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Extensive studies have discussed the economic and welfare effects of social pensions worldwide, while scant attention has been paid to their nutritional effects, especially in emerging economies. Using multitopic longitudinal survey data of households and individuals in Kyrgyzstan covering four waves of 2012, 2013, 2016, and 2019, this study aims to examine the impact of an old‐age pension on the nutritional outcomes of the elderly and shed light on their underlying mechanisms. Our empirical identification rests on a propensity score matching with difference‐in‐differences estimation design to address the endogeneity problem of receiving an old‐age pension. Overall, the results indicate that receiving an old‐age pension has a significant and negative impact on body mass index and the probability of being overweight for males, while no significant impact of the old‐age pension has been observed for females. Receiving an old‐age pension has no significant effect on expenditure of food consumption, calorie intake, and macronutrient intake, while it significantly reduces the ratio of protein intake to total calorie intake. We also observe significant heterogeneity in the nutritional effect of an old‐age pension across various education levels and income levels; the negative nutritional effect is more salient for elderly individuals who are relatively poor or who have lower education. Our findings provide profound implications for policies targeting improving the nutritional status of rural residents in emerging economies through the implementation of the old‐age pension [EconLit Citations: H55, I15, I38]. [ABSTRACT FROM AUTHOR]

Active contour based methods are effective models for image segmentation. However, they always suffer from the limited performance due to the presence of noise and intensity inhomogeneity. To solve this problem, a kernel metric induced local image fitting (KLIF) variational model is proposed in this paper. Firstly, a kernel metric induced local fitting image (KLFI) is introduced by minimising a kernel metric based energy. The combination of the kernel metric and the local fitting image enables the model to be more robust to the noise and intensity inhomogeneity. And then, using the KLFI, a variational level set model that is a squared l2 distance between the KLFI and the original image is constructed. Two regularisation terms are employed in the model to keep the level set function to be stable during the evolution. At last, an alternating iterative algorithm combining with fixed‐point iteration and gradient descent of three‐step time‐splitting is introduced to solve the proposed model. The experimental results show the effectiveness of the proposed model for image segmentation in the presence of noise and intensity inhomogeneity, and demonstrate the competitive performance over several state‐of‐the‐art variational models in term of accuracy and robustness. [ABSTRACT FROM AUTHOR]