Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method

Zircon M127 - A Homogeneous Reference Material for SIMS U-Pb Geochronology Combined with Hafnium, Oxygen and, Potentially, Lithium Isotope Analysis

In this article, we document a detailed analytical characterisation of zircon M127, a homogeneous 12.7 carat gemstone from Ratnapura, Sri Lanka. Zircon M127 has TIMS-determined mean U-Pb radiogenic isotopic ratios of 0.084743 ± 0.000027 for 206Pb/238U and 0.67676 ± 0.00023 for 207Pb/235U (weighted means, 2s uncertainties). Its 206Pb/238U age of 524.36 ± 0.16 Ma (95% confidence uncertainty) is concordant within the uncertainties of decay constants. The d18O value (determined by laser fluorination) is 8.26 ± 0.06‰ VSMOW (2s), and the mean 176Hf/177Hf ratio (determined by solution ICP-MS) is 0.282396 ± 0.000004 (2s). The SIMS-determined d7Li value is -0.6 ± 0.9‰ (2s), with a mean mass fraction of 1.0 ± 0.1 µg g-1 Li (2s). Zircon M127 contains ~ 923 µg g-1 U. The moderate degree of radiation damage corresponds well with the time-integrated self-irradiation dose of 1.82 × 1018 alpha events per gram. This observation, and the (U-Th)/He age of 426 ± 7 Ma (2s), which is typical of unheated Sri Lankan zircon, enable us to exclude any thermal treatment. Zircon M127 is proposed as a reference material for the determination of zircon U-Pb ages by means of SIMS in combination with hafnium and stable isotope (oxygen and potentially also lithium) determination

Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method

Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method

Current Research on the Effects of Non-Digestible Carbohydrates on Metabolic Disease

Metabolic diseases (MDs), including cardiovascular diseases (CVDs) and diabetes, occur when the body’s normal metabolic processes are disrupted. Behavioral risk factors such as obesity, physical inactivity, and dietary habits are strongly associated with a higher risk of MD. However, scientific evidence strongly suggests that balanced, healthy diets containing non-digestible carbohydrates (NDCs), such as dietary fiber and resistant starch, can reduce the risk of developing MD. In particular, major properties of NDCs, such as water retention, fecal bulking, viscosity, and fermentation in the gut, have been found to be important for reducing the risk of MD by decreasing blood glucose and lipid levels, increasing satiety and insulin sensitivity, and modifying the gut microbiome. Short chain fatty acids produced during the fermentation of NDCs in the gut are mainly responsible for improvement in MD. However, the effects of NDCs are dependent on the type, source, dose, and duration of NDC intake, and some of the mechanisms underlying the efficacy of NDCs on MD remain unclear. In this review, we briefly summarize current studies on the effects of NDCs on MD and discuss potential mechanisms that might contribute to further understanding these effects

Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method

Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method

Current Research on the Effects of Non-Digestible Carbohydrates on Metabolic Disease

Metabolic diseases (MDs), including cardiovascular diseases (CVDs) and diabetes, occur when the body’s normal metabolic processes are disrupted. Behavioral risk factors such as obesity, physical inactivity, and dietary habits are strongly associated with a higher risk of MD. However, scientific evidence strongly suggests that balanced, healthy diets containing non-digestible carbohydrates (NDCs), such as dietary fiber and resistant starch, can reduce the risk of developing MD. In particular, major properties of NDCs, such as water retention, fecal bulking, viscosity, and fermentation in the gut, have been found to be important for reducing the risk of MD by decreasing blood glucose and lipid levels, increasing satiety and insulin sensitivity, and modifying the gut microbiome. Short chain fatty acids produced during the fermentation of NDCs in the gut are mainly responsible for improvement in MD. However, the effects of NDCs are dependent on the type, source, dose, and duration of NDC intake, and some of the mechanisms underlying the efficacy of NDCs on MD remain unclear. In this review, we briefly summarize current studies on the effects of NDCs on MD and discuss potential mechanisms that might contribute to further understanding these effects

Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method

Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods

A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method