Background Numerous nutrition-related policy options and strategies have been proposed to tackle hypertension and other risk factors of non-communicable diseases (NCDs). In this study, we developed a comparative analysis using a multi-criteria decision-making (MCDM) model for prioritizing population-based nutrition-related interventions to prevent and control hypertension in Iran. Methods We employed a combination of Delphi technique and Analytic Hierarchy Process (AHP) method as the methodological tool to prioritize decision alternatives using multiple criteria. The prominent assessment criteria and intervention strategies were derived using a literature review, focus group discussion (n = 11), and a 2-round modified Delphi technique with specialists and experts involved in different stages of health policy-making (round 1: n = 50, round 2: n = 46). Then, the AHP was used to determine the weightage of the selected interventions and develop the decision-making model. The sensitivity analysis was performed to test the stability of the priority ranking. Results Nine alternative interventions were included in the final ranking based on eight assessment criteria. According to the results, the most priority interventions to prevent and control hypertension included reformulation of food products to contain less salt and changing the target levels of salt in foods and meals, providing low-sodium salt substitutes, and reducing salt intake through the implementation of front-of-package labeling (FOPL). The results of the sensitivity analysis and a comparison analysis suggested that the assessment model performed in this study had an appropriate level of robustness in selecting the best option among the proposed alternatives. Conclusion MCDM techniques offer a potentially valuable approach to rationally structuring the problem, along with the opportunity to make explicit the judgments used as part of the decision-making model. The findings of this study provide a preliminary evidence base to guide future decisions and reforms aiming to improve appropriate population-based interventions for tackling hypertension and other risk factors of NCDs.

This paper evaluated mixed mode I/II fracture toughness of fiber-reinforced concrete using cracked semi-circular bend (SCB) specimens subjected to three-point bending test. Additionally, a comparison was made between the experimental results and the estimations made by different theoretical criteria. Natural and synthetic fibers at various concentrations were used in this study. After producing cracks in SCB specimens at different inclination angles to induce different mixed mode loading conditions (from pure mode I to II), the fracture toughness of SCB specimens was determined. Furthermore, the compressive, splitting tensile, and flexural strength of natural and synthetic fiber-reinforced concrete were measured after 7 and 28 days of curing. While there is an increase in the aforementioned strengths with fiber content increase, 0.3% was found to be the optimum percentage regarding fracture toughness for both fibers. Also, the comparison between the experimental and theoretical results showed that generalized maximum tangential stress criterion estimated the experimental data satisfactorily.

Tabriz as one of the major industrial cities of Iran is not immune to air pollution and spends many days air-polluted each year. Since one of the goals of sustainable development is to achieve clean and good air for all segments of society and to attract clean air from the citizens of a city. In this study, it was attempted to present an efficient model for predicting CO pollutant concentrations using artificial neural network (ANN) and adaptive neural-fuzzy inference system (ANFIS). Air quality monitoring and developing efficient air pollution models can be suitable in providing requirements for sustainable development goals. For modelling, the meteorological and pollutant data were first obtained from the Meteorological and Environmental Agency of Tabriz city. The model inputs were temperature, wind speed, humidity and contaminant concentration daily hour index and weekly day index. The results of the study at validation step yielded 0.82 and 0.63 in terms of determination coefficient for ANN models and ANFIS models. It was also observed that the Ensemble method worked even better than the single 2 methods. Keywords Air pollution • Artificial neural network • Adaptive fuzzy neural inference system • CO • TabrizThis article is a part of the Topical collection in Environmental Earth Sciences on "Water Problems in E. Mediterranean Countries" guest edited by H. Gökçeku, D. Orhon, V. Nourania, and S. Sozen.

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