Both high and low body weight are associated with adverse health risk for both mother and children. Studies evaluating trends in the coverage of undernutrition and overnutrition among ever-married Bangladeshi women are limited. The objective of the present study is to assess the trends and develop future projections of body weight status among Bangladeshi women and to estimate the smoothed mean BMI by women's age for the national level and across urban and rural areas. Data from Bangladesh Demographic and Health Surveys conducted between 2004 and 2014 were used. The annual rate of change in the prevalence of underweight, overweight, and obesity, and smoothed age-specific mean BMI was estimated. During 2004–14, the prevalence of underweight reduced with an annual rate of 5⋅9 % at the national level, while the prevalence of overweight and obesity increased with an annual rate of 8⋅6 and 9⋅6 %, respectively. With the recent trends, the prevalence of underweight is expected to reduce from 11⋅9 % in 2020 to 6⋅5 % by 2025. In 2020, the prevalence of overweight and obesity were 30⋅0 and 6⋅9 %, respectively, which are projected to increase to 38⋅5 and 9⋅0 %, respectively, by 2025, if present trends continue. By 2030, the prevalence of overweight was predicted to be much higher in urban areas (44⋅7 %) compared with rural areas (36⋅5 %). Multifaceted nutrition programme should be introduced for rapid reduction of undernutrition and to halt the rise of the prevalence of overweight and obesity.

This paper presents a new order selection technique of matrix memory polynomial technique that models the nonlinearities of single-branch and multi-branch transmitters. The new criteria take into account the complexity of the model in addition to its mean-square error in the selection criteria. The quasi-convexity of the proposed criteria was proven in this work. By using this proposed Akaike information criterion (AIC) and Bayesian information criterion (BIC) criteria, the model order selection was cast as a cost minimization problem. To minimize the criteria, modified gradient descent and simulated annealing algorithms were utilized which resulted in a considerable reduction in the number of search iterations. The performances of the criteria were shown by comparing the normalized mean square error (NMSE) of a higher-order model and the optimum model. It has been shown that the NMSE difference is <0.5 dB, but the complexity is much smaller.