction, such as Principal Component Analysis (PCA) and sufficient dimensionality reduction, are widely utilized. However, the connection structure between the quantitative factors extracted using these methods and stock prices is unknown, leading to potential issues like overfitting or underfitting in prediction models. In light of this, this paper proposes a multi-factor prediction model based on Bayesian model averaging. On one hand, the proposed method employs the concept of model averaging instead of model selection, effectively balancing the variance and bias of prediction models. On the other hand, it can adaptively choose sub-models that play a crucial role in predicting stock prices, thereby enhancing overall prediction accuracy. Empirical data analysis indicates that the proposed method, compared to PCA-based Lasso and Ridge regression, exhibits smaller mean squared error and possesses a certain level of robustness. Lastly, by incorporating other model averaging techniques such as the Bagging algorithm, the generalization ability of the proposed method can be further improved.

Causal association of blood cell traits with inflammatory bowel diseases: a Mendelian randomization study.

Observational studies have found associations between blood cell traits and inflammatory bowel diseases (IBDs), whereas the causality and dose-effect relationships are still undetermined. Two-sample Mendelian randomization (MR) analyses using linear regression approaches, as well as Bayesian model averaging (MR-BMA), were conducted to identify and prioritize the causal blood cell traits for Crohn's disease (CD) and ulcerative colitis (UC). An observational study was also performed using restricted cubic spline (RCS) to explore the relationship between important blood cell traits and IBDs. Our uvMR analysis using the random effects inverse variance weighted (IVW) method identified eosinophil (EOS) as a causal factor for UC (OR = 1.36; 95% CI: 1.13, 1.63). Our MR-BMA analysis further prioritized that high level of lymphocyte (LYM) decreased CD risk (MIP = 0.307; MACE = -0.059; PP = 0.189; = -0.173), whereas high level of EOS increased UC risk (MIP = 0.824; MACE = 0.198; PP = 0.627; = 0.239). Furthermore, the observational study clearly depicts the nonlinear relationship between important blood cell traits and the risk of IBDs. Using MR approaches, several blood cell traits were identified as risk factors of CD and UC, which could be used as potential targets for the management of IBDs. Stratified genome-wide association studies (GWASs) based on the concentration of traits would be helpful owing to the nonlinear relationships between blood cell traits and IBDs, as demonstrated in our clinical observational study. Together, these findings could shed light on the clinical strategies applied to the management of CD and UC.

River ecosystem health assessment in the Qinghai-Tibet Plateau: A novel hybrid method based on artificial intelligence and multi-source data fusion

River ecosystem health assessment (REHA), an effective approach for identifying river ecosystem health, is crucial for achieving sustainable river management and ensuring water security. However, existing REHA methods still fail to consider the cumulated influences of uncertain inputs, stochastic environment and limited rationality of decision makers on REHA. Additionally, current REHA studies have mainly concentrated on plain areas, while the Qinghai-Tibet Plateau (QTP) remains largely unknown. Developing REHA techniques for plateau rivers is an urgent matter, due to the heightened fragility and complexity of river ecosystems in the QTP. To accurately assess river ecosystem health in the QTP, this study proposed Pythagorean fuzzy cloud (PFC) via coupling the Pythagorean fuzzy sets and cloud model. A novel PFC-TODIM model was developed by extending TODIM (the acronym in Portuguese for interactive and multicriteria decision making) to the Pythagorean fuzzy environment. The hybrid decision making framework was then created to handle REHA with uncertain inputs and stochastic environment, and the Senge Tsangpo River (STR) served as a case study in the QTP. We developed the indicator system based on multi-source data fusion, and employed Bayesian model averaging (BMA) method to reveal the potential risks and driving factors of river ecosystem health. Results showed that the developed models considered the limited rationality of decision makers, effectively handled REHA with uncertainties, and avoided overestimating river health levels due to ignoring the randomness and fuzziness of REHA. In STR, health statuses exhibited marked spatial differences. Sampling sites of 9.091%, 77.273 % and 13.636 % were excellent, healthy and subhealthy, respectively. Our findings highlight that dams, urban development, fish release, and grazing have adverse impacts on STR health, and effective protection measures are required to minimize human interferences on ecologically fragile areas. These findings can improve our understanding of the human disturbances and natural factors that interfere with river ecosystem health in the QTP.

Identifying and Interpreting Hydrological Model Structural Nonstationarity Using the Bayesian Model Averaging Method

Understanding hydrological nonstationarity under climate change is important for runoff prediction and it enables more robust decisions. Regarding the multiple structural hypotheses, this study aims to identify and interpret hydrological structural nonstationarity using the Bayesian Model Averaging (BMA) method by (i) constructing a nonstationary model through the Bayesian weighted averaging of two lumped conceptual rainfall–runoff (RR) models (the Xinanjiang and GR4J model) with time-varying weights; and (ii) detecting the temporal variation in the optimized Bayesian weights under climate change conditions. By combining the BMA method with period partition and time sliding windows, the efficacy of adopting time-varying model structures is investigated over three basins located in the U.S. and Australia. The results show that (i) the nonstationary ensemble-averaged model with time-varying weights surpasses both individual models and the ensemble-averaged model with time-invariant weights, improving NSE[Q] from 0.04 to 0.15; (ii) the optimized weights of Xinanjiang model increase and that of GR4J declines with larger precipitation, and vice versa; (iii) the change in the optimized weights is proportional to that of precipitation under monotonic climate change, as otherwise the mechanism changes significantly. Overall, it is recommended to adopt nonstationary structures in hydrological modeling.

A Spatiotemporal Assessment of the Precipitation Variability and Pattern and an Evaluation of the Predictive Reliability of Global Climate Models over Bihar

Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these changes in precipitation patterns on a regional scale is essential to enhance resilience against the adverse effects of climate change. The present study, focused on the state of Bihar in India, uses a long-term (1901–2020) gridded precipitation dataset to analyze the effect of climate change. Change point detection tests divide the time series into two epochs: 1901–1960 and 1961–2020, with 1960 as the change point year. Modified Mann–Kendall (MMK) and Sen’s slope estimator tests are used to identify trends in seasonal and annual time scales, while Centroidal Day (CD) analysis is performed to determine changes in temporal patterns of rainfall. The results show significant variability in seasonal rainfall, with the nature of pre-monsoon and post-monsoon observed to have flipped in second epoch. The daily rainfall intensity during the monsoon season has increased considerably, particularly in north Bihar, while the extreme rainfall has increased by 60.6 mm/day in the second epoch. The surface runoff increased by approximately 13.43% from 2001 to 2020. Further, 13 Global Climate Models (GCMs) evaluate future scenarios based on Shared Socioeconomic Pathways (SSP) 370 and SSP585. The suitability analysis of these GCMs, based on probability density function (PDF), monthly mean absolute error (MAE), root mean square error (RMSE) and percentage bias (P-Bias), suggests that EC-Earth3-Veg-LR, MIROC6, and MPI-ESM1-2-LR are the three best GCMs representative of rainfall in Bihar. A Bayesian model-averaged (BMA) multi-model ensemble reflects the variability expected in the future with the least uncertainty. The present study’s findings clarify the current state of variability, patterns and trends in precipitation, while suggesting the most appropriate GCMs for better decision-making and preparedness.

Association between body fat distribution and age at menarche: a two sample Mendelian randomization study.

Numerous studies have examined the association between obesity and age at menarche (AAM), with most focusing on traditional obesity indicators such as body mass index. However, there are limited studies that explored the connection between body fat distribution and AAM, as well as a scarcity of Mendelian randomization (MR) studies. In this study, we conducted a two-sample MR study to evaluate the causal effects of eight body fat distribution indicators on AAM. Inverse variance weighted (IVW) method was used for primary analysis, while supplementary approaches such as MR-Egger and weighted median were also utilized. Considering that the eight exposures were highly correlated, we performed an MR Bayesian model averaging (MR-BMA) analysis to prioritize the effect of major exposure on AAM. A series of sensitivity analyses were also performed. From a range of 82-105 single nucleotide polymorphisms (SNPs) were utilized as genetic instrumental variables for each of the exposure factors. After Bonferroni correction, we found that whole body fat mass (β: -0.17; 95% CI: -0.24, -0.11), left leg fat percentage (β: -0.14; 95% CI: -0.21, -0.07), left leg fat mass (β: -0.20; 95% CI: -0.27, -0.12), left arm fat percentage (β: -0.18; 95% CI: -0.26, -0.11) and left arm fat mass (β: -0.18; 95%CI: -0.26, -0.10) were associated with decreased AAM using random effects IVW method. And the beta coefficients for all MR evaluation methods exhibited consistent trends. MR-BMA method validated that left arm fat percentage plays a dominant role in AAM. Our MR study suggested that body fat has broad impacts on AAM. Obtaining more information on body measurements would greatly enhance our comprehension of pubertal development.

Trend and projection of non-communicable diseases risk factors in Iran from 2001 to 2030

This study aims to investigate the trends and project the major risk factors of Non-communicable Diseases (NCDs) in Iran. We obtained the trend of prevalence of main risk factors related to NCDs in 30 to 70-year-old-individuals. The data were extracted from WHO STEP wise approach to NCDs risk factor surveillance (STEPS) survey. Also,the previous studies conducted at national and subnational levels from 2001 to 2016 were employed. The prevalence of risk factors was projected by 2030 using Bayesian Model Averaging (BMA) and Spatio-temporal model stratified by sex and province. The percent change for the age-standardized prevalence of smoking in men between 2001 and 2016 was calculated to be − 27.0. Also, the corresponding values for the risk factors of diabetes, hypertension, obesity and overweight, physical inactivity (PI), and mean of salt intake were − 26.1, 29.0, 70.0, 96.8, 116.6, and 7.5, respectively. It is predicted that smoking and these risk factors will undergo a change to show values of − 1.26, 38.7, 43.7, 2.36, and 15.3 by 2030, respectively. The corresponding values in women for the time interval of 2001–2016 were − 27.3, 26.3, 82.8, 1.88, 75.2, and 4.2, respectively. Plus, projections indicate that the 2030 variation values are expected to be − 25.0, 16.7, 37.5, 28.7, 26.7, and 10.9 respectively. This study showed that the prevalence of four risk factors of PI, overweight and obesity, hypertension, and diabetes is increasing in Iran. Therefor, it is necessary to carry out effective interventions to adopt a healthy lifestyle and reduce the risk factors.

Bayesian typhoon precipitation prediction with a mixture of ensemble forecast-based and historical event-based prediction functions

Statistical post-processing has been widely adopted in probabilistic forecasting, especially when a predictive distribution of weather variables is of interest. Among the available post-processing methods, a common tool is the ensemble Bayesian model averaging. Challenges arise when it is applied to heavy rain prediction, including the precipitation produced by tropical cyclones. The issues are related to the rare extreme event and limited available data for model training. To address this, we propose in this research a Bayesian mixture model, Bmix, based on information from forecast members and historical tropical cyclones, rather than solely on immediately observed precipitation data. This approach provides a grid-specific Bayesian predictive distribution and an easy-to-derive probabilistic forecast with posterior samples generated from the Markov chain Monte Carlo algorithm. This post-processing procedure can be executed on each grid simultaneously, reducing the computational time significantly. In addition, a categorized Bmix (C.Bmix) is implemented to accommodate different scenarios such as heavy or torrential rain in applications. The proposed approaches are demonstrated on the 24-hour accumulated precipitation forecast of a category 4-equivalent super typhoon Dujuan landing in Taiwan in 2015; while two others, Matmo and Soudelor, are adopted to provide historical information. Over the Taiwan main island, a total of 1282 grids are considered, each with a resolution of 5 km and 20 ensemble member forecasts from the Weather Research and Forecasting Ensemble Prediction System (WEPS) at a 6-hour interval during the typhoon period. The analyses show that the proposed mixture approaches present larger percentages of positive CRPSS values than traditional BMA. For instance, at the time when Dujuan made landfall, the percentages are 92.2 % for Bmix, 95.2 % for C.Bmix, and 15.4 % for BMA when the true precipitation amount exceeds 200 mm/24 h. The proposed methods outperform in the probability prediction and the overall pattern of precipitation.

CMIP6 Simulation-Based Daily Surface Air Temperature and Precipitation Projections over the Qinghai-Tibetan Plateau in the 21st Century

The Qinghai-Tibetan Plateau (QTP), the source of many major Asian rivers, is sensitive to climate change, affecting billions of people’s livelihoods across Asia. Here, we developed high-resolution projections of precipitation and daily maximum/minimum temperatures at 0.1° spatial resolution over the QTP. The projections are based on the output from seven global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for historical (1979–2013) and projected (2015–2100) climates across four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). An updated nonstationary cumulative distribution function matching method (called CNCDFm) was used to remove model systemic bias. We verify the necessity of taking into account altitude in downscaling processes and the validity of nonstationary bias correction. Compared to the historical period, the climate in the QTP in the 21st century is warmer (1.2–5.1 °C, for maximum surface temperature) and wetter (3.9–26.8%) according to the corrected GCM projection. For precipitation, the Indus River (IDR), Tarim River (TMR), Inner of Qiangtang Basin (IQTB), Yarlung Zangbo (YLZBR), and Qaidam Basin (QDB) showed growth well above the global average across high radiative forcing scenarios, which could have a profound impact on the regional hydrological cycle. However, there is great uncertainty in precipitation prediction, which is demonstrated by a very low signal-to-noise ratio (SNR) and a large difference between Bayesian model averaging (BMA) and multi-model averages (MMAs). This bias-corrected dataset is available for climate change impact research in the QTP at the subregion scale.