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The Reynolds-averaged Navier–Stokes (RANS) equations represent the computational workhorse for engineering design, despite their numerous flaws. Improving and quantifying the uncertainties associated with RANS models is particularly critical in view of the analysis and optimization of complex turbomachinery flows.

First, an efficient strategy is introduced for calibrating turbulence model coefficients from high-fidelity data. The results are highly sensitive to the flow configuration (called a calibration scenario) used to inform the coefficients. Second, the bias introduced by the choice of a specific turbulence model is reduced by constructing a mixture model by means of Bayesian model-scenario averaging (BMSA). The BMSA model makes predictions of flows not included in the calibration scenarios as a probability-weighted average of a set of competing turbulence models, each supplemented with multiple sets of closure coefficients inferred from alternative calibration scenarios.

Different choices for the scenario probabilities are assessed for the prediction of the NACA65 V103 cascade at off-design conditions. In all cases, BMSA improves the solution accuracy with respect to the baseline turbulence models, and the estimated uncertainty intervals encompass reasonably well the reference data. The BMSA results were found to be little sensitive to the user-defined scenario-weighting criterion, both in terms of average prediction and of estimated confidence intervals.

A delicate step in the BMSA is the selection of suitable scenario-weighting criteria, i.e. suitable prior probability mass functions (PMFs) for the calibration scenarios. The role of such PMFs is to assign higher probability to calibration scenarios more likely to provide an accurate estimate of model coefficients for the new flow. In this paper, three mixture models are constructed, based on alternative choices of the scenario probabilities. The authors then compare the capabilities of three different criteria.

The purpose of this study is to discuss policy review in the interest of sustainable innovations in Malaysia’s public procurement. This study also offers the overall relationship between existing policies related to sustainable innovations in public procurement and the coherences towards the four dimensions of sustainable innovations.

This study outlines the current policies in Malaysia which are related to sustainable innovation initiatives and explores the cohesiveness that appears disconnected and understood separately. Policy content analysis is conducted on the current policies related to sustainable innovations in the context of Malaysia’s public procurement.

This study observed that the current policies related to sustainable innovations in public procurement are actually interconnected with each other through a hierarchical framework. This study also demonstrates that the 12th Malaysia Plan has comprehensively encompassed every aspect of the environment, social, economic and innovation to contribute to one primary goal – green economic growth.

The proposed policy framework is expected to be beneficial for the administrator executive among the civil servant to connect the independent policies and, at the same time, contribute to the overall goal of green economic growth. Through a broad policy structure too, this study helps the industry player to recognize their potential in any area related to sustainable innovation.

The policy framework illustrated is new to the literature, especially in Malaysia’s context. The compilation of current policy grounded by the 12th Malaysia Plan has not been presented in any publications.

The authors performed a systematic review and meta-analysis of all published randomized controlled trials with the aim to determine and quantify the anti-hyperglycemic effects of glutamine (Gln) in acute and chronic clinical settings.

The authors conducted a comprehensive search of all randomized clinical trials performed up to December 2018, to identify those investigating the impact of Gln supplementation on fasting blood sugar (FBS), insulin levels and homeostatic model assessment-insulin resistance (HOMA-IR) via ISI Web of Science, Cochrane library PubMed and SCOPUS databases. A meta-analysis of eligible studies was conducted using random effects model to estimate the pooled effect size. Fractional polynomial modeling was used to explore the dose–response relationships between Gln supplementation and diabetic indices.

The results of the present meta-analysis suggest that of Gln supplementation had a significant effect on FBS (weighted mean difference (WMD): –2.868 mg/dl, 95 per cent CI: –5.467, –0.269, p = 0.031). However, the authors failed to observe that Gln supplementation affected insulin levels (WMD: 1.06 units, 95 per cent CI: –1.13, 3.26, p = 0.34) and HOMA-IR (WMD: 0.001 units, 95 per cent CI: –2.031, 2.029, p = 0.999). Subgroup analyses showed that the highest decrease in FBS levels was observed when the duration of intervention was less than two weeks (WMD: –4.064 mg/dl, 95 per cent CI: –7.428, –0.700, p = 0.01) and when Gln was applied via infusion (WMD: –5.334 mg/dl, 95 per cent CI: –10.48, 0.17, p = 0.04).

The results from this meta-analysis show that Gln supplementation did not have a significant effect on insulin levels and HOMA-IR. However, it did significantly reduce the levels of FBS, obtaining a higher effect when the duration of the intervention period was less than two weeks.